diff --git a/LICENSE.txt b/LICENSE.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f288702d2fa16d3cdf0035b15a9fcbc552cd88e7
--- /dev/null
+++ b/LICENSE.txt
@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
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+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..dcf961a241f2beae8b77af91a32733d4f32a649f
--- /dev/null
+++ b/README.md
@@ -0,0 +1,26 @@
+# AngioPy Segmentation
+
+## AngioPy paper
+Please see [here](https://doi.org/10.1016/j.ijcard.2024.132598) for our paper in the International Journal of Cardiology
+
+Please cite it!
+> Mahendiran, T., Thanou, D., Senouf, O., Jamaa, Y., Fournier, S., De Bruyne, B., ... & Andò, E. (2025). AngioPy Segmentation: An open-source, user-guided deep learning tool for coronary artery segmentation. International journal of cardiology, 418, 132598.
+
+## AngioPy in the news
+AngioPy segmentation is being used in [this RTS reportage on AI in Cardiology](https://www.rts.ch/play/tv/19h30/video/lia-fait-irruption-en-cardiologie-et-redefinit-le-role-des-medecins?urn=urn:rts:video:15479233) (in French)
+
+## Online Example
+Please visit https://imaging.epfl.ch/angiopy-segmentation/ for a live demo of this code on some example DICOM images
+
+![](illustration.mp4)
+
+## Description
+This software allows single arteries to be segmented given a few clicks on a single time frame with a PyTorch 2 Deep Learning model.
+
+## Installing and running
+ - Install dependencies: ` pip install -r requirements.txt`
+ - Launch Streamlit Web Interface: `streamlit run angioPySegmentation.py --server.fileWatcherType none`
+
+ ...a website should pop up in your browser!
+
+ You need to create a /Dicom folder and put some angiography DICOMs in there
diff --git a/angioPyFunctions.py b/angioPyFunctions.py
new file mode 100644
index 0000000000000000000000000000000000000000..d994b2cc08d2922bfdd21536be01e1eb2d9c3d6b
--- /dev/null
+++ b/angioPyFunctions.py
@@ -0,0 +1,265 @@
+import numpy
+import scipy.interpolate
+import skimage.filters
+import skimage.morphology
+import scipy.ndimage
+import scipy.optimize
+import predict
+from PIL import Image
+from fil_finder import FilFinder2D
+import astropy.units as u
+from tqdm import tqdm
+import pooch
+import utils.dataset
+import cv2
+
+
+colourTableHex = {
+                'LAD':       "#f03b20",
+                'D':         "#fd8d3c",
+                'CX':        "#31a354",
+                'OM':        "#74c476",
+                'RCA':       "#08519c",
+                'AM':        "#3182bd",
+                'LM':        "#984ea3",
+                }
+
+colourTableList = {}
+
+for item in colourTableHex.keys():
+    ### WARNING HACK: The colours go in backwards here for some reason perhaps related to RGBA?
+    colourTableList[item] = [int(colourTableHex[item][5:7], 16),
+                             int(colourTableHex[item][3:5], 16),
+                             int(colourTableHex[item][1:3], 16)]
+
+
+def skeletonise(maskArray):
+    # if len(maskArray.shape) == 3:
+    maskArray = cv2.cvtColor(maskArray, cv2.COLOR_BGR2GRAY)
+
+    skeleton = skimage.morphology.skeletonize(maskArray.astype('bool'))
+
+    # Process the skeleton and find the longest path
+    fil = FilFinder2D(skeleton.astype('uint8'),
+                    distance=250 * u.pc, mask=skeleton, beamwidth=10.0*u.pix)
+    fil.preprocess_image(flatten_percent=85)
+    fil.create_mask(border_masking=True, verbose=False,
+                    use_existing_mask=True)
+    fil.medskel(verbose=False)
+    fil.analyze_skeletons(branch_thresh=400 * u.pix,
+                        skel_thresh=10 * u.pix, prune_criteria='length')
+
+    # add image arrays dictionary
+    # tifffile.imwrite(os.path.join(arteryFolder, "skel.tif"), fil.skeleton.astype('<u1')*255)
+
+    skel = fil.skeleton.astype('<u1')*255
+
+    return skel
+
+
+def skelEndpoints(skel):
+    #skel[skel!=0] = 1
+    skel = numpy.uint8(skel>0)
+
+    # Apply the convolution.
+    kernel = numpy.uint8([[1,  1, 1],
+    [1, 10, 1],
+    [1,  1, 1]])
+    src_depth = -1
+    filtered = cv2.filter2D(skel,src_depth,kernel)
+
+    # Look through to find the value of 11.
+    # This returns a mask of the endpoints, but if you
+    # just want the coordinates, you could simply
+    # return np.where(filtered==11)
+    out = numpy.zeros_like(skel)
+    out[numpy.where(filtered==11)] = 1
+    endCoords = numpy.where(filtered==11)
+    endCoords = list(zip(*endCoords))
+    startPoint = endCoords[0]
+    endPoint = endCoords[1]
+
+    # print(f"Skel starts at {startPoint} and finishes at {endPoint}")
+
+    return startPoint, endPoint
+
+
+def skelPointsInOrder(skel, startPoint=None):
+    """
+    put in a skel image, get the y, x points out in order
+    """
+
+    # Lazy!!
+    if startPoint is None:
+        startPoint, _ = skelEndpoints(skel)
+
+    # get the coordinates of all points in the skeleton
+    skelXY = numpy.array(numpy.where(skel))
+    skelPoints = list(zip(skelXY[0], skelXY[1]))
+    skelLength = len(skelPoints)
+
+    # Loop through the skeleton starting with startPoint, deleting the starting point from the skelPoints list, and finding the closest pixel. This is appended to orderedPoints. startPoint now becomes the last point to be appended.
+    startPointCopy = startPoint # copied as we are going to loop and overwrite, but want to also keep the original startPoint
+    orderedPoints = []
+
+    while len(skelPoints) > 1:
+
+        skelPoints.remove(startPointCopy)
+
+        # Calculate the point that is closest to the start point
+        diffs = numpy.abs(numpy.array(skelPoints)-numpy.array(startPointCopy))
+        dists = numpy.sum(diffs,axis=1) #l1-distance
+        closest_point_index = numpy.argmin(dists)
+        closestPoint = skelPoints[closest_point_index]
+        orderedPoints.append(closestPoint)
+
+        startPointCopy = closestPoint
+
+    orderedPoints = numpy.array(orderedPoints)
+
+    # YX points
+    return orderedPoints
+
+
+def skelSplinerWithThickness(skel, EDT, smoothing=50, order=3, decimation=2):
+    # NOTE: the coordinate seem to come out with y first, then x
+    startPoint, endPoint = skelEndpoints(skel)
+
+    # Impose an order to points
+    orderedPoints = skelPointsInOrder(skel, startPoint)
+
+    # unzip ordered points to extract x and y arrays
+    x = orderedPoints[:, 1].ravel()
+    y = orderedPoints[:, 0].ravel()
+
+    x = x[::decimation]
+    y = y[::decimation]
+
+    #NOTE: Should the EDT be median filtered? I wonder in fact if doing so will reduce the accuracy of the model.
+    # EDT = skimage.filters.median(EDT)
+
+    t = EDT[y, x]
+
+    x = x[0:-1]
+    y = y[0:-1]
+    t = t[0:-1]
+
+    print(x.shape, y.shape, t.shape)
+
+    tcko, uo = scipy.interpolate.splprep(
+        [y, x, t], s=smoothing, k=order, per=False)
+
+    return tcko
+
+
+def arterySegmentation(inputImage, groundTruthPoints, segmentationModelWeights=None):
+    """
+    Segment a single greyscale artery with a UNet model.
+
+    Parameters
+    ----------
+        inputImage: 2D numpy array
+            Ideally this input is normalised 0-255 and 512x512
+            If a different size it is rescaled along with groundTruthPoints
+
+        groundTruthPoints: Nx2 numpy array
+            Y and X positions of annotated points along the artery,
+            Ordering is not important except that start and end points should be top and bottom of the array
+
+        segmentationModelWeights: segmentation model weights (pth), optional
+            Segmentation model weights to use.
+            If not set the default ones from this paper: https://doi.org/10.1016/j.ijcard.2024.132598
+
+    Returns
+    -------
+        mask : 512x512 numpy array (int64)
+            Mask selecting the selected artery, 0 = background and 1 = artery
+    """
+    if segmentationModelWeights is None:
+        segmentationModelWeights = pooch.retrieve(
+            url="doi:10.5281/zenodo.13848135/modelWeights-InternalData-inceptionresnetv2-fold2-e40-b10-a4.pth",
+            known_hash="md5:bf893ef57adaf39cfee33b25c7c1d87b",
+        )
+
+    if inputImage.shape[0] != 512 and inputImage.shape[1] != 512:
+        ratioYX = numpy.array([512./inputImage.shape[0], 512./inputImage.shape[1]])
+        print(f"arterySegmentation(): Rescaling image to 512x512 by {ratioYX=}, and also applying this to input points")
+        inputImage = scipy.ndimage.zoom(inputImage, ratioYX)
+        points = groundTruthPoints.copy() * ratioYX
+        print(inputImage.shape)
+    else:
+        points = groundTruthPoints
+
+    imageSize = inputImage.shape
+
+    n_classes = 2 # binary output
+
+    net = predict.smp.Unet(
+        encoder_name='inceptionresnetv2',
+        encoder_weights="imagenet",
+        in_channels=3,
+        classes=n_classes
+    )
+
+    net = predict.nn.DataParallel(net)
+
+    device = predict.torch.device('cuda' if predict.torch.cuda.is_available() else 'cpu')
+    net.to(device=device)
+
+    net.load_state_dict(
+        predict.torch.load(
+            segmentationModelWeights,
+            map_location=device
+        )
+    )
+
+    orig_image = Image.fromarray(inputImage)
+
+    image = predict.Image.new('RGB', imageSize, (0, 0, 0))
+    image.paste(orig_image, (0, 0))
+
+    imageArray = numpy.array(image).astype('uint8')
+
+    # Clear last channels
+    imageArray[:, :, -1] = 0
+    imageArray[:, :, -2] = 0
+
+    ## Get endpoints of skeleton
+    startPoint = points[0]
+    endPoint = points[-1]
+
+    # End points on Channel 1
+    for y, x in [startPoint, endPoint]:
+        y = int(numpy.round(y))
+        x = int(numpy.round(x))
+        imageArray[y-2:y+2, x-2:x+2, 1] = 255
+
+    # All other points on Channel 2
+    for y, x in points[1:-1]:
+        y = int(numpy.round(y))
+        x = int(numpy.round(x))
+        imageArray[y-2:y+ 2, x-2:x+2, 2] = 255
+
+    image = Image.fromarray(imageArray.astype(numpy.uint8))
+
+    mask = predict.predict_img(
+        net=net,
+        dataset_class=utils.dataset.CoronaryDataset,
+        full_img=image,
+        scale_factor=1,
+        device=device
+    )
+
+    return mask
+
+
+
+def maskOutliner(labelledArtery, outlineThickness=3):
+
+    # Compute the boundary of the mask
+    contours, _ = cv2.findContours(labelledArtery, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
+    tmp = numpy.zeros_like(labelledArtery)
+    boundary = cv2.drawContours(tmp, contours, -1, (255,255,255), outlineThickness)
+    boundary = boundary > 0
+
+    return boundary
diff --git a/angioPySegmentation.py b/angioPySegmentation.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc899c91d636ab8ed5ef3efb5739be394eeaac0c
--- /dev/null
+++ b/angioPySegmentation.py
@@ -0,0 +1,335 @@
+import os
+import os.path
+import matplotlib.pyplot as plt
+import numpy
+import pandas as pd
+import streamlit as st
+import SimpleITK as sitk
+import pydicom
+import glob
+import mpld3
+import streamlit.components.v1 as components
+import plotly.express as px
+import plotly.graph_objects as go
+import tifffile
+from streamlit_plotly_events import plotly_events
+from streamlit_drawable_canvas import st_canvas
+from PIL import Image
+# from streamlit_image_coordinates import streamlit_image_coordinates
+import predict
+import angioPyFunctions
+import scipy
+import cv2
+
+import ssl
+
+ssl._create_default_https_context = ssl._create_unverified_context
+
+st.set_page_config(page_title="AngioPy Segmentation", layout="wide")
+
+if 'stage' not in st.session_state:
+    st.session_state.stage = 0
+
+
+
+# Make output folder
+# os.makedirs(name=outputPath, exist_ok=True)
+
+# arteryDictionary = {
+#     'LAD':       {'colour': "#f03b20"},
+#     'CX':        {'colour': "#31a354"},
+#     'OM':    {'colour' : "#74c476"},
+#     'RCA':       {'colour': "#08519c"},
+#     'AM':   {'colour' : "#3182bd"},
+#     'LM':        {'colour' : "#984ea3"},
+# }
+
+# def file_selector(folder_path='.'):
+#     fileNames = [file for file in glob.glob(f"{folder_path}/*")]
+#     selectedDicom = st.sidebar.selectbox('Select a DICOM file:', fileNames)
+#     if selectedDicom is None:
+#         return None
+
+#     return selectedDicom
+
+@st.cache_data
+def selectSlice(slice_ix, pixelArray, fileName):
+
+    # Save the selected frame 
+    tifffile.imwrite(f"{outputPath}/{fileName}", pixelArray[slice_ix, :, :])
+
+    # Set the button as clicked
+    st.session_state.btnSelectSlice = True
+
+
+DicomFolder = "Dicoms/"
+# exampleDicoms = {
+#     'RCA2' : 'Dicoms/RCA1',
+#     'RCA1' : 'Dicoms/RCA4',
+#     # 'RCA2' : 'Dicoms/RCA2',
+#     # 'RCA3' : 'Dicoms/RCA3',
+#     # 'LCA1' : 'Dicoms/LCA1',
+#     # 'LCA2' : 'Dicoms/LCA2',
+# 
+# }
+exampleDicoms = {}
+files = sorted(glob.glob(DicomFolder+"/*"))
+for file in files:
+    exampleDicoms[os.path.basename(file)] = file
+
+
+# Main text
+st.markdown("<h1 style='text-align: center;'>AngioPy Segmentation</h1>", unsafe_allow_html=True)
+st.markdown("<h5 style='text-align: center;'> Welcome to <b>AngioPy Segmentation</b>, an AI-driven, coronary angiography segmentation tool.</h1>", unsafe_allow_html=True)
+st.markdown("")
+
+# Build the sidebar
+# Select DICOM file: here eventually we will use the file_uploader widget, but for the demo this is deactivate. Instead we will have a choice of 3 anonymised DICOMs to pick from
+# selectedDicom = st.sidebar.file_uploader("Upload DICOM file:",type=["dcm"], accept_multiple_files=False)
+
+# def changeSessionState():
+
+#     # value += 1
+
+#     print("CHANGED!")
+
+
+DropDownDicom = st.sidebar.selectbox("Select example DICOM file:",
+                        options = list(exampleDicoms.keys()),
+                        # on_change=changeSessionState(st.session_state.key),
+                        key="dicomDropDown"
+                    )
+
+selectedDicom = exampleDicoms[DropDownDicom]
+
+stepOne = st.sidebar.expander("STEP ONE", True)
+stepTwo = st.sidebar.expander("STEP TWO", True)
+
+# Create tabs 
+tab1, tab2 = st.tabs(["Segmentation", "Analysis"])
+
+# Increase tab font size
+css = '''
+<style>
+    .stTabs [data-baseweb="tab-list"] button [data-testid="stMarkdownContainer"] p {
+    font-size:16px;
+    }
+</style>
+'''
+
+st.markdown(css, unsafe_allow_html=True)
+
+# while True:
+# Once a file is uploaded, the following annotation sequence is initiated 
+if selectedDicom is not None:
+        try:
+            print(f"Trying to load {selectedDicom}")
+            dcm = pydicom.dcmread(selectedDicom, force=True)
+
+            # handAngle = dcm.PositionerPrimaryAngle
+            # headAngle = dcm.PositionerSecondaryAngle
+            # dcmLabel = f"{'LAO' if handAngle > 0 else 'RAO'} {numpy.abs(handAngle):04.1f}° {'CRA' if headAngle > 0 else 'CAU'} {numpy.abs(headAngle):04.1f}°"
+
+            pixelArray = dcm.pixel_array
+
+            # Just take first channel if it's RGB?
+            if len(pixelArray.shape) == 4:
+                pixelArray = pixelArray[:,:,:,0]
+
+            n_slices = pixelArray.shape[0]
+
+            slice_ix = 0
+        except:
+            selectedDicom = None
+            # continue
+
+        with tab1:
+
+            with stepOne:
+                st.write("Select frame for annotation. Aim for an end-diastolic frame with good visualisation of the artery of interest.")
+
+                slice_ix = st.slider('Frame', 0, n_slices-1, int(n_slices/2), key='sliceSlider')
+
+
+                predictedMask = numpy.zeros_like(pixelArray[slice_ix, :, :])
+
+
+            with stepTwo:
+
+                selectedArtery = st.selectbox("Select artery for annotation:",
+                        ['LAD', 'CX', 'RCA', 'LM', 'OM', 'AM', 'D'],
+                        key="arteryDropMenu"
+                    )
+
+                st.write("Beginning with the desired start point and finishing at the desired end point, click along the artery aiming for ~5-10 points.")
+
+
+                stroke_color = angioPyFunctions.colourTableList[selectedArtery]
+
+
+            col1, col2 = st.columns((15,15))
+
+            with col1:
+                col1a, col1b, col1c = st.columns((1,10,1))
+
+                with col1b:
+
+                    leftImageText = "<p style='text-align: center; color: white;'>Beginning with the desired <u><b>start point</b></u> and finishing at the desired <u><b>end point</b></u>, click along the artery aiming for ~5-10 points. Segmentation is automatic.</p>"
+
+                    st.markdown(f"<h5 style='text-align: center; color: white;'>Selected frame</h5>", unsafe_allow_html=True)
+
+                    st.markdown(leftImageText, unsafe_allow_html=True)
+
+                    selectedFrame = pixelArray[slice_ix, :, :]
+                    selectedFrame = cv2.resize(selectedFrame, (512,512))
+
+                    # Create a canvas component
+                    annotationCanvas = st_canvas(
+                        fill_color="red",  # Fixed fill color with some opacity
+                        stroke_width=1,
+                        stroke_color="red",
+                        background_color='black',
+                        background_image= Image.fromarray(selectedFrame),
+                        update_streamlit=True,
+                        height=512,
+                        width=512,
+                        drawing_mode="point",
+                        point_display_radius=2,
+                        key=st.session_state.dicomDropDown,
+                    )
+
+
+                    # Do something interesting with the image data and paths
+                    if annotationCanvas.json_data is not None:
+                        objects = pd.json_normalize(annotationCanvas.json_data["objects"]) # need to convert obj to str because PyArrow
+
+                        if len(objects) != 0:
+
+                            for col in objects.select_dtypes(include=['object']).columns:
+                                objects[col] = objects[col].astype("str")
+
+                            groundTruthPoints = numpy.vstack(
+                                (
+                                    numpy.array(objects['top']),
+                                    numpy.array(objects['left']+3.5) # compensate for some streamlit offset or something
+                                )
+                            ).T
+
+                            mask = angioPyFunctions.arterySegmentation(
+                                pixelArray[slice_ix],
+                                groundTruthPoints,
+                            )
+                            predictedMask = predict.CoronaryDataset.mask2image(mask)
+                            # predictedMask = predictedMask.crop((0, 0, imageSize[0], imageSize[1]))
+                            predictedMask = numpy.asarray(predictedMask)
+
+            with col2:
+                col2a, col2b, col2c = st.columns((1,10,1))
+
+                with col2b:
+                    st.markdown(f"<h5 style='text-align: center; color: white;'>Predicted mask</h1>", unsafe_allow_html=True)
+                    st.markdown(f"<p style='text-align: center; color: white;'>If the predicted mask has errors, restart and select more points to help the segmentation model. </p>", unsafe_allow_html=True)
+
+                    stroke_color = "rgba(255, 255, 255, 255)"
+
+                    maskCanvas = st_canvas(
+                        fill_color=angioPyFunctions.colourTableList[selectedArtery],  # Fixed fill color with some opacity
+                        stroke_width=0,
+                        stroke_color=stroke_color,
+                        background_color='black',
+                        background_image= Image.fromarray(predictedMask),
+                        update_streamlit=True,
+                        height=512,
+                        width=512,
+                        drawing_mode="freedraw",
+                        point_display_radius=3,
+                        key="maskCanvas",
+                    )
+
+
+                    # Check that the mask array is not blank
+                    if numpy.sum(predictedMask) > 0 and len(objects)>4:
+                        # add alpha channel to predict mask in order to merge
+                        b_channel, g_channel, r_channel = cv2.split(predictedMask)
+                        a_channel = numpy.full_like(predictedMask[:,:,0], fill_value=255)
+
+                        predictedMaskRGBA = cv2.merge((predictedMask, a_channel))
+
+
+                        with tab2:
+                            # combinedMask = cv2.cvtColor(predictedMaskRGBA, cv2.COLOR_RGBA2RGB)
+
+                            # print(combinedMask.shape)
+                            # tifffile.imwrite(f"{outputPath}/test.tif", combinedMask)
+
+
+                            # tab2Col1, tab2Col2, tab2Col3 = st.columns([1,15,1])
+                            tab2Col1, tab2Col2 = st.columns([20,10])
+
+                            with tab2Col1:
+                                st.markdown(f"<h5 style='text-align: center; color: white;'><br>Artery profile</h5>", unsafe_allow_html=True)
+
+                                # Extract thickness information from mask
+                                EDT = scipy.ndimage.distance_transform_edt(cv2.cvtColor(predictedMaskRGBA, cv2.COLOR_RGBA2GRAY))
+
+                                # Skeletonise, get a list of ordered centreline points, and spline them
+                                skel = angioPyFunctions.skeletonise(predictedMaskRGBA)
+                                tck = angioPyFunctions.skelSplinerWithThickness(skel=skel, EDT=EDT)
+
+                                # Interogate the spline function over 1000 points
+                                splinePointsY, splinePointsX, splineThicknesses = scipy.interpolate.splev(
+                                numpy.linspace(
+                                    0.0,
+                                    1.0,
+                                    1000), 
+                                    tck)
+
+                                clippingLength = 20
+
+                                vesselThicknesses = splineThicknesses[clippingLength:-clippingLength]*2
+
+                                fig = px.line(x=numpy.arange(1,len(vesselThicknesses)+1),y=vesselThicknesses, labels=dict(x="Centreline point", y="Thickness (pixels)"), width=800)
+                                # fig.update_layout(showlegend=False, xaxis={'showgrid': False, 'zeroline': True})
+                                fig.update_traces(line_color='rgb(31, 119, 180)', textfont_color="white", line={'width':4})
+                                fig.update_xaxes(showline=True, linewidth=2, linecolor='white', showgrid=False,gridcolor='white')
+                                fig.update_yaxes(showline=True, linewidth=2, linecolor='white', gridcolor='white')
+
+                                fig.update_layout(yaxis_range=[0,numpy.max(vesselThicknesses)*1.2])
+                                fig.update_layout(font_color="white",title_font_color="white")
+                                fig.update_layout({'plot_bgcolor': 'rgba(0, 0, 0, 0)','paper_bgcolor': 'rgba(0, 0, 0, 0)'})
+
+
+                                selected_points = plotly_events(fig)
+
+
+
+                            with tab2Col2:
+
+                                st.markdown(f"<h5 style='text-align: center; color: white;'><br>Contours</h5>", unsafe_allow_html=True)
+
+
+                                selectedFrameRGBA = cv2.cvtColor(selectedFrame, cv2.COLOR_GRAY2RGBA)
+
+                                contour = angioPyFunctions.maskOutliner(labelledArtery=predictedMaskRGBA[:,:,0], outlineThickness=1)
+
+                                selectedFrameRGBA[contour, :] =    [angioPyFunctions.colourTableList[selectedArtery][2],
+                                                                    angioPyFunctions.colourTableList[selectedArtery][1],
+                                                                    angioPyFunctions.colourTableList[selectedArtery][0],
+                                                                    255]
+
+                                fig2 = px.imshow(selectedFrameRGBA)
+
+
+                                fig2.update_xaxes(visible=False)
+                                fig2.update_yaxes(visible=False)
+                                fig2.update_layout(margin={"t": 0, "b": 0, "r": 0, "l": 0, "pad": 0},) #remove margins
+                                # fig2.coloraxis(visible=False)
+
+                                fig2.update_traces(dict(
+                                    showscale=False, 
+                                    coloraxis=None, 
+                                    colorscale='gray'), selector={'type':'heatmap'})
+
+                                fig2.add_trace(go.Scatter(x=splinePointsX[clippingLength:-clippingLength], y=splinePointsY[clippingLength:-clippingLength], line=dict(width=1)))
+
+                                st.plotly_chart(fig2, use_container_width=True)
diff --git a/predict.py b/predict.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a15f5f3776180fb6f18974cc90446f713db771e
--- /dev/null
+++ b/predict.py
@@ -0,0 +1,65 @@
+import argparse
+import logging
+import os
+
+import torch
+import torch.nn as nn
+from PIL import Image
+from torchvision import transforms
+
+from utils.dataset import CoronaryDataset
+import segmentation_models_pytorch.segmentation_models_pytorch as smp
+
+from torch.backends import cudnn
+
+'''
+This uses a pytorch coronary segmentation model (EfficientNetPLusPlus) that has been trained using a freely available dataset of labelled coronary angiograms from: http://personal.cimat.mx:8181/~ivan.cruz/DB_Angiograms.html
+The input is a raw angiogram image, and the output is a segmentation mask of all the arteries. This output will be used as the 'first guess' to speed up artery annotation. 
+'''
+
+def predict_img(net, dataset_class, full_img, device, scale_factor=1, n_classes=3):
+    # NOTE n_classes is the number of possible values that can be predicted for a given pixel. In a standard binary segmentation task, this will be 2 i.e. black or white
+
+    net.eval()
+
+    img = torch.from_numpy(dataset_class.preprocess(full_img, scale_factor))
+
+    img = img.unsqueeze(0)
+    img = img.to(device=device, dtype=torch.float32)
+
+    with torch.no_grad():
+        output = net(img)
+
+        if n_classes > 1:
+            probs = torch.softmax(output, dim=1)
+        else:
+            probs = torch.sigmoid(output)
+
+        probs = probs.squeeze(0)
+
+        tf = transforms.Compose(
+            [
+                transforms.ToPILImage(),
+                transforms.Resize(full_img.size[1]),
+                transforms.ToTensor()
+            ]
+        )
+
+        full_mask = tf(probs.cpu())   
+
+    if n_classes > 1:
+        return dataset_class.one_hot2mask(full_mask)
+    else:
+        return full_mask > 0.5
+
+
+def get_args():
+    parser = argparse.ArgumentParser(description='Predict masks from input images', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    # parser.add_argument('-d', '--dataset', type=str, help='Specifies the dataset to be used', dest='dataset', required=True)
+    parser.add_argument('--model', '-m', default='MODEL.pth', metavar='FILE', help="Specify the file in which the model is stored")
+    parser.add_argument('--input', '-i', metavar='INPUT', nargs='+', help='filenames of input images', required=True)
+    parser.add_argument('--output', '-o', metavar='INPUT', nargs='+', help='Filenames of output images')
+
+    return parser.parse_args()
+
+
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2a2bf678c9540a9aa33b49cece60f5a07ef69659
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,29 @@
+# Automatically generated by https://github.com/damnever/pigar.
+
+astropy==5.2.2
+efficientnet-pytorch==0.7.1
+fil-finder==1.7.2
+matplotlib==3.7.2
+mpld3==0.5.9
+numpy==1.24.4
+opencv-python==4.8.0.76
+pandas==2.0.3
+Pillow==9.5.0
+plotly==5.16.1
+pretrainedmodels==0.7.4
+pydicom==2.4.3
+PyYAML==6.0.1
+scikit-image==0.21.0
+scikit-learn==1.3.0
+scipy==1.10.1
+setuptools==47.1.0
+SimpleITK==2.2.1
+streamlit<=1.38.0
+streamlit-drawable-canvas==0.9.3
+streamlit-plotly-events==0.0.6
+tifffile==2023.7.10
+timm==0.9.6
+torch==2.0.1
+torchvision==0.15.2
+tqdm==4.61.1
+pooch
diff --git a/segmentation_models_pytorch/.github/FUNDING.yml b/segmentation_models_pytorch/.github/FUNDING.yml
new file mode 100644
index 0000000000000000000000000000000000000000..77a16ab4e281a68b84ab7aff548fb8fd1c138958
--- /dev/null
+++ b/segmentation_models_pytorch/.github/FUNDING.yml
@@ -0,0 +1,12 @@
+# These are supported funding model platforms
+
+github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
+patreon: # Replace with a single Patreon username
+open_collective: # Replace with a single Open Collective username
+ko_fi: qubvel
+tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
+community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
+liberapay: qubvel
+issuehunt: # Replace with a single IssueHunt username
+otechie: # Replace with a single Otechie username
+custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']
diff --git a/segmentation_models_pytorch/.github/stale.yml b/segmentation_models_pytorch/.github/stale.yml
new file mode 100644
index 0000000000000000000000000000000000000000..dc90e5a1c3aad4818a813606b52fdecd2fdf6782
--- /dev/null
+++ b/segmentation_models_pytorch/.github/stale.yml
@@ -0,0 +1,17 @@
+# Number of days of inactivity before an issue becomes stale
+daysUntilStale: 60
+# Number of days of inactivity before a stale issue is closed
+daysUntilClose: 7
+# Issues with these labels will never be considered stale
+exemptLabels:
+  - pinned
+  - security
+# Label to use when marking an issue as stale
+staleLabel: wontfix
+# Comment to post when marking an issue as stale. Set to `false` to disable
+markComment: >
+  This issue has been automatically marked as stale because it has not had
+  recent activity. It will be closed if no further activity occurs. Thank you
+  for your contributions.
+# Comment to post when closing a stale issue. Set to `false` to disable
+closeComment: false
diff --git a/segmentation_models_pytorch/.github/workflows/pypi.yml b/segmentation_models_pytorch/.github/workflows/pypi.yml
new file mode 100644
index 0000000000000000000000000000000000000000..496bb7b43ac938e2640fbbbfa5018b80bdf661f6
--- /dev/null
+++ b/segmentation_models_pytorch/.github/workflows/pypi.yml
@@ -0,0 +1,26 @@
+name: Upload Python Package
+
+on:
+  release:
+    types: [published]
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.6'
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install setuptools wheel twine mock
+    - name: Build and publish
+      env:
+        TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
+        TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
+      run: |
+        python setup.py sdist bdist_wheel
+        twine upload dist/*
diff --git a/segmentation_models_pytorch/.github/workflows/tests.yml b/segmentation_models_pytorch/.github/workflows/tests.yml
new file mode 100644
index 0000000000000000000000000000000000000000..be4c44621117a3e9ce3be2f72da54e66f2cfdc19
--- /dev/null
+++ b/segmentation_models_pytorch/.github/workflows/tests.yml
@@ -0,0 +1,34 @@
+
+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
+
+name: CI
+
+on:
+  push:
+    branches: [ master ]
+  pull_request:
+    branches: [ master ]
+
+jobs:
+  test:
+
+    runs-on: ubuntu-18.04
+
+    steps:
+    - uses: actions/checkout@v2
+    
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: 3.6
+        
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        python -m pip install codecov pytest mock
+        pip install torch==1.7.1+cpu torchvision==0.8.2+cpu torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
+        pip install .
+    - name: Test
+      run: |
+        python -m pytest -s tests
diff --git a/segmentation_models_pytorch/.gitignore b/segmentation_models_pytorch/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..ad2eb978bb442ffdef2306780a7c36e48defe4e2
--- /dev/null
+++ b/segmentation_models_pytorch/.gitignore
@@ -0,0 +1,105 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+.idea/
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
\ No newline at end of file
diff --git a/segmentation_models_pytorch/HALLOFFAME.md b/segmentation_models_pytorch/HALLOFFAME.md
new file mode 100644
index 0000000000000000000000000000000000000000..a5abac6ea8ca589792c8bbe623af3b621f588bbc
--- /dev/null
+++ b/segmentation_models_pytorch/HALLOFFAME.md
@@ -0,0 +1,90 @@
+# Hall of Fame
+
+`Segmentation Models` package is widely used in the image segmentation competitions.
+Here you can find competitions, names of the winners and links to their solutions.
+
+Please, follow these rules, when adding a solution to the "Hall of Fame":
+
+1. Solution should be high rated (e.g. for Kaggle gold or silver medal) 
+2. There should be a description of the solution (post at the forum / code / blog post / paper / pre-print)
+
+
+## Kaggle
+
+### [Severstal: Steel Defect Detection](https://www.kaggle.com/c/severstal-steel-defect-detection)
+
+- 1st place. 
+[Wuxi Jiangsu](https://www.kaggle.com/rguo97), 
+[Hongbo Zhu](https://www.kaggle.com/zhuhongbo), 
+[Yizhuo Yu](https://www.kaggle.com/paffpaffyu)  
+[[description](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/114254#latest-675874)]
+
+- 5th place. 
+[Guanshuo Xu](https://www.kaggle.com/wowfattie)  
+[[description](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/117208#latest-675385)]
+
+- 9th place. 
+[Jacek Poplawski](https://www.linkedin.com/in/jacekpoplawski/)  
+[[description](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/114297#latest-660842)]
+
+- 10th place.
+[Alexey Rozhkov](https://www.linkedin.com/in/alexisrozhkov)  
+[[description](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/114465#latest-659615)]
+
+- 12th place. 
+[Pavel Yakubovskiy](https://www.linkedin.com/in/pavel-yakubovskiy/), 
+[Ilya Dobrynin](https://www.linkedin.com/in/ilya-dobrynin-79a89b106/), 
+[Denis Kolpakov](https://www.linkedin.com/in/denis-kolpakov-ab3137197/)  
+[[description](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/114309#latest-661404)]
+
+- 31st place. 
+[Insaf Ashrapov](https://www.linkedin.com/in/iashrapov/), 
+[Igor Krashenyi](https://www.linkedin.com/in/igor-krashenyi-38b89b98), 
+[Pavel Pleskov](https://www.linkedin.com/in/ppleskov), 
+[Anton Zakharenkov](https://www.linkedin.com/in/anton-zakharenkov/), 
+[Nikolai Popov](https://www.linkedin.com/in/nikolai-popov-b2157370/)  
+[[description](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/114383#latest-658438)]
+[[code](https://github.com/Diyago/Severstal-Steel-Defect-Detection)]
+
+- 55th place. 
+[Karl Hornlund](https://www.linkedin.com/in/karl-hornlund/)  
+[[description](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/114410#latest-672682)]
+[[code](https://github.com/khornlund/severstal-steel-defect-detection)]
+
+- Efficiency round 1st place.
+[Stefan Stefanov](https://www.linkedin.com/in/stefan-stefanov-63a77b1)  
+[[description](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/117486#latest-674229)]
+
+
+### [Understanding Clouds from Satellite Images](https://www.kaggle.com/c/understanding_cloud_organization)
+
+- 2nd place.
+[Andrey Kiryasov](https://www.kaggle.com/ekydna)  
+[[description](https://www.kaggle.com/c/understanding_cloud_organization/discussion/118255#latest-678189)]
+
+- 4th place.
+[Ching-Loong Seow](https://www.linkedin.com/in/clseow/)  
+[[description](https://www.kaggle.com/c/understanding_cloud_organization/discussion/118016#latest-677333)]
+
+- 34th place.
+[Karl Hornlund](https://www.linkedin.com/in/karl-hornlund/)  
+[[description](https://www.kaggle.com/c/understanding_cloud_organization/discussion/118250#latest-678176)]
+[[code](https://github.com/khornlund/understanding-cloud-organization)]
+
+- 55th place.
+[Pavel Yakubovskiy](https://www.linkedin.com/in/pavel-yakubovskiy/)  
+[[description](https://www.kaggle.com/c/understanding_cloud_organization/discussion/118019#latest-678626)]
+
+## Other platforms
+
+### [MICCAI 2020 TN-SCUI challenge](https://tn-scui2020.grand-challenge.org/Home/)  
+- 1st place.
+[Mingyu Wang](https://github.com/WAMAWAMA)  
+[[description](https://github.com/WAMAWAMA/TNSCUI2020-Seg-Rank1st)]
+[[code](https://github.com/WAMAWAMA/TNSCUI2020-Seg-Rank1st)]
+
+### [Open Cities AI Challenge: Segmenting Buildings for Disaster Resilience](https://www.drivendata.org/competitions/60/building-segmentation-disaster-resilience/)
+ - 1st place.
+[Pavel Yakubovskiy](https://www.linkedin.com/in/pavel-yakubovskiy/).  
+[[code and description](https://github.com/qubvel/open-cities-challenge)]
+
diff --git a/segmentation_models_pytorch/LICENSE b/segmentation_models_pytorch/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..fca801c3e26bc990bd3282ea3d6e4276006ccf40
--- /dev/null
+++ b/segmentation_models_pytorch/LICENSE
@@ -0,0 +1,21 @@
+The MIT License
+
+Copyright (c) 2019, Pavel Yakubovskiy
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
diff --git a/segmentation_models_pytorch/MANIFEST.in b/segmentation_models_pytorch/MANIFEST.in
new file mode 100644
index 0000000000000000000000000000000000000000..b2cc4f509e8ba0d699613fe80350ddb7bdd51efd
--- /dev/null
+++ b/segmentation_models_pytorch/MANIFEST.in
@@ -0,0 +1 @@
+include README.md LICENSE requirements.txt
diff --git a/segmentation_models_pytorch/README.md b/segmentation_models_pytorch/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..23418d6035a2d92bbc370a050a4b205db5748590
--- /dev/null
+++ b/segmentation_models_pytorch/README.md
@@ -0,0 +1,409 @@
+<div align="center">
+ 
+![logo](https://i.ibb.co/dc1XdhT/Segmentation-Models-V2-Side-1-1.png)  
+**Python library with Neural Networks for Image  
+Segmentation based on [PyTorch](https://pytorch.org/).**  
+
+[![Documentation Status](https://readthedocs.org/projects/smp/badge/?version=latest)](https://segmentation-models-pytorch.readthedocs.io/en/latest/?badge=latest) <br> [![Generic badge](https://img.shields.io/badge/License-MIT-<COLOR>.svg)](https://shields.io/)
+
+</div>
+
+The main features of this library are:
+
+ - High level API (just two lines to create a neural network)
+ - 12 models architectures for binary and multi class segmentation (including legendary Unet)
+ - 104 available encoders
+ - All encoders have pre-trained weights for faster and better convergence
+ 
+### [📚 Project Documentation 📚](http://smp.readthedocs.io/)
+
+Visit [Read The Docs Project Page](https://segmentation-models-pytorch.readthedocs.io/en/latest/) or read following README to know more about Segmentation Models Pytorch (SMP for short) library
+
+### 📋 Table of content
+ 1. [Quick start](#start)
+ 2. [Examples](#examples)
+ 3. [Models](#models)
+    1. [Architectures](#architectures)
+    2. [Encoders](#encoders)
+ 4. [Models API](#api)
+    1. [Input channels](#input-channels)
+    2. [Auxiliary classification output](#auxiliary-classification-output)
+    3. [Depth](#depth)
+ 5. [Installation](#installation)
+ 6. [Competitions won with the library](#competitions-won-with-the-library)
+ 7. [Contributing](#contributing)
+ 8. [Citing](#citing)
+ 9. [License](#license)
+
+### ⏳ Quick start <a name="start"></a>
+
+#### 1. Create your first Segmentation model with SMP
+
+Segmentation model is just a PyTorch nn.Module, which can be created as easy as:
+
+```python
+import segmentation_models_pytorch as smp
+
+model = smp.Unet(
+    encoder_name="resnet34",        # choose encoder, e.g. mobilenet_v2 or efficientnet-b7
+    encoder_weights="imagenet",     # use `imagenet` pre-trained weights for encoder initialization
+    in_channels=1,                  # model input channels (1 for gray-scale images, 3 for RGB, etc.)
+    classes=3,                      # model output channels (number of classes in your dataset)
+)
+```
+ - see [table](#architectures) with available model architectures
+ - see [table](#encoders) with available encoders and their corresponding weights
+
+#### 2. Configure data preprocessing
+
+All encoders have pretrained weights. Preparing your data the same way as during weights pre-training may give your better results (higher metric score and faster convergence). But it is relevant only for 1-2-3-channels images and **not necessary** in case you train the whole model, not only decoder.
+
+```python
+from segmentation_models_pytorch.encoders import get_preprocessing_fn
+
+preprocess_input = get_preprocessing_fn('resnet18', pretrained='imagenet')
+```
+
+Congratulations! You are done! Now you can train your model with your favorite framework!
+
+### 💡 Examples <a name="examples"></a>
+ - Training model for cars segmentation on CamVid dataset [here](https://github.com/qubvel/segmentation_models.pytorch/blob/master/examples/cars%20segmentation%20(camvid).ipynb).
+ - Training SMP model with [Catalyst](https://github.com/catalyst-team/catalyst) (high-level framework for PyTorch), [TTAch](https://github.com/qubvel/ttach) (TTA library for PyTorch) and [Albumentations](https://github.com/albu/albumentations) (fast image augmentation library) - [here](https://github.com/catalyst-team/catalyst/blob/master/examples/notebooks/segmentation-tutorial.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/catalyst-team/catalyst/blob/master/examples/notebooks/segmentation-tutorial.ipynb)
+ - Training SMP model with [Pytorch-Lightning](https://pytorch-lightning.readthedocs.io) framework - [here](https://github.com/ternaus/cloths_segmentation) (clothes binary segmentation by [@teranus](https://github.com/ternaus)).
+
+### 📦 Models <a name="models"></a>
+
+#### Architectures <a name="architectures"></a>
+ - Unet [[paper](https://arxiv.org/abs/1505.04597)] [[docs](https://smp.readthedocs.io/en/latest/models.html#unet)]
+ - Unet++ [[paper1](https://arxiv.org/abs/1807.10165), [paper2](https://arxiv.org/abs/1912.05074)] [[docs](https://smp.readthedocs.io/en/latest/models.html#id2)]
+ - EfficientUNet++ [[paper]()] [[docs](https://segmentation-models-pytorch.readthedocs.io/en/latest/models.html#efficientunet)]
+ - ResUnet [[paper](https://arxiv.org/abs/1711.10684)] [[docs](https://segmentation-models-pytorch.readthedocs.io/en/latest/models.html#resunet)]
+ - ResUnet++ [[paper](https://arxiv.org/abs/1911.07067)] [[docs](https://segmentation-models-pytorch.readthedocs.io/en/latest/models.html#id4)]
+ - MAnet [[paper](https://ieeexplore.ieee.org/abstract/document/9201310)] [[docs](https://smp.readthedocs.io/en/latest/models.html#manet)]
+ - Linknet [[paper](https://arxiv.org/abs/1707.03718)] [[docs](https://smp.readthedocs.io/en/latest/models.html#linknet)]
+ - FPN [[paper](http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf)] [[docs](https://smp.readthedocs.io/en/latest/models.html#fpn)]
+ - PSPNet [[paper](https://arxiv.org/abs/1612.01105)] [[docs](https://smp.readthedocs.io/en/latest/models.html#pspnet)]
+ - PAN [[paper](https://arxiv.org/abs/1805.10180)] [[docs](https://smp.readthedocs.io/en/latest/models.html#pan)]
+ - DeepLabV3 [[paper](https://arxiv.org/abs/1706.05587)] [[docs](https://smp.readthedocs.io/en/latest/models.html#deeplabv3)]
+ - DeepLabV3+ [[paper](https://arxiv.org/abs/1802.02611)] [[docs](https://smp.readthedocs.io/en/latest/models.html#id9)]
+
+#### Encoders <a name="encoders"></a>
+
+The following is a list of supported encoders in the SMP. Select the appropriate family of encoders and click to expand the table and select a specific encoder and its pre-trained weights (`encoder_name` and `encoder_weights` parameters).
+
+<details>
+<summary style="margin-left: 25px;">ResNet</summary>
+<div style="margin-left: 25px;">
+
+|Encoder                         |Weights                         |Params, M                       |
+|--------------------------------|:------------------------------:|:------------------------------:|
+|resnet18                        |imagenet / ssl / swsl           |11M                             |
+|resnet34                        |imagenet                        |21M                             |
+|resnet50                        |imagenet / ssl / swsl           |23M                             |
+|resnet101                       |imagenet                        |42M                             |
+|resnet152                       |imagenet                        |58M                             |
+
+</div>
+</details>
+
+<details>
+<summary style="margin-left: 25px;">ResNeXt</summary>
+<div style="margin-left: 25px;">
+
+|Encoder                         |Weights                         |Params, M                       |
+|--------------------------------|:------------------------------:|:------------------------------:|
+|resnext50_32x4d                 |imagenet / ssl / swsl           |22M                             |
+|resnext101_32x4d                |ssl / swsl                      |42M                             |
+|resnext101_32x8d                |imagenet / instagram / ssl / swsl|86M                         |
+|resnext101_32x16d               |instagram / ssl / swsl          |191M                            |
+|resnext101_32x32d               |instagram                       |466M                            |
+|resnext101_32x48d               |instagram                       |826M                            |
+
+</div>
+</details>
+
+<details>
+<summary style="margin-left: 25px;">ResNeSt</summary>
+<div style="margin-left: 25px;">
+
+|Encoder                         |Weights                         |Params, M                       |
+|--------------------------------|:------------------------------:|:------------------------------:|
+|timm-resnest14d                 |imagenet                        |8M                              |
+|timm-resnest26d                 |imagenet                        |15M                             |
+|timm-resnest50d                 |imagenet                        |25M                             |
+|timm-resnest101e                |imagenet                        |46M                             |
+|timm-resnest200e                |imagenet                        |68M                             |
+|timm-resnest269e                |imagenet                        |108M                            |
+|timm-resnest50d_4s2x40d         |imagenet                        |28M                             |
+|timm-resnest50d_1s4x24d         |imagenet                        |23M                             |
+
+</div>
+</details>
+
+<details>
+<summary style="margin-left: 25px;">Res2Ne(X)t</summary>
+<div style="margin-left: 25px;">
+
+|Encoder                         |Weights                         |Params, M                       |
+|--------------------------------|:------------------------------:|:------------------------------:|
+|timm-res2net50_26w_4s           |imagenet                        |23M                             |
+|timm-res2net101_26w_4s          |imagenet                        |43M                             |
+|timm-res2net50_26w_6s           |imagenet                        |35M                             |
+|timm-res2net50_26w_8s           |imagenet                        |46M                             |
+|timm-res2net50_48w_2s           |imagenet                        |23M                             |
+|timm-res2net50_14w_8s           |imagenet                        |23M                             |
+|timm-res2next50                 |imagenet                        |22M                             |
+
+</div>
+</details>
+
+<details>
+<summary style="margin-left: 25px;">RegNet(x/y)</summary>
+<div style="margin-left: 25px;">
+
+|Encoder                         |Weights                         |Params, M                       |
+|--------------------------------|:------------------------------:|:------------------------------:|
+|timm-regnetx_002                |imagenet                        |2M                              |
+|timm-regnetx_004                |imagenet                        |4M                              |
+|timm-regnetx_006                |imagenet                        |5M                              |
+|timm-regnetx_008                |imagenet                        |6M                              |
+|timm-regnetx_016                |imagenet                        |8M                              |
+|timm-regnetx_032                |imagenet                        |14M                             |
+|timm-regnetx_040                |imagenet                        |20M                             |
+|timm-regnetx_064                |imagenet                        |24M                             |
+|timm-regnetx_080                |imagenet                        |37M                             |
+|timm-regnetx_120                |imagenet                        |43M                             |
+|timm-regnetx_160                |imagenet                        |52M                             |
+|timm-regnetx_320                |imagenet                        |105M                            |
+|timm-regnety_002                |imagenet                        |2M                              |
+|timm-regnety_004                |imagenet                        |3M                              |
+|timm-regnety_006                |imagenet                        |5M                              |
+|timm-regnety_008                |imagenet                        |5M                              |
+|timm-regnety_016                |imagenet                        |10M                             |
+|timm-regnety_032                |imagenet                        |17M                             |
+|timm-regnety_040                |imagenet                        |19M                             |
+|timm-regnety_064                |imagenet                        |29M                             |
+|timm-regnety_080                |imagenet                        |37M                             |
+|timm-regnety_120                |imagenet                        |49M                             |
+|timm-regnety_160                |imagenet                        |80M                             |
+|timm-regnety_320                |imagenet                        |141M                            |
+
+</div>
+</details>
+
+<details>
+<summary style="margin-left: 25px;">SE-Net</summary>
+<div style="margin-left: 25px;">
+
+|Encoder                         |Weights                         |Params, M                       |
+|--------------------------------|:------------------------------:|:------------------------------:|
+|senet154                        |imagenet                        |113M                            |
+|se_resnet50                     |imagenet                        |26M                             |
+|se_resnet101                    |imagenet                        |47M                             |
+|se_resnet152                    |imagenet                        |64M                             |
+|se_resnext50_32x4d              |imagenet                        |25M                             |
+|se_resnext101_32x4d             |imagenet                        |46M                             |
+
+</div>
+</details>
+
+<details>
+<summary style="margin-left: 25px;">SK-ResNe(X)t</summary>
+<div style="margin-left: 25px;">
+
+|Encoder                         |Weights                         |Params, M                       |
+|--------------------------------|:------------------------------:|:------------------------------:|
+|timm-skresnet18                 |imagenet                        |11M                             |
+|timm-skresnet34                 |imagenet                        |21M                             |
+|timm-skresnext50_32x4d          |imagenet                        |25M                             |
+
+</div>
+</details>
+
+<details>
+<summary style="margin-left: 25px;">DenseNet</summary>
+<div style="margin-left: 25px;">
+
+|Encoder                         |Weights                         |Params, M                       |
+|--------------------------------|:------------------------------:|:------------------------------:|
+|densenet121                     |imagenet                        |6M                              |
+|densenet169                     |imagenet                        |12M                             |
+|densenet201                     |imagenet                        |18M                             |
+|densenet161                     |imagenet                        |26M                             |
+
+</div>
+</details>
+
+<details>
+<summary style="margin-left: 25px;">Inception</summary>
+<div style="margin-left: 25px;">
+
+|Encoder                         |Weights                         |Params, M                       |
+|--------------------------------|:------------------------------:|:------------------------------:|
+|inceptionresnetv2               |imagenet /  imagenet+background |54M                             |
+|inceptionv4                     |imagenet /  imagenet+background |41M                             |
+|xception                        |imagenet                        |22M                             |
+
+</div>
+</details>
+
+<details>
+<summary style="margin-left: 25px;">EfficientNet</summary>
+<div style="margin-left: 25px;">
+
+|Encoder                         |Weights                         |Params, M                       |
+|--------------------------------|:------------------------------:|:------------------------------:|
+|efficientnet-b0                 |imagenet                        |4M                              |
+|efficientnet-b1                 |imagenet                        |6M                              |
+|efficientnet-b2                 |imagenet                        |7M                              |
+|efficientnet-b3                 |imagenet                        |10M                             |
+|efficientnet-b4                 |imagenet                        |17M                             |
+|efficientnet-b5                 |imagenet                        |28M                             |
+|efficientnet-b6                 |imagenet                        |40M                             |
+|efficientnet-b7                 |imagenet                        |63M                             |
+|timm-efficientnet-b0            |imagenet / advprop / noisy-student|4M                              |
+|timm-efficientnet-b1            |imagenet / advprop / noisy-student|6M                              |
+|timm-efficientnet-b2            |imagenet / advprop / noisy-student|7M                              |
+|timm-efficientnet-b3            |imagenet / advprop / noisy-student|10M                             |
+|timm-efficientnet-b4            |imagenet / advprop / noisy-student|17M                             |
+|timm-efficientnet-b5            |imagenet / advprop / noisy-student|28M                             |
+|timm-efficientnet-b6            |imagenet / advprop / noisy-student|40M                             |
+|timm-efficientnet-b7            |imagenet / advprop / noisy-student|63M                             |
+|timm-efficientnet-b8            |imagenet / advprop             |84M                             |
+|timm-efficientnet-l2            |noisy-student                   |474M                            |
+|timm-efficientnet-lite0         |imagenet                        |4M                              |
+|timm-efficientnet-lite1         |imagenet                        |5M                              |
+|timm-efficientnet-lite2         |imagenet                        |6M                              |
+|timm-efficientnet-lite3         |imagenet                        |8M                             |
+|timm-efficientnet-lite4         |imagenet                        |13M                             |
+
+</div>
+</details>
+
+<details>
+<summary style="margin-left: 25px;">MobileNet</summary>
+<div style="margin-left: 25px;">
+
+|Encoder                         |Weights                         |Params, M                       |
+|--------------------------------|:------------------------------:|:------------------------------:|
+|mobilenet_v2                    |imagenet                        |2M                              |
+
+</div>
+</details>
+
+<details>
+<summary style="margin-left: 25px;">DPN</summary>
+<div style="margin-left: 25px;">
+
+|Encoder                         |Weights                         |Params, M                       |
+|--------------------------------|:------------------------------:|:------------------------------:|
+|dpn68                           |imagenet                        |11M                             |
+|dpn68b                          |imagenet+5k                     |11M                             |
+|dpn92                           |imagenet+5k                     |34M                             |
+|dpn98                           |imagenet                        |58M                             |
+|dpn107                          |imagenet+5k                     |84M                             |
+|dpn131                          |imagenet                        |76M                             |
+
+</div>
+</details>
+
+<details>
+<summary style="margin-left: 25px;">VGG</summary>
+<div style="margin-left: 25px;">
+
+|Encoder                         |Weights                         |Params, M                       |
+|--------------------------------|:------------------------------:|:------------------------------:|
+|vgg11                           |imagenet                        |9M                              |
+|vgg11_bn                        |imagenet                        |9M                              |
+|vgg13                           |imagenet                        |9M                              |
+|vgg13_bn                        |imagenet                        |9M                              |
+|vgg16                           |imagenet                        |14M                             |
+|vgg16_bn                        |imagenet                        |14M                             |
+|vgg19                           |imagenet                        |20M                             |
+|vgg19_bn                        |imagenet                        |20M                             |
+
+</div>
+</details>
+
+
+\* `ssl`, `swsl` - semi-supervised and weakly-supervised learning on ImageNet ([repo](https://github.com/facebookresearch/semi-supervised-ImageNet1K-models)).
+
+
+### 🔁 Models API <a name="api"></a>
+
+ - `model.encoder` - pretrained backbone to extract features of different spatial resolution
+ - `model.decoder` - depends on models architecture (`Unet`/`Linknet`/`PSPNet`/`FPN`)
+ - `model.segmentation_head` - last block to produce required number of mask channels (include also optional upsampling and activation)
+ - `model.classification_head` - optional block which create classification head on top of encoder
+ - `model.forward(x)` - sequentially pass `x` through model\`s encoder, decoder and segmentation head (and classification head if specified)
+
+##### Input channels
+Input channels parameter allows you to create models, which process tensors with arbitrary number of channels.
+If you use pretrained weights from imagenet - weights of first convolution will be reused for
+1- or 2- channels inputs, for input channels > 4 weights of first convolution will be initialized randomly.
+```python
+model = smp.FPN('resnet34', in_channels=1)
+mask = model(torch.ones([1, 1, 64, 64]))
+```
+
+##### Auxiliary classification output  
+All models support `aux_params` parameters, which is default set to `None`. 
+If `aux_params = None` then classification auxiliary output is not created, else
+model produce not only `mask`, but also `label` output with shape `NC`.
+Classification head consists of GlobalPooling->Dropout(optional)->Linear->Activation(optional) layers, which can be 
+configured by `aux_params` as follows:
+```python
+aux_params=dict(
+    pooling='avg',             # one of 'avg', 'max'
+    dropout=0.5,               # dropout ratio, default is None
+    activation='sigmoid',      # activation function, default is None
+    classes=4,                 # define number of output labels
+)
+model = smp.Unet('resnet34', classes=4, aux_params=aux_params)
+mask, label = model(x)
+```
+
+##### Depth
+Depth parameter specify a number of downsampling operations in encoder, so you can make
+your model lighter if specify smaller `depth`.
+```python
+model = smp.Unet('resnet34', encoder_depth=4)
+```
+
+
+### 🛠 Installation <a name="installation"></a>
+Latest version from source:
+```bash
+$ pip install git+https://github.com/jlcsilva/segmentation_models.pytorch
+````
+
+### 🏆 Competitions won with the library
+
+`Segmentation Models` package is widely used in the image segmentation competitions.
+[Here](https://github.com/qubvel/segmentation_models.pytorch/blob/master/HALLOFFAME.md) you can find competitions, names of the winners and links to their solutions.
+
+### 🤝 Contributing
+
+##### Run test
+```bash
+$ docker build -f docker/Dockerfile.dev -t smp:dev . && docker run --rm smp:dev pytest -p no:cacheprovider
+```
+##### Generate table
+```bash
+$ docker build -f docker/Dockerfile.dev -t smp:dev . && docker run --rm smp:dev python misc/generate_table.py
+```
+
+### 📝 Citing
+```
+@misc{Yakubovskiy:2019,
+  Author = {Pavel Yakubovskiy},
+  Title = {Segmentation Models Pytorch},
+  Year = {2020},
+  Publisher = {GitHub},
+  Journal = {GitHub repository},
+  Howpublished = {\url{https://github.com/qubvel/segmentation_models.pytorch}}
+}
+```
+
+### 🛡️ License <a name="license"></a>
+Project is distributed under [MIT License](https://github.com/qubvel/segmentation_models.pytorch/blob/master/LICENSE)
diff --git a/segmentation_models_pytorch/__init__.py b/segmentation_models_pytorch/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc8a79aadfff36a6d49dbb89592e315fa6463a98
--- /dev/null
+++ b/segmentation_models_pytorch/__init__.py
@@ -0,0 +1 @@
+from segmentation_models_pytorch import *
\ No newline at end of file
diff --git a/segmentation_models_pytorch/docker/Dockerfile b/segmentation_models_pytorch/docker/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..8877454407cd7584054fabcd56978df58f08f0be
--- /dev/null
+++ b/segmentation_models_pytorch/docker/Dockerfile
@@ -0,0 +1,3 @@
+FROM anibali/pytorch:cuda-9.0
+
+RUN pip install segmentation-models-pytorch
\ No newline at end of file
diff --git a/segmentation_models_pytorch/docker/Dockerfile.dev b/segmentation_models_pytorch/docker/Dockerfile.dev
new file mode 100644
index 0000000000000000000000000000000000000000..2a7abbe90b1467f39f6528d2d320a8f4afdc1daf
--- /dev/null
+++ b/segmentation_models_pytorch/docker/Dockerfile.dev
@@ -0,0 +1,10 @@
+FROM anibali/pytorch:1.5.0-nocuda
+
+WORKDIR /tmp/smp/
+
+COPY ./requirements.txt /tmp/smp/requirements.txt
+RUN pip install -r requirements.txt
+RUN pip install pytest mock
+
+COPY . /tmp/smp/
+RUN pip install .
diff --git a/segmentation_models_pytorch/docs/Makefile b/segmentation_models_pytorch/docs/Makefile
new file mode 100644
index 0000000000000000000000000000000000000000..ed88099027f775942fa65dce2314f1ae9675cb36
--- /dev/null
+++ b/segmentation_models_pytorch/docs/Makefile
@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/segmentation_models_pytorch/docs/conf.py b/segmentation_models_pytorch/docs/conf.py
new file mode 100644
index 0000000000000000000000000000000000000000..71253e4c7082d72a4db026110c30b5b44fe35043
--- /dev/null
+++ b/segmentation_models_pytorch/docs/conf.py
@@ -0,0 +1,120 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+# import os
+# import sys
+# sys.path.insert(0, os.path.abspath('.'))
+
+import os
+import re
+import sys
+import datetime
+sys.path.append('..')
+
+# -- Project information -----------------------------------------------------
+
+project = 'Segmentation Models'
+copyright = '{}, Pavel Yakubovskiy'.format(datetime.datetime.now().year)
+author = 'Pavel Yakubovskiy'
+
+def get_version():
+    sys.path.append('../segmentation_models_pytorch')
+    from __version__ import __version__ as version
+    sys.path.pop(-1)
+    return version
+
+version = get_version()
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+
+extensions = [
+    'sphinx.ext.autodoc',
+    'sphinx.ext.coverage',
+    'sphinx.ext.napoleon',
+    'sphinx.ext.viewcode',
+    'sphinx.ext.mathjax', 
+]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = []
+
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+
+import sphinx_rtd_theme
+html_theme = "sphinx_rtd_theme"
+html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
+
+# import karma_sphinx_theme
+# html_theme = "karma_sphinx_theme"
+import faculty_sphinx_theme
+html_theme = "faculty_sphinx_theme"
+
+# import catalyst_sphinx_theme
+# html_theme = "catalyst_sphinx_theme"
+# html_theme_path = [catalyst_sphinx_theme.get_html_theme_path()]
+
+html_logo = "logo.png"
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
+
+# -- Extension configuration -------------------------------------------------
+
+autodoc_inherit_docstrings = False
+napoleon_google_docstring = True
+napoleon_include_init_with_doc = True
+napoleon_numpy_docstring = False
+
+autodoc_mock_imports = [
+    'torch',
+    'tqdm',
+    'numpy',
+    'timm',
+    'pretrainedmodels',
+    'torchvision',
+    'efficientnet-pytorch',
+    'segmentation_models_pytorch.encoders',
+    'segmentation_models_pytorch.utils',
+    # 'segmentation_models_pytorch.base',
+]
+
+autoclass_content = 'both'
+autodoc_typehints = 'description'
+
+# --- Work around to make autoclass signatures not (*args, **kwargs) ----------
+
+class FakeSignature():
+    def __getattribute__(self, *args):
+        raise ValueError
+
+def f(app, obj, bound_method):
+    if "__new__" in obj.__name__:
+        obj.__signature__ = FakeSignature()
+
+def setup(app):
+    app.connect('autodoc-before-process-signature', f)
diff --git a/segmentation_models_pytorch/docs/encoders.rst b/segmentation_models_pytorch/docs/encoders.rst
new file mode 100644
index 0000000000000000000000000000000000000000..62c381c48d87887904202d56f1e5d087199bb0ca
--- /dev/null
+++ b/segmentation_models_pytorch/docs/encoders.rst
@@ -0,0 +1,301 @@
+🏔 Available Encoders
+=====================
+
+ResNet
+~~~~~~
+
++-------------+-------------------------+-------------+
+| Encoder     | Weights                 | Params, M   |
++=============+=========================+=============+
+| resnet18    | imagenet / ssl / swsl   | 11M         |
++-------------+-------------------------+-------------+
+| resnet34    | imagenet                | 21M         |
++-------------+-------------------------+-------------+
+| resnet50    | imagenet / ssl / swsl   | 23M         |
++-------------+-------------------------+-------------+
+| resnet101   | imagenet                | 42M         |
++-------------+-------------------------+-------------+
+| resnet152   | imagenet                | 58M         |
++-------------+-------------------------+-------------+
+
+ResNeXt
+~~~~~~~
+
++----------------------+-------------------------------------+-------------+
+| Encoder              | Weights                             | Params, M   |
++======================+=====================================+=============+
+| resnext50\_32x4d     | imagenet / ssl / swsl               | 22M         |
++----------------------+-------------------------------------+-------------+
+| resnext101\_32x4d    | ssl / swsl                          | 42M         |
++----------------------+-------------------------------------+-------------+
+| resnext101\_32x8d    | imagenet / instagram / ssl / swsl   | 86M         |
++----------------------+-------------------------------------+-------------+
+| resnext101\_32x16d   | instagram / ssl / swsl              | 191M        |
++----------------------+-------------------------------------+-------------+
+| resnext101\_32x32d   | instagram                           | 466M        |
++----------------------+-------------------------------------+-------------+
+| resnext101\_32x48d   | instagram                           | 826M        |
++----------------------+-------------------------------------+-------------+
+
+ResNeSt
+~~~~~~~
+
++----------------------------+------------+-------------+
+| Encoder                    | Weights    | Params, M   |
++============================+============+=============+
+| timm-resnest14d            | imagenet   | 8M          |
++----------------------------+------------+-------------+
+| timm-resnest26d            | imagenet   | 15M         |
++----------------------------+------------+-------------+
+| timm-resnest50d            | imagenet   | 25M         |
++----------------------------+------------+-------------+
+| timm-resnest101e           | imagenet   | 46M         |
++----------------------------+------------+-------------+
+| timm-resnest200e           | imagenet   | 68M         |
++----------------------------+------------+-------------+
+| timm-resnest269e           | imagenet   | 108M        |
++----------------------------+------------+-------------+
+| timm-resnest50d\_4s2x40d   | imagenet   | 28M         |
++----------------------------+------------+-------------+
+| timm-resnest50d\_1s4x24d   | imagenet   | 23M         |
++----------------------------+------------+-------------+
+
+Res2Ne(X)t
+~~~~~~~~~~
+
++----------------------------+------------+-------------+
+| Encoder                    | Weights    | Params, M   |
++============================+============+=============+
+| timm-res2net50\_26w\_4s    | imagenet   | 23M         |
++----------------------------+------------+-------------+
+| timm-res2net101\_26w\_4s   | imagenet   | 43M         |
++----------------------------+------------+-------------+
+| timm-res2net50\_26w\_6s    | imagenet   | 35M         |
++----------------------------+------------+-------------+
+| timm-res2net50\_26w\_8s    | imagenet   | 46M         |
++----------------------------+------------+-------------+
+| timm-res2net50\_48w\_2s    | imagenet   | 23M         |
++----------------------------+------------+-------------+
+| timm-res2net50\_14w\_8s    | imagenet   | 23M         |
++----------------------------+------------+-------------+
+| timm-res2next50            | imagenet   | 22M         |
++----------------------------+------------+-------------+
+
+RegNet(x/y)
+~~~~~~~~~~~
+
++---------------------+------------+-------------+
+| Encoder             | Weights    | Params, M   |
++=====================+============+=============+
+| timm-regnetx\_002   | imagenet   | 2M          |
++---------------------+------------+-------------+
+| timm-regnetx\_004   | imagenet   | 4M          |
++---------------------+------------+-------------+
+| timm-regnetx\_006   | imagenet   | 5M          |
++---------------------+------------+-------------+
+| timm-regnetx\_008   | imagenet   | 6M          |
++---------------------+------------+-------------+
+| timm-regnetx\_016   | imagenet   | 8M          |
++---------------------+------------+-------------+
+| timm-regnetx\_032   | imagenet   | 14M         |
++---------------------+------------+-------------+
+| timm-regnetx\_040   | imagenet   | 20M         |
++---------------------+------------+-------------+
+| timm-regnetx\_064   | imagenet   | 24M         |
++---------------------+------------+-------------+
+| timm-regnetx\_080   | imagenet   | 37M         |
++---------------------+------------+-------------+
+| timm-regnetx\_120   | imagenet   | 43M         |
++---------------------+------------+-------------+
+| timm-regnetx\_160   | imagenet   | 52M         |
++---------------------+------------+-------------+
+| timm-regnetx\_320   | imagenet   | 105M        |
++---------------------+------------+-------------+
+| timm-regnety\_002   | imagenet   | 2M          |
++---------------------+------------+-------------+
+| timm-regnety\_004   | imagenet   | 3M          |
++---------------------+------------+-------------+
+| timm-regnety\_006   | imagenet   | 5M          |
++---------------------+------------+-------------+
+| timm-regnety\_008   | imagenet   | 5M          |
++---------------------+------------+-------------+
+| timm-regnety\_016   | imagenet   | 10M         |
++---------------------+------------+-------------+
+| timm-regnety\_032   | imagenet   | 17M         |
++---------------------+------------+-------------+
+| timm-regnety\_040   | imagenet   | 19M         |
++---------------------+------------+-------------+
+| timm-regnety\_064   | imagenet   | 29M         |
++---------------------+------------+-------------+
+| timm-regnety\_080   | imagenet   | 37M         |
++---------------------+------------+-------------+
+| timm-regnety\_120   | imagenet   | 49M         |
++---------------------+------------+-------------+
+| timm-regnety\_160   | imagenet   | 80M         |
++---------------------+------------+-------------+
+| timm-regnety\_320   | imagenet   | 141M        |
++---------------------+------------+-------------+
+
+SE-Net
+~~~~~~
+
++-------------------------+------------+-------------+
+| Encoder                 | Weights    | Params, M   |
++=========================+============+=============+
+| senet154                | imagenet   | 113M        |
++-------------------------+------------+-------------+
+| se\_resnet50            | imagenet   | 26M         |
++-------------------------+------------+-------------+
+| se\_resnet101           | imagenet   | 47M         |
++-------------------------+------------+-------------+
+| se\_resnet152           | imagenet   | 64M         |
++-------------------------+------------+-------------+
+| se\_resnext50\_32x4d    | imagenet   | 25M         |
++-------------------------+------------+-------------+
+| se\_resnext101\_32x4d   | imagenet   | 46M         |
++-------------------------+------------+-------------+
+
+SK-ResNe(X)t
+~~~~~~~~~~~~
+
++---------------------------+------------+-------------+
+| Encoder                   | Weights    | Params, M   |
++===========================+============+=============+
+| timm-skresnet18           | imagenet   | 11M         |
++---------------------------+------------+-------------+
+| timm-skresnet34           | imagenet   | 21M         |
++---------------------------+------------+-------------+
+| timm-skresnext50\_32x4d   | imagenet   | 25M         |
++---------------------------+------------+-------------+
+
+DenseNet
+~~~~~~~~
+
++---------------+------------+-------------+
+| Encoder       | Weights    | Params, M   |
++===============+============+=============+
+| densenet121   | imagenet   | 6M          |
++---------------+------------+-------------+
+| densenet169   | imagenet   | 12M         |
++---------------+------------+-------------+
+| densenet201   | imagenet   | 18M         |
++---------------+------------+-------------+
+| densenet161   | imagenet   | 26M         |
++---------------+------------+-------------+
+
+Inception
+~~~~~~~~~
+
++---------------------+----------------------------------+-------------+
+| Encoder             | Weights                          | Params, M   |
++=====================+==================================+=============+
+| inceptionresnetv2   | imagenet / imagenet+background   | 54M         |
++---------------------+----------------------------------+-------------+
+| inceptionv4         | imagenet / imagenet+background   | 41M         |
++---------------------+----------------------------------+-------------+
+| xception            | imagenet                         | 22M         |
++---------------------+----------------------------------+-------------+
+
+EfficientNet
+~~~~~~~~~~~~
+
++------------------------+--------------------------------------+-------------+
+| Encoder                | Weights                              | Params, M   |
++========================+======================================+=============+
+| efficientnet-b0        | imagenet                             | 4M          |
++------------------------+--------------------------------------+-------------+
+| efficientnet-b1        | imagenet                             | 6M          |
++------------------------+--------------------------------------+-------------+
+| efficientnet-b2        | imagenet                             | 7M          |
++------------------------+--------------------------------------+-------------+
+| efficientnet-b3        | imagenet                             | 10M         |
++------------------------+--------------------------------------+-------------+
+| efficientnet-b4        | imagenet                             | 17M         |
++------------------------+--------------------------------------+-------------+
+| efficientnet-b5        | imagenet                             | 28M         |
++------------------------+--------------------------------------+-------------+
+| efficientnet-b6        | imagenet                             | 40M         |
++------------------------+--------------------------------------+-------------+
+| efficientnet-b7        | imagenet                             | 63M         |
++------------------------+--------------------------------------+-------------+
+| timm-efficientnet-b0   | imagenet / advprop / noisy-student   | 4M          |
++------------------------+--------------------------------------+-------------+
+| timm-efficientnet-b1   | imagenet / advprop / noisy-student   | 6M          |
++------------------------+--------------------------------------+-------------+
+| timm-efficientnet-b2   | imagenet / advprop / noisy-student   | 7M          |
++------------------------+--------------------------------------+-------------+
+| timm-efficientnet-b3   | imagenet / advprop / noisy-student   | 10M         |
++------------------------+--------------------------------------+-------------+
+| timm-efficientnet-b4   | imagenet / advprop / noisy-student   | 17M         |
++------------------------+--------------------------------------+-------------+
+| timm-efficientnet-b5   | imagenet / advprop / noisy-student   | 28M         |
++------------------------+--------------------------------------+-------------+
+| timm-efficientnet-b6   | imagenet / advprop / noisy-student   | 40M         |
++------------------------+--------------------------------------+-------------+
+| timm-efficientnet-b7   | imagenet / advprop / noisy-student   | 63M         |
++------------------------+--------------------------------------+-------------+
+| timm-efficientnet-b8   | imagenet / advprop                   | 84M         |
++------------------------+--------------------------------------+-------------+
+| timm-efficientnet-l2   | noisy-student                        | 474M        |
++------------------------+--------------------------------------+-------------+
+| timm-efficientnet-lite0| imagenet                             | 4M          |
++------------------------+--------------------------------------+-------------+
+| timm-efficientnet-lite1| imagenet                             | 4M          |
++------------------------+--------------------------------------+-------------+
+| timm-efficientnet-lite2| imagenet                             | 6M          |
++------------------------+--------------------------------------+-------------+
+| timm-efficientnet-lite3| imagenet                             | 8M          |
++------------------------+--------------------------------------+-------------+
+| timm-efficientnet-lite4| imagenet                             | 13M         |
++------------------------+--------------------------------------+-------------+
+
+MobileNet
+~~~~~~~~~
+
++-----------------+------------+-------------+
+| Encoder         | Weights    | Params, M   |
++=================+============+=============+
+| mobilenet\_v2   | imagenet   | 2M          |
++-----------------+------------+-------------+
+
+DPN
+~~~
+
++-----------+---------------+-------------+
+| Encoder   | Weights       | Params, M   |
++===========+===============+=============+
+| dpn68     | imagenet      | 11M         |
++-----------+---------------+-------------+
+| dpn68b    | imagenet+5k   | 11M         |
++-----------+---------------+-------------+
+| dpn92     | imagenet+5k   | 34M         |
++-----------+---------------+-------------+
+| dpn98     | imagenet      | 58M         |
++-----------+---------------+-------------+
+| dpn107    | imagenet+5k   | 84M         |
++-----------+---------------+-------------+
+| dpn131    | imagenet      | 76M         |
++-----------+---------------+-------------+
+
+VGG
+~~~
+
++-------------+------------+-------------+
+| Encoder     | Weights    | Params, M   |
++=============+============+=============+
+| vgg11       | imagenet   | 9M          |
++-------------+------------+-------------+
+| vgg11\_bn   | imagenet   | 9M          |
++-------------+------------+-------------+
+| vgg13       | imagenet   | 9M          |
++-------------+------------+-------------+
+| vgg13\_bn   | imagenet   | 9M          |
++-------------+------------+-------------+
+| vgg16       | imagenet   | 14M         |
++-------------+------------+-------------+
+| vgg16\_bn   | imagenet   | 14M         |
++-------------+------------+-------------+
+| vgg19       | imagenet   | 20M         |
++-------------+------------+-------------+
+| vgg19\_bn   | imagenet   | 20M         |
++-------------+------------+-------------+
diff --git a/segmentation_models_pytorch/docs/index.rst b/segmentation_models_pytorch/docs/index.rst
new file mode 100644
index 0000000000000000000000000000000000000000..69bf6e1932682746ed761faf652bf2b9169145dc
--- /dev/null
+++ b/segmentation_models_pytorch/docs/index.rst
@@ -0,0 +1,26 @@
+.. Segmentation Models documentation master file, created by
+   sphinx-quickstart on Fri Nov 27 00:00:20 2020.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Welcome to Segmentation Models's documentation!
+===============================================
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+   install
+   quickstart
+   models
+   encoders
+   losses
+   insights
+
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
diff --git a/segmentation_models_pytorch/docs/insights.rst b/segmentation_models_pytorch/docs/insights.rst
new file mode 100644
index 0000000000000000000000000000000000000000..6489dfd1d6504406efb860eb315eafd097aa91c0
--- /dev/null
+++ b/segmentation_models_pytorch/docs/insights.rst
@@ -0,0 +1,119 @@
+🔧 Insights
+===========
+
+1. Models architecture
+~~~~~~~~~~~~~~~~~~~~~~
+
+All segmentation models in SMP (this library short name) are made of:
+
+- encoder (feature extractor, a.k.a backbone)
+- decoder (features fusion block to create segmentation *mask*)
+- segmentation head (final head to reduce number of channels from decoder and upsample mask to preserve input-output spatial resolution identity)
+- classification head (optional head which build on top of deepest encoder features)
+
+
+2. Creating your own encoder
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Encoder is a "classification model" which extract features from image and pass it to decoder.
+Each encoder should have following attributes and methods and be inherited from `segmentation_models_pytorch.encoders._base.EncoderMixin`
+
+.. code-block:: python
+
+    class MyEncoder(torch.nn.Module, EncoderMixin):
+        
+        def __init__(self, **kwargs):
+            super().__init__()
+            
+            # A number of channels for each encoder feature tensor, list of integers
+            self._out_channels: List[int] = [3, 16, 64, 128, 256, 512]
+
+            # A number of stages in decoder (in other words number of downsampling operations), integer
+            # use in in forward pass to reduce number of returning features
+            self._depth: int = 5 
+
+            # Default number of input channels in first Conv2d layer for encoder (usually 3)
+            self._in_channels: int = 3 
+            
+            # Define encoder modules below
+            ...
+
+        def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
+            """Produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of
+            shape NCHW (features should be sorted in descending order according to spatial resolution, starting
+            with resolution same as input `x` tensor).
+
+            Input: `x` with shape (1, 3, 64, 64)
+            Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes
+                    [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8),
+                    (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ)
+
+            also should support number of features according to specified depth, e.g. if depth = 5,
+            number of feature tensors = 6 (one with same resolution as input and 5 downsampled),
+            depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled).
+            """
+
+            return [feat1, feat2, feat3, feat4, feat5, feat6]
+
+When you write your own Encoder class register its build parameters
+
+.. code-block:: python
+
+    smp.encoders.encoders["my_awesome_encoder"] = {
+        "encoder": MyEncoder, # encoder class here
+        "pretrained_settings": {
+            "imagenet": {
+                "mean": [0.485, 0.456, 0.406],
+                "std": [0.229, 0.224, 0.225],
+                "url": "https://some-url.com/my-model-weights",
+                "input_space": "RGB",
+                "input_range": [0, 1],
+            },
+        },
+        "params": {
+            # init params for encoder if any
+        },
+    },
+
+Now you can use your encoder
+
+.. code-block:: python
+
+    model = smp.Unet(encoder_name="my_awesome_encoder")
+
+For better understanding see more examples of encoder in smp.encoders module.
+
+.. note::
+
+    If it works fine, don`t forget to contribute your work and make a PR to SMP 😉
+
+3. Aux classification output
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+All models support ``aux_params`` parameter, which is default set to ``None``. 
+If ``aux_params = None`` than classification auxiliary output is not created, else
+model produce not only ``mask``, but also ``label`` output with shape ``(N, C)``.
+
+Classification head consist of following layers:
+    
+1. GlobalPooling
+2. Dropout (optional)
+3. Linear
+4. Activation (optional)
+
+Example:
+
+.. code-block:: python
+    
+    aux_params=dict(
+        pooling='avg',             # one of 'avg', 'max'
+        dropout=0.5,               # dropout ratio, default is None
+        activation='sigmoid',      # activation function, default is None
+        classes=4,                 # define number of output labels
+    )
+
+    model = smp.Unet('resnet34', classes=4, aux_params=aux_params)
+    mask, label = model(x)
+
+    mask.shape, label.shape
+    # (N, 4, H, W), (N, 4)
diff --git a/segmentation_models_pytorch/docs/install.rst b/segmentation_models_pytorch/docs/install.rst
new file mode 100644
index 0000000000000000000000000000000000000000..cd838b38c76c27c5921f1211a62fa93122fa3131
--- /dev/null
+++ b/segmentation_models_pytorch/docs/install.rst
@@ -0,0 +1,8 @@
+🛠 Installation
+===============
+
+Latest version from source:
+
+.. code-block:: bash
+
+    $ pip install -U git+https://github.com/jlcsilva/segmentation_models.pytorch
\ No newline at end of file
diff --git a/segmentation_models_pytorch/docs/losses.rst b/segmentation_models_pytorch/docs/losses.rst
new file mode 100644
index 0000000000000000000000000000000000000000..333088fa648b489836b808e7def83881ec08edac
--- /dev/null
+++ b/segmentation_models_pytorch/docs/losses.rst
@@ -0,0 +1,34 @@
+📉 Losses
+=========
+
+Collection of popular semantic segmentation losses. Adapted from 
+an awesome repo with pytorch utils https://github.com/BloodAxe/pytorch-toolbelt
+
+Constants
+~~~~~~~~~
+.. automodule:: segmentation_models_pytorch.losses.constants
+        :members:
+
+JaccardLoss
+~~~~~~~~~~~
+.. autoclass:: segmentation_models_pytorch.losses.JaccardLoss
+
+DiceLoss
+~~~~~~~~
+.. autoclass:: segmentation_models_pytorch.losses.DiceLoss
+
+FocalLoss
+~~~~~~~~~
+.. autoclass:: segmentation_models_pytorch.losses.FocalLoss
+
+LovaszLoss
+~~~~~~~~~~
+.. autoclass:: segmentation_models_pytorch.losses.LovaszLoss
+
+SoftBCEWithLogitsLoss
+~~~~~~~~~~~~~~~~~~~~~
+.. autoclass:: segmentation_models_pytorch.losses.SoftBCEWithLogitsLoss
+
+SoftCrossEntropyLoss
+~~~~~~~~~~~~~~~~~~~~
+.. autoclass:: segmentation_models_pytorch.losses.SoftCrossEntropyLoss
diff --git a/segmentation_models_pytorch/docs/make.bat b/segmentation_models_pytorch/docs/make.bat
new file mode 100644
index 0000000000000000000000000000000000000000..6247f7e231716482115f34084ac61030743e0715
--- /dev/null
+++ b/segmentation_models_pytorch/docs/make.bat
@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=source
+set BUILDDIR=build
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.http://sphinx-doc.org/
+	exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd
diff --git a/segmentation_models_pytorch/docs/models.rst b/segmentation_models_pytorch/docs/models.rst
new file mode 100644
index 0000000000000000000000000000000000000000..6355e0d87c3fe3f569c7c893be45fa6284ec1bbf
--- /dev/null
+++ b/segmentation_models_pytorch/docs/models.rst
@@ -0,0 +1,52 @@
+📦 Segmentation Models
+==============================
+
+Unet
+~~~~
+.. autoclass:: segmentation_models_pytorch.Unet
+
+Unet++
+~~~~~~
+.. autoclass:: segmentation_models_pytorch.UnetPlusPlus
+
+EfficientUNet++
+~~~~~~~~~~~~~~~
+.. autoclass:: segmentation_models_pytorch.EfficientUnetPlusPlus
+
+ResUnet
+~~~~~~~
+.. autoclass:: segmentation_models_pytorch.ResUnet
+
+ResUnet++
+~~~~~~~~~
+.. autoclass:: segmentation_models_pytorch.ResUnetPlusPlus
+
+MAnet
+~~~~~~
+.. autoclass:: segmentation_models_pytorch.MAnet
+
+Linknet
+~~~~~~~
+.. autoclass:: segmentation_models_pytorch.Linknet
+
+FPN
+~~~
+.. autoclass:: segmentation_models_pytorch.FPN
+
+PSPNet
+~~~~~~
+.. autoclass:: segmentation_models_pytorch.PSPNet
+
+PAN
+~~~
+.. autoclass:: segmentation_models_pytorch.PAN
+
+DeepLabV3
+~~~~~~~~~
+.. autoclass:: segmentation_models_pytorch.DeepLabV3
+
+DeepLabV3+
+~~~~~~~~~~
+.. autoclass:: segmentation_models_pytorch.DeepLabV3Plus
+
+
diff --git a/segmentation_models_pytorch/docs/quickstart.rst b/segmentation_models_pytorch/docs/quickstart.rst
new file mode 100644
index 0000000000000000000000000000000000000000..60f4f287aea83dd292e6b36b03465c5307fbd6da
--- /dev/null
+++ b/segmentation_models_pytorch/docs/quickstart.rst
@@ -0,0 +1,36 @@
+⏳ Quick Start
+==============
+
+**1. Create segmentation model**
+
+Segmentation model is just a PyTorch nn.Module, which can be created as easy as:
+
+.. code-block:: python
+    
+    import segmentation_models_pytorch as smp
+
+    model = smp.Unet(
+        encoder_name="resnet34",        # choose encoder, e.g. mobilenet_v2 or efficientnet-b7
+        encoder_weights="imagenet",     # use `imagenet` pre-trained weights for encoder initialization
+        in_channels=1,                  # model input channels (1 for gray-scale images, 3 for RGB, etc.)
+        classes=3,                      # model output channels (number of classes in your dataset)
+    )
+
+- see table with available model architectures
+- see table with avaliable encoders and its corresponding weights
+
+**2. Configure data preprocessing**
+
+All encoders have pretrained weights. Preparing your data the same way as during weights pre-training may give your better results (higher metric score and faster convergence). But it is relevant only for 1-2-3-channels images and **not necessary** in case you train the whole model, not only decoder.
+
+.. code-block:: python
+
+    from segmentation_models_pytorch.encoders import get_preprocessing_fn
+
+    preprocess_input = get_preprocessing_fn('resnet18', pretrained='imagenet')
+
+
+**3. Congratulations!** 🎉
+
+
+You are done! Now you can train your model with your favorite framework!
diff --git a/segmentation_models_pytorch/docs/requirements.txt b/segmentation_models_pytorch/docs/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..11072118251c67b3dfff69fc3980bc1acfb837dc
--- /dev/null
+++ b/segmentation_models_pytorch/docs/requirements.txt
@@ -0,0 +1,2 @@
+faculty-sphinx-theme==0.2.2
+six==1.15.0
\ No newline at end of file
diff --git a/segmentation_models_pytorch/examples/cars segmentation (camvid).ipynb b/segmentation_models_pytorch/examples/cars segmentation (camvid).ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..dfad7d8dfe0320fb6235fe84c614cb044857d363
--- /dev/null
+++ b/segmentation_models_pytorch/examples/cars segmentation (camvid).ipynb	
@@ -0,0 +1,903 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install required libs\n",
+    "#!pip install -U segmentation-models-pytorch albumentations --user "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall -y segmentation-models-pytorch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this example we will use **CamVid** dataset. It is a set of:\n",
+    " - **train** images + segmentation masks\n",
+    " - **validation** images + segmentation masks\n",
+    " - **test** images + segmentation masks\n",
+    " \n",
+    "All images have 320 pixels height and 480 pixels width.\n",
+    "For more inforamtion about dataset visit http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ['CUDA_VISIBLE_DEVICES'] = '0'\n",
+    "\n",
+    "import numpy as np\n",
+    "import cv2\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_DIR = './data/CamVid/'\n",
+    "\n",
+    "# load repo with data if it is not exists\n",
+    "if not os.path.exists(DATA_DIR):\n",
+    "    print('Loading data...')\n",
+    "    os.system('git clone https://github.com/alexgkendall/SegNet-Tutorial ./data')\n",
+    "    print('Done!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train_dir = os.path.join(DATA_DIR, 'train')\n",
+    "y_train_dir = os.path.join(DATA_DIR, 'trainannot')\n",
+    "\n",
+    "x_valid_dir = os.path.join(DATA_DIR, 'val')\n",
+    "y_valid_dir = os.path.join(DATA_DIR, 'valannot')\n",
+    "\n",
+    "x_test_dir = os.path.join(DATA_DIR, 'test')\n",
+    "y_test_dir = os.path.join(DATA_DIR, 'testannot')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# helper function for data visualization\n",
+    "def visualize(**images):\n",
+    "    \"\"\"PLot images in one row.\"\"\"\n",
+    "    n = len(images)\n",
+    "    plt.figure(figsize=(16, 5))\n",
+    "    for i, (name, image) in enumerate(images.items()):\n",
+    "        plt.subplot(1, n, i + 1)\n",
+    "        plt.xticks([])\n",
+    "        plt.yticks([])\n",
+    "        plt.title(' '.join(name.split('_')).title())\n",
+    "        plt.imshow(image)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dataloader\n",
+    "\n",
+    "Writing helper class for data extraction, tranformation and preprocessing  \n",
+    "https://pytorch.org/docs/stable/data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch.utils.data import DataLoader\n",
+    "from torch.utils.data import Dataset as BaseDataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Dataset(BaseDataset):\n",
+    "    \"\"\"CamVid Dataset. Read images, apply augmentation and preprocessing transformations.\n",
+    "    \n",
+    "    Args:\n",
+    "        images_dir (str): path to images folder\n",
+    "        masks_dir (str): path to segmentation masks folder\n",
+    "        class_values (list): values of classes to extract from segmentation mask\n",
+    "        augmentation (albumentations.Compose): data transfromation pipeline \n",
+    "            (e.g. flip, scale, etc.)\n",
+    "        preprocessing (albumentations.Compose): data preprocessing \n",
+    "            (e.g. noralization, shape manipulation, etc.)\n",
+    "    \n",
+    "    \"\"\"\n",
+    "    \n",
+    "    CLASSES = ['sky', 'building', 'pole', 'road', 'pavement', \n",
+    "               'tree', 'signsymbol', 'fence', 'car', \n",
+    "               'pedestrian', 'bicyclist', 'unlabelled']\n",
+    "    \n",
+    "    def __init__(\n",
+    "            self, \n",
+    "            images_dir, \n",
+    "            masks_dir, \n",
+    "            classes=None, \n",
+    "            augmentation=None, \n",
+    "            preprocessing=None,\n",
+    "    ):\n",
+    "        self.ids = os.listdir(images_dir)\n",
+    "        self.images_fps = [os.path.join(images_dir, image_id) for image_id in self.ids]\n",
+    "        self.masks_fps = [os.path.join(masks_dir, image_id) for image_id in self.ids]\n",
+    "        \n",
+    "        # convert str names to class values on masks\n",
+    "        self.class_values = [self.CLASSES.index(cls.lower()) for cls in classes]\n",
+    "        \n",
+    "        self.augmentation = augmentation\n",
+    "        self.preprocessing = preprocessing\n",
+    "    \n",
+    "    def __getitem__(self, i):\n",
+    "        \n",
+    "        # read data\n",
+    "        image = cv2.imread(self.images_fps[i])\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "        mask = cv2.imread(self.masks_fps[i], 0)\n",
+    "        \n",
+    "        # extract certain classes from mask (e.g. cars)\n",
+    "        masks = [(mask == v) for v in self.class_values]\n",
+    "        mask = np.stack(masks, axis=-1).astype('float')\n",
+    "        \n",
+    "        # apply augmentations\n",
+    "        if self.augmentation:\n",
+    "            sample = self.augmentation(image=image, mask=mask)\n",
+    "            image, mask = sample['image'], sample['mask']\n",
+    "        \n",
+    "        # apply preprocessing\n",
+    "        if self.preprocessing:\n",
+    "            sample = self.preprocessing(image=image, mask=mask)\n",
+    "            image, mask = sample['image'], sample['mask']\n",
+    "            \n",
+    "        return image, mask\n",
+    "        \n",
+    "    def __len__(self):\n",
+    "        return len(self.ids)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Lets look at data we have\n",
+    "\n",
+    "dataset = Dataset(x_train_dir, y_train_dir, classes=['car'])\n",
+    "\n",
+    "image, mask = dataset[4] # get some sample\n",
+    "visualize(\n",
+    "    image=image, \n",
+    "    cars_mask=mask.squeeze(),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Augmentations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Data augmentation is a powerful technique to increase the amount of your data and prevent model overfitting.  \n",
+    "If you not familiar with such trick read some of these articles:\n",
+    " - [The Effectiveness of Data Augmentation in Image Classification using Deep\n",
+    "Learning](http://cs231n.stanford.edu/reports/2017/pdfs/300.pdf)\n",
+    " - [Data Augmentation | How to use Deep Learning when you have Limited Data](https://medium.com/nanonets/how-to-use-deep-learning-when-you-have-limited-data-part-2-data-augmentation-c26971dc8ced)\n",
+    " - [Data Augmentation Experimentation](https://towardsdatascience.com/data-augmentation-experimentation-3e274504f04b)\n",
+    "\n",
+    "Since our dataset is very small we will apply a large number of different augmentations:\n",
+    " - horizontal flip\n",
+    " - affine transforms\n",
+    " - perspective transforms\n",
+    " - brightness/contrast/colors manipulations\n",
+    " - image bluring and sharpening\n",
+    " - gaussian noise\n",
+    " - random crops\n",
+    "\n",
+    "All this transforms can be easily applied with [**Albumentations**](https://github.com/albu/albumentations/) - fast augmentation library.\n",
+    "For detailed explanation of image transformations you can look at [kaggle salt segmentation exmaple](https://github.com/albu/albumentations/blob/master/notebooks/example_kaggle_salt.ipynb) provided by [**Albumentations**](https://github.com/albu/albumentations/) authors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import albumentations as albu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_training_augmentation():\n",
+    "    train_transform = [\n",
+    "\n",
+    "        albu.HorizontalFlip(p=0.5),\n",
+    "\n",
+    "        albu.ShiftScaleRotate(scale_limit=0.5, rotate_limit=0, shift_limit=0.1, p=1, border_mode=0),\n",
+    "\n",
+    "        albu.PadIfNeeded(min_height=320, min_width=320, always_apply=True, border_mode=0),\n",
+    "        albu.RandomCrop(height=320, width=320, always_apply=True),\n",
+    "\n",
+    "        albu.IAAAdditiveGaussianNoise(p=0.2),\n",
+    "        albu.IAAPerspective(p=0.5),\n",
+    "\n",
+    "        albu.OneOf(\n",
+    "            [\n",
+    "                albu.CLAHE(p=1),\n",
+    "                albu.RandomBrightness(p=1),\n",
+    "                albu.RandomGamma(p=1),\n",
+    "            ],\n",
+    "            p=0.9,\n",
+    "        ),\n",
+    "\n",
+    "        albu.OneOf(\n",
+    "            [\n",
+    "                albu.IAASharpen(p=1),\n",
+    "                albu.Blur(blur_limit=3, p=1),\n",
+    "                albu.MotionBlur(blur_limit=3, p=1),\n",
+    "            ],\n",
+    "            p=0.9,\n",
+    "        ),\n",
+    "\n",
+    "        albu.OneOf(\n",
+    "            [\n",
+    "                albu.RandomContrast(p=1),\n",
+    "                albu.HueSaturationValue(p=1),\n",
+    "            ],\n",
+    "            p=0.9,\n",
+    "        ),\n",
+    "    ]\n",
+    "    return albu.Compose(train_transform)\n",
+    "\n",
+    "\n",
+    "def get_validation_augmentation():\n",
+    "    \"\"\"Add paddings to make image shape divisible by 32\"\"\"\n",
+    "    test_transform = [\n",
+    "        albu.PadIfNeeded(384, 480)\n",
+    "    ]\n",
+    "    return albu.Compose(test_transform)\n",
+    "\n",
+    "\n",
+    "def to_tensor(x, **kwargs):\n",
+    "    return x.transpose(2, 0, 1).astype('float32')\n",
+    "\n",
+    "\n",
+    "def get_preprocessing(preprocessing_fn):\n",
+    "    \"\"\"Construct preprocessing transform\n",
+    "    \n",
+    "    Args:\n",
+    "        preprocessing_fn (callbale): data normalization function \n",
+    "            (can be specific for each pretrained neural network)\n",
+    "    Return:\n",
+    "        transform: albumentations.Compose\n",
+    "    \n",
+    "    \"\"\"\n",
+    "    \n",
+    "    _transform = [\n",
+    "        albu.Lambda(image=preprocessing_fn),\n",
+    "        albu.Lambda(image=to_tensor, mask=to_tensor),\n",
+    "    ]\n",
+    "    return albu.Compose(_transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#### Visualize resulted augmented images and masks\n",
+    "\n",
+    "augmented_dataset = Dataset(\n",
+    "    x_train_dir, \n",
+    "    y_train_dir, \n",
+    "    augmentation=get_training_augmentation(), \n",
+    "    classes=['car'],\n",
+    ")\n",
+    "\n",
+    "# same image with different random transforms\n",
+    "for i in range(3):\n",
+    "    image, mask = augmented_dataset[1]\n",
+    "    visualize(image=image, mask=mask.squeeze(-1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create model and train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import numpy as np\n",
+    "import segmentation_models_pytorch as smp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ENCODER = 'se_resnext50_32x4d'\n",
+    "ENCODER_WEIGHTS = 'imagenet'\n",
+    "CLASSES = ['car']\n",
+    "ACTIVATION = 'sigmoid' # could be None for logits or 'softmax2d' for multicalss segmentation\n",
+    "DEVICE = 'cuda'\n",
+    "\n",
+    "# create segmentation model with pretrained encoder\n",
+    "model = smp.FPN(\n",
+    "    encoder_name=ENCODER, \n",
+    "    encoder_weights=ENCODER_WEIGHTS, \n",
+    "    classes=len(CLASSES), \n",
+    "    activation=ACTIVATION,\n",
+    ")\n",
+    "\n",
+    "preprocessing_fn = smp.encoders.get_preprocessing_fn(ENCODER, ENCODER_WEIGHTS)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.6/site-packages/albumentations/augmentations/transforms.py:2471: UserWarning: Using lambda is incompatible with multiprocessing. Consider using regular functions or partial().\n",
+      "  warnings.warn('Using lambda is incompatible with multiprocessing. '\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_dataset = Dataset(\n",
+    "    x_train_dir, \n",
+    "    y_train_dir, \n",
+    "    augmentation=get_training_augmentation(), \n",
+    "    preprocessing=get_preprocessing(preprocessing_fn),\n",
+    "    classes=CLASSES,\n",
+    ")\n",
+    "\n",
+    "valid_dataset = Dataset(\n",
+    "    x_valid_dir, \n",
+    "    y_valid_dir, \n",
+    "    augmentation=get_validation_augmentation(), \n",
+    "    preprocessing=get_preprocessing(preprocessing_fn),\n",
+    "    classes=CLASSES,\n",
+    ")\n",
+    "\n",
+    "train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True, num_workers=12)\n",
+    "valid_loader = DataLoader(valid_dataset, batch_size=1, shuffle=False, num_workers=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dice/F1 score - https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient\n",
+    "# IoU/Jaccard score - https://en.wikipedia.org/wiki/Jaccard_index\n",
+    "\n",
+    "loss = smp.utils.losses.DiceLoss()\n",
+    "metrics = [\n",
+    "    smp.utils.metrics.IoU(threshold=0.5),\n",
+    "]\n",
+    "\n",
+    "optimizer = torch.optim.Adam([ \n",
+    "    dict(params=model.parameters(), lr=0.0001),\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create epoch runners \n",
+    "# it is a simple loop of iterating over dataloader`s samples\n",
+    "train_epoch = smp.utils.train.TrainEpoch(\n",
+    "    model, \n",
+    "    loss=loss, \n",
+    "    metrics=metrics, \n",
+    "    optimizer=optimizer,\n",
+    "    device=DEVICE,\n",
+    "    verbose=True,\n",
+    ")\n",
+    "\n",
+    "valid_epoch = smp.utils.train.ValidEpoch(\n",
+    "    model, \n",
+    "    loss=loss, \n",
+    "    metrics=metrics, \n",
+    "    device=DEVICE,\n",
+    "    verbose=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Epoch: 0\n",
+      "train: 100%|██████████| 46/46 [00:12<00:00,  3.59it/s, dice_loss - 0.2156, iou_score - 0.6585]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.53it/s, dice_loss - 0.3525, iou_score - 0.5599]\n",
+      "Model saved!\n",
+      "\n",
+      "Epoch: 1\n",
+      "train: 100%|██████████| 46/46 [00:12<00:00,  3.58it/s, dice_loss - 0.1612, iou_score - 0.7336]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.32it/s, dice_loss - 0.3014, iou_score - 0.6105]\n",
+      "Model saved!\n",
+      "\n",
+      "Epoch: 2\n",
+      "train: 100%|██████████| 46/46 [00:12<00:00,  3.57it/s, dice_loss - 0.1543, iou_score - 0.7438]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.59it/s, dice_loss - 0.2838, iou_score - 0.6298]\n",
+      "Model saved!\n",
+      "\n",
+      "Epoch: 3\n",
+      "train: 100%|██████████| 46/46 [00:12<00:00,  3.56it/s, dice_loss - 0.1412, iou_score - 0.7622]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.67it/s, dice_loss - 0.2725, iou_score - 0.638]\n",
+      "Model saved!\n",
+      "\n",
+      "Epoch: 4\n",
+      "train: 100%|██████████| 46/46 [00:12<00:00,  3.57it/s, dice_loss - 0.1284, iou_score - 0.7806]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.53it/s, dice_loss - 0.2358, iou_score - 0.6802]\n",
+      "Model saved!\n",
+      "\n",
+      "Epoch: 5\n",
+      "train: 100%|██████████| 46/46 [00:13<00:00,  3.54it/s, dice_loss - 0.1097, iou_score - 0.8088] \n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.37it/s, dice_loss - 0.2155, iou_score - 0.7026]\n",
+      "Model saved!\n",
+      "\n",
+      "Epoch: 6\n",
+      "train: 100%|██████████| 46/46 [00:12<00:00,  3.55it/s, dice_loss - 0.1082, iou_score - 0.8117]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.35it/s, dice_loss - 0.2451, iou_score - 0.6708]\n",
+      "\n",
+      "Epoch: 7\n",
+      "train: 100%|██████████| 46/46 [00:12<00:00,  3.55it/s, dice_loss - 0.1103, iou_score - 0.8083] \n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.74it/s, dice_loss - 0.2657, iou_score - 0.6435]\n",
+      "\n",
+      "Epoch: 8\n",
+      "train: 100%|██████████| 46/46 [00:13<00:00,  3.54it/s, dice_loss - 0.1058, iou_score - 0.8146]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.16it/s, dice_loss - 0.2129, iou_score - 0.7055]\n",
+      "Model saved!\n",
+      "\n",
+      "Epoch: 9\n",
+      "train: 100%|██████████| 46/46 [00:12<00:00,  3.54it/s, dice_loss - 0.1023, iou_score - 0.8205] \n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 31.92it/s, dice_loss - 0.2011, iou_score - 0.7227]\n",
+      "Model saved!\n",
+      "\n",
+      "Epoch: 10\n",
+      "train: 100%|██████████| 46/46 [00:13<00:00,  3.53it/s, dice_loss - 0.0997, iou_score - 0.8259] \n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.25it/s, dice_loss - 0.2066, iou_score - 0.7166]\n",
+      "\n",
+      "Epoch: 11\n",
+      "train: 100%|██████████| 46/46 [00:12<00:00,  3.54it/s, dice_loss - 0.09359, iou_score - 0.8346]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.05it/s, dice_loss - 0.2284, iou_score - 0.6891]\n",
+      "\n",
+      "Epoch: 12\n",
+      "train: 100%|██████████| 46/46 [00:12<00:00,  3.54it/s, dice_loss - 0.09215, iou_score - 0.8371]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.74it/s, dice_loss - 0.1978, iou_score - 0.7239]\n",
+      "Model saved!\n",
+      "\n",
+      "Epoch: 13\n",
+      "train: 100%|██████████| 46/46 [00:13<00:00,  3.54it/s, dice_loss - 0.08742, iou_score - 0.8441]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 31.95it/s, dice_loss - 0.2065, iou_score - 0.7173]\n",
+      "\n",
+      "Epoch: 14\n",
+      "train: 100%|██████████| 46/46 [00:13<00:00,  3.54it/s, dice_loss - 0.08557, iou_score - 0.848] \n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.24it/s, dice_loss - 0.207, iou_score - 0.7134]\n",
+      "\n",
+      "Epoch: 15\n",
+      "train: 100%|██████████| 46/46 [00:13<00:00,  3.53it/s, dice_loss - 0.09291, iou_score - 0.8362]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.03it/s, dice_loss - 0.1933, iou_score - 0.7295]\n",
+      "Model saved!\n",
+      "\n",
+      "Epoch: 16\n",
+      "train: 100%|██████████| 46/46 [00:13<00:00,  3.53it/s, dice_loss - 0.08356, iou_score - 0.8504]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.03it/s, dice_loss - 0.1791, iou_score - 0.7448]\n",
+      "Model saved!\n",
+      "\n",
+      "Epoch: 17\n",
+      "train: 100%|██████████| 46/46 [00:13<00:00,  3.52it/s, dice_loss - 0.08644, iou_score - 0.8461]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.11it/s, dice_loss - 0.2116, iou_score - 0.7079]\n",
+      "\n",
+      "Epoch: 18\n",
+      "train: 100%|██████████| 46/46 [00:12<00:00,  3.54it/s, dice_loss - 0.08984, iou_score - 0.8406]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.04it/s, dice_loss - 0.1956, iou_score - 0.7272]\n",
+      "\n",
+      "Epoch: 19\n",
+      "train: 100%|██████████| 46/46 [00:13<00:00,  3.52it/s, dice_loss - 0.09075, iou_score - 0.8389]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.26it/s, dice_loss - 0.2099, iou_score - 0.7094]\n",
+      "\n",
+      "Epoch: 20\n",
+      "train: 100%|██████████| 46/46 [00:12<00:00,  3.55it/s, dice_loss - 0.0798, iou_score - 0.8568] \n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.59it/s, dice_loss - 0.2008, iou_score - 0.7211]\n",
+      "\n",
+      "Epoch: 21\n",
+      "train: 100%|██████████| 46/46 [00:12<00:00,  3.54it/s, dice_loss - 0.07976, iou_score - 0.8568]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.48it/s, dice_loss - 0.1951, iou_score - 0.7213]\n",
+      "\n",
+      "Epoch: 22\n",
+      "train: 100%|██████████| 46/46 [00:13<00:00,  3.52it/s, dice_loss - 0.07822, iou_score - 0.8597]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.10it/s, dice_loss - 0.1983, iou_score - 0.7249]\n",
+      "\n",
+      "Epoch: 23\n",
+      "train: 100%|██████████| 46/46 [00:13<00:00,  3.52it/s, dice_loss - 0.07319, iou_score - 0.8675]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.09it/s, dice_loss - 0.1936, iou_score - 0.7318]\n",
+      "\n",
+      "Epoch: 24\n",
+      "train: 100%|██████████| 46/46 [00:13<00:00,  3.53it/s, dice_loss - 0.08332, iou_score - 0.8509]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.35it/s, dice_loss - 0.1957, iou_score - 0.7294]\n",
+      "\n",
+      "Epoch: 25\n",
+      "train: 100%|██████████| 46/46 [00:12<00:00,  3.54it/s, dice_loss - 0.07854, iou_score - 0.8585]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.21it/s, dice_loss - 0.2031, iou_score - 0.7198]\n",
+      "Decrease decoder learning rate to 1e-5!\n",
+      "\n",
+      "Epoch: 26\n",
+      "train: 100%|██████████| 46/46 [00:12<00:00,  3.54it/s, dice_loss - 0.07578, iou_score - 0.8633]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.50it/s, dice_loss - 0.1789, iou_score - 0.7438]\n",
+      "\n",
+      "Epoch: 27\n",
+      "train: 100%|██████████| 46/46 [00:13<00:00,  3.53it/s, dice_loss - 0.07451, iou_score - 0.8651]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.05it/s, dice_loss - 0.1872, iou_score - 0.7372]\n",
+      "\n",
+      "Epoch: 28\n",
+      "train: 100%|██████████| 46/46 [00:12<00:00,  3.54it/s, dice_loss - 0.07716, iou_score - 0.861] \n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.01it/s, dice_loss - 0.1773, iou_score - 0.7485]\n",
+      "Model saved!\n",
+      "\n",
+      "Epoch: 29\n",
+      "train: 100%|██████████| 46/46 [00:13<00:00,  3.53it/s, dice_loss - 0.07528, iou_score - 0.8642]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 31.84it/s, dice_loss - 0.181, iou_score - 0.7444]\n",
+      "\n",
+      "Epoch: 30\n",
+      "train: 100%|██████████| 46/46 [00:12<00:00,  3.54it/s, dice_loss - 0.07441, iou_score - 0.8659]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.30it/s, dice_loss - 0.1787, iou_score - 0.746]\n",
+      "\n",
+      "Epoch: 31\n",
+      "train: 100%|██████████| 46/46 [00:13<00:00,  3.52it/s, dice_loss - 0.07471, iou_score - 0.8652]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 31.96it/s, dice_loss - 0.1729, iou_score - 0.752]\n",
+      "Model saved!\n",
+      "\n",
+      "Epoch: 32\n",
+      "train: 100%|██████████| 46/46 [00:12<00:00,  3.54it/s, dice_loss - 0.06953, iou_score - 0.8739]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.16it/s, dice_loss - 0.1817, iou_score - 0.7436]\n",
+      "\n",
+      "Epoch: 33\n",
+      "train: 100%|██████████| 46/46 [00:13<00:00,  3.53it/s, dice_loss - 0.06954, iou_score - 0.8738]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.00it/s, dice_loss - 0.1897, iou_score - 0.7321]\n",
+      "\n",
+      "Epoch: 34\n",
+      "train: 100%|██████████| 46/46 [00:13<00:00,  3.54it/s, dice_loss - 0.06618, iou_score - 0.8795]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.34it/s, dice_loss - 0.1842, iou_score - 0.7405]\n",
+      "\n",
+      "Epoch: 35\n",
+      "train: 100%|██████████| 46/46 [00:12<00:00,  3.55it/s, dice_loss - 0.0743, iou_score - 0.8659] \n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.02it/s, dice_loss - 0.1793, iou_score - 0.745]\n",
+      "\n",
+      "Epoch: 36\n",
+      "train: 100%|██████████| 46/46 [00:13<00:00,  3.53it/s, dice_loss - 0.07089, iou_score - 0.8715]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.07it/s, dice_loss - 0.1818, iou_score - 0.7426]\n",
+      "\n",
+      "Epoch: 37\n",
+      "train: 100%|██████████| 46/46 [00:13<00:00,  3.54it/s, dice_loss - 0.07055, iou_score - 0.8723]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.12it/s, dice_loss - 0.1778, iou_score - 0.7462]\n",
+      "\n",
+      "Epoch: 38\n",
+      "train: 100%|██████████| 46/46 [00:13<00:00,  3.52it/s, dice_loss - 0.06931, iou_score - 0.8741]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 31.99it/s, dice_loss - 0.1855, iou_score - 0.7363]\n",
+      "\n",
+      "Epoch: 39\n",
+      "train: 100%|██████████| 46/46 [00:12<00:00,  3.54it/s, dice_loss - 0.06552, iou_score - 0.8806]\n",
+      "valid: 100%|██████████| 101/101 [00:03<00:00, 32.08it/s, dice_loss - 0.1788, iou_score - 0.7422]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# train model for 40 epochs\n",
+    "\n",
+    "max_score = 0\n",
+    "\n",
+    "for i in range(0, 40):\n",
+    "    \n",
+    "    print('\\nEpoch: {}'.format(i))\n",
+    "    train_logs = train_epoch.run(train_loader)\n",
+    "    valid_logs = valid_epoch.run(valid_loader)\n",
+    "    \n",
+    "    # do something (save model, change lr, etc.)\n",
+    "    if max_score < valid_logs['iou_score']:\n",
+    "        max_score = valid_logs['iou_score']\n",
+    "        torch.save(model, './best_model.pth')\n",
+    "        print('Model saved!')\n",
+    "        \n",
+    "    if i == 25:\n",
+    "        optimizer.param_groups[0]['lr'] = 1e-5\n",
+    "        print('Decrease decoder learning rate to 1e-5!')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test best saved model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load best saved checkpoint\n",
+    "best_model = torch.load('./best_model.pth')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create test dataset\n",
+    "test_dataset = Dataset(\n",
+    "    x_test_dir, \n",
+    "    y_test_dir, \n",
+    "    augmentation=get_validation_augmentation(), \n",
+    "    preprocessing=get_preprocessing(preprocessing_fn),\n",
+    "    classes=CLASSES,\n",
+    ")\n",
+    "\n",
+    "test_dataloader = DataLoader(test_dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "valid: 100%|██████████| 233/233 [00:08<00:00, 27.19it/s, dice_loss - 0.1979, iou_score - 0.7498]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# evaluate model on test set\n",
+    "test_epoch = smp.utils.train.ValidEpoch(\n",
+    "    model=best_model,\n",
+    "    loss=loss,\n",
+    "    metrics=metrics,\n",
+    "    device=DEVICE,\n",
+    ")\n",
+    "\n",
+    "logs = test_epoch.run(test_dataloader)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualize predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# test dataset without transformations for image visualization\n",
+    "test_dataset_vis = Dataset(\n",
+    "    x_test_dir, y_test_dir, \n",
+    "    classes=CLASSES,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i in range(5):\n",
+    "    n = np.random.choice(len(test_dataset))\n",
+    "    \n",
+    "    image_vis = test_dataset_vis[n][0].astype('uint8')\n",
+    "    image, gt_mask = test_dataset[n]\n",
+    "    \n",
+    "    gt_mask = gt_mask.squeeze()\n",
+    "    \n",
+    "    x_tensor = torch.from_numpy(image).to(DEVICE).unsqueeze(0)\n",
+    "    pr_mask = best_model.predict(x_tensor)\n",
+    "    pr_mask = (pr_mask.squeeze().cpu().numpy().round())\n",
+    "        \n",
+    "    visualize(\n",
+    "        image=image_vis, \n",
+    "        ground_truth_mask=gt_mask, \n",
+    "        predicted_mask=pr_mask\n",
+    "    )"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/segmentation_models_pytorch/misc/generate_table.py b/segmentation_models_pytorch/misc/generate_table.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb54861b4896d6034796684ae35217cf277cb6eb
--- /dev/null
+++ b/segmentation_models_pytorch/misc/generate_table.py
@@ -0,0 +1,33 @@
+import segmentation_models_pytorch as smp
+
+encoders = smp.encoders.encoders
+
+
+WIDTH = 32
+COLUMNS = [
+    "Encoder",
+    "Weights",
+    "Params, M",
+]
+
+def wrap_row(r):
+    return "|{}|".format(r)
+
+header = "|".join([column.ljust(WIDTH, ' ') for column in COLUMNS])
+separator = "|".join(["-" * WIDTH] + [":" + "-" * (WIDTH - 2) + ":"] * (len(COLUMNS) - 1))
+
+print(wrap_row(header))
+print(wrap_row(separator))
+
+for encoder_name, encoder in encoders.items():
+    weights = "<br>".join(encoder["pretrained_settings"].keys())
+    encoder_name = encoder_name.ljust(WIDTH, " ")
+    weights = weights.ljust(WIDTH, " ")
+
+    model = encoder["encoder"](**encoder["params"], depth=5)
+    params = sum(p.numel() for p in model.parameters())
+    params = str(params // 1000000) + "M"
+    params = params.ljust(WIDTH, " ")
+
+    row = "|".join([encoder_name, weights, params])
+    print(wrap_row(row))
diff --git a/segmentation_models_pytorch/requirements.txt b/segmentation_models_pytorch/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a88a7a87af301a561b2469a0e39240b2a2e481f8
--- /dev/null
+++ b/segmentation_models_pytorch/requirements.txt
@@ -0,0 +1,4 @@
+torchvision>=0.3.0
+pretrainedmodels==0.7.4
+efficientnet-pytorch==0.6.3
+timm==0.3.2
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/__init__.py b/segmentation_models_pytorch/segmentation_models_pytorch/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f3ef11f3ab5c2711111661181fab46331647f75
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/__init__.py
@@ -0,0 +1,49 @@
+from .unet import Unet
+from .unetplusplus import UnetPlusPlus
+from .manet import MAnet
+from .linknet import Linknet
+from .fpn import FPN
+from .pspnet import PSPNet
+from .deeplabv3 import DeepLabV3, DeepLabV3Plus
+from .pan import PAN
+from .resunet import ResUnet
+from .resunetplusplus import ResUnetPlusPlus
+from .efficientunetplusplus import EfficientUnetPlusPlus
+
+from . import encoders
+from . import utils
+from . import losses
+
+from .__version__ import __version__
+
+from typing import Optional
+import torch
+
+
+def create_model(
+    arch: str,
+    encoder_name: str = "resnet34",
+    encoder_weights: Optional[str] = "imagenet",
+    in_channels: int = 3,
+    classes: int = 1,
+    **kwargs,
+) -> torch.nn.Module:
+    """Models wrapper. Allows to create any model just with parametes
+
+    """
+
+    archs = [Unet, UnetPlusPlus, MAnet, Linknet, FPN, PSPNet, DeepLabV3, DeepLabV3Plus, PAN, ResUnet, EfficientUnetPlusPlus, ResUnetPlusPlus]
+    archs_dict = {a.__name__.lower(): a for a in archs}
+    try:
+        model_class = archs_dict[arch.lower()]
+    except KeyError:
+        raise KeyError("Wrong architecture type `{}`. Avalibale options are: {}".format(
+            arch, list(archs_dict.keys()),
+        ))
+    return model_class(
+        encoder_name=encoder_name,
+        encoder_weights=encoder_weights,
+        in_channels=in_channels,
+        classes=classes,
+        **kwargs,
+    )
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/__version__.py b/segmentation_models_pytorch/segmentation_models_pytorch/__version__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d91e7fbb02a7966af8be98f9fa552013977063b
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/__version__.py
@@ -0,0 +1,3 @@
+VERSION = (0, 1, 3)
+
+__version__ = '.'.join(map(str, VERSION))
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/base/__init__.py b/segmentation_models_pytorch/segmentation_models_pytorch/base/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5db953060c2309a8d37d760478ae6b4c88b3b47
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/base/__init__.py
@@ -0,0 +1,12 @@
+from .model import SegmentationModel
+
+from .modules import (
+    PreActivatedConv2dReLU,
+    Conv2dReLU,
+    Attention,
+)
+
+from .heads import (
+    SegmentationHead,
+    ClassificationHead,
+)
\ No newline at end of file
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/base/heads.py b/segmentation_models_pytorch/segmentation_models_pytorch/base/heads.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa13f8d0ca3877111714e303e3be5478f1ad2a14
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/base/heads.py
@@ -0,0 +1,24 @@
+import torch.nn as nn
+from .modules import Flatten, Activation
+
+
+class SegmentationHead(nn.Sequential):
+
+    def __init__(self, in_channels, out_channels, kernel_size=3, activation=None, upsampling=1):
+        conv2d = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, padding=kernel_size // 2)
+        upsampling = nn.UpsamplingBilinear2d(scale_factor=upsampling) if upsampling > 1 else nn.Identity()
+        activation = Activation(activation)
+        super().__init__(conv2d, upsampling, activation)
+
+
+class ClassificationHead(nn.Sequential):
+
+    def __init__(self, in_channels, classes, pooling="avg", dropout=0.2, activation=None):
+        if pooling not in ("max", "avg"):
+            raise ValueError("Pooling should be one of ('max', 'avg'), got {}.".format(pooling))
+        pool = nn.AdaptiveAvgPool2d(1) if pooling == 'avg' else nn.AdaptiveMaxPool2d(1)
+        flatten = Flatten()
+        dropout = nn.Dropout(p=dropout, inplace=True) if dropout else nn.Identity()
+        linear = nn.Linear(in_channels, classes, bias=True)
+        activation = Activation(activation)
+        super().__init__(pool, flatten, dropout, linear, activation)
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/base/initialization.py b/segmentation_models_pytorch/segmentation_models_pytorch/base/initialization.py
new file mode 100644
index 0000000000000000000000000000000000000000..9622130204a0172d43a5f32f4ade065e100f746e
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/base/initialization.py
@@ -0,0 +1,27 @@
+import torch.nn as nn
+
+
+def initialize_decoder(module):
+    for m in module.modules():
+
+        if isinstance(m, nn.Conv2d):
+            nn.init.kaiming_uniform_(m.weight, mode="fan_in", nonlinearity="relu")
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+
+        elif isinstance(m, nn.BatchNorm2d):
+            nn.init.constant_(m.weight, 1)
+            nn.init.constant_(m.bias, 0)
+
+        elif isinstance(m, nn.Linear):
+            nn.init.xavier_uniform_(m.weight)
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+
+
+def initialize_head(module):
+    for m in module.modules():
+        if isinstance(m, (nn.Linear, nn.Conv2d)):
+            nn.init.xavier_uniform_(m.weight)
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/base/model.py b/segmentation_models_pytorch/segmentation_models_pytorch/base/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5ffad15a69e1dfcafc92f47c79bff28d6dfd474
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/base/model.py
@@ -0,0 +1,42 @@
+import torch
+from . import initialization as init
+
+
+class SegmentationModel(torch.nn.Module):
+
+    def initialize(self):
+        init.initialize_decoder(self.decoder)
+        init.initialize_head(self.segmentation_head)
+        if self.classification_head is not None:
+            init.initialize_head(self.classification_head)
+
+    def forward(self, x):
+        """Sequentially pass `x` trough model`s encoder, decoder and heads"""
+        features = self.encoder(x)
+        decoder_output = self.decoder(*features)
+
+        masks = self.segmentation_head(decoder_output)
+
+        if self.classification_head is not None:
+            labels = self.classification_head(features[-1])
+            return masks, labels
+
+        return masks
+
+    def predict(self, x):
+        """Inference method. Switch model to `eval` mode, call `.forward(x)` with `torch.no_grad()`
+
+        Args:
+            x: 4D torch tensor with shape (batch_size, channels, height, width)
+
+        Return:
+            prediction: 4D torch tensor with shape (batch_size, classes, height, width)
+
+        """
+        if self.training:
+            self.eval()
+
+        with torch.no_grad():
+            x = self.forward(x)
+
+        return x
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/base/modules.py b/segmentation_models_pytorch/segmentation_models_pytorch/base/modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..e595434f87b49c10422469fa26fc025dce3c53e7
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/base/modules.py
@@ -0,0 +1,206 @@
+import torch
+import torch.nn as nn
+
+try:
+    from inplace_abn import InPlaceABN
+except ImportError:
+    InPlaceABN = None
+
+class PreActivatedConv2dReLU(nn.Sequential):
+    """
+    Pre-activated 2D convolution, as proposed in https://arxiv.org/pdf/1603.05027.pdf. Feature maps are processed by a normalization layer, 
+    followed by a ReLU activation and a 3x3 convolution.
+    normalization
+    """
+    def __init__(
+            self,
+            in_channels, 
+            out_channels,
+            kernel_size,
+            padding=0,
+            stride=1,
+            use_batchnorm=True,
+    ):
+
+        if use_batchnorm == "inplace" and InPlaceABN is None:
+            raise RuntimeError(
+                "In order to use `use_batchnorm='inplace'` inplace_abn package must be installed. "
+                + "To install see: https://github.com/mapillary/inplace_abn"
+            )
+        if use_batchnorm == "inplace":
+            bn = InPlaceABN(out_channels, activation="leaky_relu", activation_param=0.0)
+            relu = nn.Identity()
+        elif use_batchnorm and use_batchnorm != "inplace":
+            bn = nn.BatchNorm2d(out_channels)
+        else:
+            bn = nn.Identity()
+
+        relu = nn.ReLU(inplace=True)
+
+        conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            bias=not (use_batchnorm),
+        )
+        super(PreActivatedConv2dReLU, self).__init__(conv, bn, relu)
+
+class Conv2dReLU(nn.Sequential):
+    """
+    Block composed of a 3x3 convolution followed by a normalization layer and ReLU activation.
+    """
+    def __init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            padding=0,
+            stride=1,
+            use_batchnorm=True,
+    ):
+
+        if use_batchnorm == "inplace" and InPlaceABN is None:
+            raise RuntimeError(
+                "In order to use `use_batchnorm='inplace'` inplace_abn package must be installed. "
+                + "To install see: https://github.com/mapillary/inplace_abn"
+            )
+
+        conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            bias=not (use_batchnorm),
+        )
+        relu = nn.ReLU(inplace=True)
+
+        if use_batchnorm == "inplace":
+            bn = InPlaceABN(out_channels, activation="leaky_relu", activation_param=0.0)
+            relu = nn.Identity()
+        elif use_batchnorm and use_batchnorm != "inplace":
+            bn = nn.BatchNorm2d(out_channels)
+        else:
+            bn = nn.Identity()
+
+        super(Conv2dReLU, self).__init__(conv, bn, relu)
+
+class DepthWiseConv2d(nn.Conv2d):
+    "Depth-wise convolution operation"
+    def __init__(self, channels, kernel_size=3, stride=1):
+        super().__init__(channels, channels, kernel_size, stride=stride, padding=kernel_size//2, groups=channels)
+
+class PointWiseConv2d(nn.Conv2d):
+    "Point-wise (1x1) convolution operation"
+    def __init__(self, in_channels, out_channels):
+        super().__init__(in_channels, out_channels, kernel_size=1, stride=1)
+
+class SEModule(nn.Module):
+    """
+    Spatial squeeze & channel excitation attention module, as proposed in https://arxiv.org/abs/1709.01507.
+    """
+    def __init__(self, in_channels, reduction=16):
+        super().__init__()
+        self.cSE = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1),
+            nn.Conv2d(in_channels, in_channels // reduction, 1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels // reduction, in_channels, 1),
+            nn.Sigmoid(),
+        )
+
+    def forward(self, x):
+        return x * self.cSE(x)
+
+class sSEModule(nn.Module):
+    """
+    Channel squeeze & spatial excitation attention module, as proposed in https://arxiv.org/abs/1808.08127.
+    """
+    def __init__(self, in_channels):
+        super().__init__()
+        self.sSE = nn.Sequential(nn.Conv2d(in_channels, 1, 1), nn.Sigmoid())
+
+    def forward(self, x):
+        return x * self.sSE(x)
+
+class SCSEModule(nn.Module):
+    """
+    Concurrent spatial and channel squeeze & excitation attention module, as proposed in https://arxiv.org/pdf/1803.02579.pdf.
+    """
+    def __init__(self, in_channels, reduction=16):
+        super().__init__()
+        self.cSE = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1),
+            nn.Conv2d(in_channels, in_channels // reduction, 1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels // reduction, in_channels, 1),
+            nn.Sigmoid(),
+        )
+        self.sSE = nn.Sequential(nn.Conv2d(in_channels, 1, 1), nn.Sigmoid())
+
+    def forward(self, x):
+        return x * self.cSE(x) + x * self.sSE(x)
+
+class ArgMax(nn.Module):
+
+    def __init__(self, dim=None):
+        super().__init__()
+        self.dim = dim
+
+    def forward(self, x):
+        return torch.argmax(x, dim=self.dim)
+
+
+class Activation(nn.Module):
+
+    def __init__(self, name, **params):
+
+        super().__init__()
+
+        if name is None or name == 'identity':
+            self.activation = nn.Identity(**params)
+        elif name == 'sigmoid':
+            self.activation = nn.Sigmoid()
+        elif name == 'softmax2d':
+            self.activation = nn.Softmax(dim=1, **params)
+        elif name == 'softmax':
+            self.activation = nn.Softmax(**params)
+        elif name == 'logsoftmax':
+            self.activation = nn.LogSoftmax(**params)
+        elif name == 'tanh':
+            self.activation = nn.Tanh()
+        elif name == 'argmax':
+            self.activation = ArgMax(**params)
+        elif name == 'argmax2d':
+            self.activation = ArgMax(dim=1, **params)
+        elif callable(name):
+            self.activation = name(**params)
+        else:
+            raise ValueError('Activation should be callable/sigmoid/softmax/logsoftmax/tanh/None; got {}'.format(name))
+
+    def forward(self, x):
+        return self.activation(x)
+
+
+class Attention(nn.Module):
+
+    def __init__(self, name, **params):
+        super().__init__()
+
+        if name is None:
+            self.attention = nn.Identity(**params)
+        elif name == 'scse':
+            self.attention = SCSEModule(**params)
+        elif name == 'se':
+            self.attention = SEModule(**params)
+        else:
+            raise ValueError("Attention {} is not implemented".format(name))
+
+    def forward(self, x):
+        return self.attention(x)
+
+class Flatten(nn.Module):
+    def forward(self, x):
+        return x.view(x.shape[0], -1)
\ No newline at end of file
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/deeplabv3/__init__.py b/segmentation_models_pytorch/segmentation_models_pytorch/deeplabv3/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9acd50af70bad232b3459f1c2705fd7c041285d6
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/deeplabv3/__init__.py
@@ -0,0 +1 @@
+from .model import DeepLabV3, DeepLabV3Plus
\ No newline at end of file
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/deeplabv3/decoder.py b/segmentation_models_pytorch/segmentation_models_pytorch/deeplabv3/decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..c19a5eaa8f0c9faa912bb34104f8b876dfa6aede
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/deeplabv3/decoder.py
@@ -0,0 +1,220 @@
+"""
+BSD 3-Clause License
+
+Copyright (c) Soumith Chintala 2016,
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+"""
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+__all__ = ["DeepLabV3Decoder"]
+
+
+class DeepLabV3Decoder(nn.Sequential):
+    def __init__(self, in_channels, out_channels=256, atrous_rates=(12, 24, 36)):
+        super().__init__(
+            ASPP(in_channels, out_channels, atrous_rates),
+            nn.Conv2d(out_channels, out_channels, 3, padding=1, bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(),
+        )
+        self.out_channels = out_channels
+
+    def forward(self, *features):
+        return super().forward(features[-1])
+
+
+class DeepLabV3PlusDecoder(nn.Module):
+    def __init__(
+        self,
+        encoder_channels,
+        out_channels=256,
+        atrous_rates=(12, 24, 36),
+        output_stride=16,
+    ):
+        super().__init__()
+        if output_stride not in {8, 16}:
+            raise ValueError("Output stride should be 8 or 16, got {}.".format(output_stride))
+
+        self.out_channels = out_channels
+        self.output_stride = output_stride
+
+        self.aspp = nn.Sequential(
+            ASPP(encoder_channels[-1], out_channels, atrous_rates, separable=True),
+            SeparableConv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(),
+        )
+
+        scale_factor = 2 if output_stride == 8 else 4
+        self.up = nn.UpsamplingBilinear2d(scale_factor=scale_factor)
+
+        highres_in_channels = encoder_channels[-4]
+        highres_out_channels = 48   # proposed by authors of paper
+        self.block1 = nn.Sequential(
+            nn.Conv2d(highres_in_channels, highres_out_channels, kernel_size=1, bias=False),
+            nn.BatchNorm2d(highres_out_channels),
+            nn.ReLU(),
+        )
+        self.block2 = nn.Sequential(
+            SeparableConv2d(
+                highres_out_channels + out_channels,
+                out_channels,
+                kernel_size=3,
+                padding=1,
+                bias=False,
+            ),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(),
+        )
+
+    def forward(self, *features):
+        aspp_features = self.aspp(features[-1])
+        aspp_features = self.up(aspp_features)
+        high_res_features = self.block1(features[-4])
+        concat_features = torch.cat([aspp_features, high_res_features], dim=1)
+        fused_features = self.block2(concat_features)
+        return fused_features
+
+
+class ASPPConv(nn.Sequential):
+    def __init__(self, in_channels, out_channels, dilation):
+        super().__init__(
+            nn.Conv2d(
+                in_channels,
+                out_channels,
+                kernel_size=3,
+                padding=dilation,
+                dilation=dilation,
+                bias=False,
+            ),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(),
+        )
+
+
+class ASPPSeparableConv(nn.Sequential):
+    def __init__(self, in_channels, out_channels, dilation):
+        super().__init__(
+            SeparableConv2d(
+                in_channels,
+                out_channels,
+                kernel_size=3,
+                padding=dilation,
+                dilation=dilation,
+                bias=False,
+            ),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(),
+        )
+
+
+class ASPPPooling(nn.Sequential):
+    def __init__(self, in_channels, out_channels):
+        super().__init__(
+            nn.AdaptiveAvgPool2d(1),
+            nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(),
+        )
+
+    def forward(self, x):
+        size = x.shape[-2:]
+        for mod in self:
+            x = mod(x)
+        return F.interpolate(x, size=size, mode='bilinear', align_corners=False)
+
+
+class ASPP(nn.Module):
+    def __init__(self, in_channels, out_channels, atrous_rates, separable=False):
+        super(ASPP, self).__init__()
+        modules = []
+        modules.append(
+            nn.Sequential(
+                nn.Conv2d(in_channels, out_channels, 1, bias=False),
+                nn.BatchNorm2d(out_channels),
+                nn.ReLU(),
+            )
+        )
+
+        rate1, rate2, rate3 = tuple(atrous_rates)
+        ASPPConvModule = ASPPConv if not separable else ASPPSeparableConv
+
+        modules.append(ASPPConvModule(in_channels, out_channels, rate1))
+        modules.append(ASPPConvModule(in_channels, out_channels, rate2))
+        modules.append(ASPPConvModule(in_channels, out_channels, rate3))
+        modules.append(ASPPPooling(in_channels, out_channels))
+
+        self.convs = nn.ModuleList(modules)
+
+        self.project = nn.Sequential(
+            nn.Conv2d(5 * out_channels, out_channels, kernel_size=1, bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(),
+            nn.Dropout(0.5),
+        )
+
+    def forward(self, x):
+        res = []
+        for conv in self.convs:
+            res.append(conv(x))
+        res = torch.cat(res, dim=1)
+        return self.project(res)
+
+
+class SeparableConv2d(nn.Sequential):
+
+    def __init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=1,
+            padding=0,
+            dilation=1,
+            bias=True,
+    ):
+        dephtwise_conv = nn.Conv2d(
+            in_channels,
+            in_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=in_channels,
+            bias=False,
+        )
+        pointwise_conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            kernel_size=1,
+            bias=bias,
+        )
+        super().__init__(dephtwise_conv, pointwise_conv)
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/deeplabv3/model.py b/segmentation_models_pytorch/segmentation_models_pytorch/deeplabv3/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..49462be1bc52021a9481417c36d73cb8a572c3f4
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/deeplabv3/model.py
@@ -0,0 +1,183 @@
+import torch.nn as nn
+
+from typing import Optional
+from .decoder import DeepLabV3Decoder, DeepLabV3PlusDecoder
+from ..base import SegmentationModel, SegmentationHead, ClassificationHead
+from ..encoders import get_encoder
+
+
+class DeepLabV3(SegmentationModel):
+    """DeepLabV3_ implementation from "Rethinking Atrous Convolution for Semantic Image Segmentation"
+
+    Args:
+        encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone)
+            to extract features of different spatial resolution
+        encoder_depth: A number of stages used in encoder in range [3, 5]. Each stage generate features 
+            two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features
+            with shapes [(N, C, H, W),], for depth 1 - [(N, C, H, W), (N, C, H // 2, W // 2)] and so on).
+            Default is 5
+        encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and 
+            other pretrained weights (see table with available weights for each encoder_name)
+        decoder_channels: A number of convolution filters in ASPP module. Default is 256
+        in_channels: A number of input channels for the model, default is 3 (RGB images)
+        classes: A number of classes for output mask (or you can think as a number of channels of output mask)
+        activation: An activation function to apply after the final convolution layer.
+            Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, **callable** and **None**.
+            Default is **None**
+        upsampling: Final upsampling factor. Default is 8 to preserve input-output spatial shape identity
+        aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build 
+            on top of encoder if **aux_params** is not **None** (default). Supported params:
+                - classes (int): A number of classes
+                - pooling (str): One of "max", "avg". Default is "avg"
+                - dropout (float): Dropout factor in [0, 1)
+                - activation (str): An activation function to apply "sigmoid"/"softmax" (could be **None** to return logits)
+    Returns:
+        ``torch.nn.Module``: **DeepLabV3**
+
+    .. _DeepLabV3:
+        https://arxiv.org/abs/1706.05587
+
+    Reference:
+        https://arxiv.org/abs/1706.05587
+    """
+
+    def __init__(
+            self,
+            encoder_name: str = "resnet34",
+            encoder_depth: int = 5,
+            encoder_weights: Optional[str] = "imagenet",
+            decoder_channels: int = 256,
+            in_channels: int = 3,
+            classes: int = 1,
+            activation: Optional[str] = None,
+            upsampling: int = 8,
+            aux_params: Optional[dict] = None,
+    ):
+        super().__init__()
+
+        self.encoder = get_encoder(
+            encoder_name,
+            in_channels=in_channels,
+            depth=encoder_depth,
+            weights=encoder_weights,
+        )
+        self.encoder.make_dilated(
+            stage_list=[4, 5],
+            dilation_list=[2, 4]
+        )
+
+        self.decoder = DeepLabV3Decoder(
+            in_channels=self.encoder.out_channels[-1],
+            out_channels=decoder_channels,
+        )
+
+        self.segmentation_head = SegmentationHead(
+            in_channels=self.decoder.out_channels,
+            out_channels=classes,
+            activation=activation,
+            kernel_size=1,
+            upsampling=upsampling,
+        )
+
+        if aux_params is not None:
+            self.classification_head = ClassificationHead(
+                in_channels=self.encoder.out_channels[-1], **aux_params
+            )
+        else:
+            self.classification_head = None
+
+
+class DeepLabV3Plus(SegmentationModel):
+    """DeepLabV3+ implementation from "Encoder-Decoder with Atrous Separable
+    Convolution for Semantic Image Segmentation"
+    
+    Args:
+        encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone)
+            to extract features of different spatial resolution
+        encoder_depth: A number of stages used in encoder in range [3, 5]. Each stage generate features 
+            two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features
+            with shapes [(N, C, H, W),], for depth 1 - [(N, C, H, W), (N, C, H // 2, W // 2)] and so on).
+            Default is 5
+        encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and 
+            other pretrained weights (see table with available weights for each encoder_name)
+        encoder_output_stride: Downsampling factor for last encoder features (see original paper for explanation)
+        decoder_atrous_rates: Dilation rates for ASPP module (should be a tuple of 3 integer values)
+        decoder_channels: A number of convolution filters in ASPP module. Default is 256
+        in_channels: A number of input channels for the model, default is 3 (RGB images)
+        classes: A number of classes for output mask (or you can think as a number of channels of output mask)
+        activation: An activation function to apply after the final convolution layer.
+            Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, **callable** and **None**.
+            Default is **None**
+        upsampling: Final upsampling factor. Default is 4 to preserve input-output spatial shape identity
+        aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build 
+            on top of encoder if **aux_params** is not **None** (default). Supported params:
+                - classes (int): A number of classes
+                - pooling (str): One of "max", "avg". Default is "avg"
+                - dropout (float): Dropout factor in [0, 1)
+                - activation (str): An activation function to apply "sigmoid"/"softmax" (could be **None** to return logits)
+    Returns:
+        ``torch.nn.Module``: **DeepLabV3Plus**
+
+    Reference:
+        https://arxiv.org/abs/1802.02611v3
+    """
+    def __init__(
+            self,
+            encoder_name: str = "resnet34",
+            encoder_depth: int = 5,
+            encoder_weights: Optional[str] = "imagenet",
+            encoder_output_stride: int = 16,
+            decoder_channels: int = 256,
+            decoder_atrous_rates: tuple = (12, 24, 36),
+            in_channels: int = 3,
+            classes: int = 1,
+            activation: Optional[str] = None,
+            upsampling: int = 4,
+            aux_params: Optional[dict] = None,
+    ):
+        super().__init__()
+
+        self.encoder = get_encoder(
+            encoder_name,
+            in_channels=in_channels,
+            depth=encoder_depth,
+            weights=encoder_weights,
+        )
+
+        if encoder_output_stride == 8:
+            self.encoder.make_dilated(
+                stage_list=[4, 5],
+                dilation_list=[2, 4]
+            )
+
+        elif encoder_output_stride == 16:
+            self.encoder.make_dilated(
+                stage_list=[5],
+                dilation_list=[2]
+            )
+        else:
+            raise ValueError(
+                "Encoder output stride should be 8 or 16, got {}".format(encoder_output_stride)
+            )
+
+        self.decoder = DeepLabV3PlusDecoder(
+            encoder_channels=self.encoder.out_channels,
+            out_channels=decoder_channels,
+            atrous_rates=decoder_atrous_rates,
+            output_stride=encoder_output_stride,
+        )
+
+        self.segmentation_head = SegmentationHead(
+            in_channels=self.decoder.out_channels,
+            out_channels=classes,
+            activation=activation,
+            kernel_size=1,
+            upsampling=upsampling,
+        )
+
+        if aux_params is not None:
+            self.classification_head = ClassificationHead(
+                in_channels=self.encoder.out_channels[-1], **aux_params
+            )
+        else:
+            self.classification_head = None
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/efficientunetplusplus/__init__.py b/segmentation_models_pytorch/segmentation_models_pytorch/efficientunetplusplus/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4141c0c7d433bc73eb88bebfcc1fe1a1d4332886
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/efficientunetplusplus/__init__.py
@@ -0,0 +1 @@
+from .model import EfficientUnetPlusPlus
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/efficientunetplusplus/decoder.py b/segmentation_models_pytorch/segmentation_models_pytorch/efficientunetplusplus/decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..99eef47f080623da799ccfa8b0d840839a4b7a0e
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/efficientunetplusplus/decoder.py
@@ -0,0 +1,148 @@
+import torch
+from torch.functional import norm
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..base import modules as md
+
+class InvertedResidual(nn.Module):
+    """
+    Inverted bottleneck residual block with an scSE block embedded into the residual layer, after the 
+    depthwise convolution. By default, uses batch normalization and Hardswish activation.
+    """
+    def __init__(self, in_channels, out_channels, kernel_size = 3, stride = 1, expansion_ratio = 1, squeeze_ratio = 1, \
+        activation = nn.Hardswish(True), normalization = nn.BatchNorm2d):
+        super().__init__()
+        self.same_shape = in_channels == out_channels
+        self.mid_channels = expansion_ratio*in_channels
+        self.block = nn.Sequential(
+            md.PointWiseConv2d(in_channels, self.mid_channels),
+            normalization(self.mid_channels),
+            activation,
+            md.DepthWiseConv2d(self.mid_channels, kernel_size=kernel_size, stride=stride),
+            normalization(self.mid_channels),
+            activation,
+            #md.sSEModule(self.mid_channels),
+            md.SCSEModule(self.mid_channels, reduction = squeeze_ratio),
+            #md.SEModule(self.mid_channels, reduction = squeeze_ratio),
+            md.PointWiseConv2d(self.mid_channels, out_channels),
+            normalization(out_channels)
+        )
+        
+        if not self.same_shape:
+            # 1x1 convolution used to match the number of channels in the skip feature maps with that 
+            # of the residual feature maps
+            self.skip_conv = nn.Sequential(
+                nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1),
+                normalization(out_channels)
+            )
+          
+    def forward(self, x):
+        residual = self.block(x)
+        
+        if not self.same_shape:
+            x = self.skip_conv(x)
+        return x + residual
+        
+class DecoderBlock(nn.Module):
+    def __init__(
+            self,
+            in_channels,
+            skip_channels,
+            out_channels,
+            squeeze_ratio=1,
+            expansion_ratio=1
+    ):
+        super().__init__()
+
+        # Inverted Residual block convolutions
+        self.conv1 = InvertedResidual(
+            in_channels=in_channels+skip_channels, 
+            out_channels=out_channels, 
+            kernel_size=3, 
+            stride=1, 
+            expansion_ratio=expansion_ratio, 
+            squeeze_ratio=squeeze_ratio
+        )
+        self.conv2 = InvertedResidual(
+            in_channels=out_channels, 
+            out_channels=out_channels, 
+            kernel_size=3, 
+            stride=1, 
+            expansion_ratio=expansion_ratio, 
+            squeeze_ratio=squeeze_ratio
+        )
+
+    def forward(self, x, skip=None):
+        x = F.interpolate(x, scale_factor=2, mode="nearest")
+
+        if skip is not None:
+            x = torch.cat([x, skip], dim=1)
+        x = self.conv1(x)
+        x = self.conv2(x)
+        return x
+
+class EfficientUnetPlusPlusDecoder(nn.Module):
+    def __init__(
+            self,
+            encoder_channels,
+            decoder_channels,
+            n_blocks=5,
+            squeeze_ratio=1,
+            expansion_ratio=1
+    ):
+        super().__init__()
+        if n_blocks != len(decoder_channels):
+            raise ValueError(
+                "Model depth is {}, but you provide `decoder_channels` for {} blocks.".format(
+                    n_blocks, len(decoder_channels)
+                )
+            )
+
+        encoder_channels = encoder_channels[1:]  # remove first skip with same spatial resolution
+        encoder_channels = encoder_channels[::-1]  # reverse channels to start from head of encoder
+        # computing blocks input and output channels
+        head_channels = encoder_channels[0]
+        self.in_channels = [head_channels] + list(decoder_channels[:-1])
+        self.skip_channels = list(encoder_channels[1:]) + [0]
+        self.out_channels = decoder_channels
+
+        # combine decoder keyword arguments
+        kwargs = dict(squeeze_ratio=squeeze_ratio, expansion_ratio=expansion_ratio)
+
+        blocks = {}
+        for layer_idx in range(len(self.in_channels) - 1):
+            for depth_idx in range(layer_idx+1):
+                if depth_idx == 0:
+                    in_ch = self.in_channels[layer_idx]
+                    skip_ch = self.skip_channels[layer_idx] * (layer_idx+1)
+                    out_ch = self.out_channels[layer_idx]
+                else:
+                    out_ch = self.skip_channels[layer_idx]
+                    skip_ch = self.skip_channels[layer_idx] * (layer_idx+1-depth_idx)
+                    in_ch = self.skip_channels[layer_idx - 1]
+                blocks[f'x_{depth_idx}_{layer_idx}'] = DecoderBlock(in_ch, skip_ch, out_ch, **kwargs)
+        blocks[f'x_{0}_{len(self.in_channels)-1}'] =\
+            DecoderBlock(self.in_channels[-1], 0, self.out_channels[-1], **kwargs)
+        self.blocks = nn.ModuleDict(blocks)
+        self.depth = len(self.in_channels) - 1
+
+    def forward(self, *features):
+
+        features = features[1:]    # remove first skip with same spatial resolution
+        features = features[::-1]  # reverse channels to start from head of encoder
+        # start building dense connections
+        dense_x = {}
+        for layer_idx in range(len(self.in_channels)-1):
+            for depth_idx in range(self.depth-layer_idx):
+                if layer_idx == 0:
+                    output = self.blocks[f'x_{depth_idx}_{depth_idx}'](features[depth_idx], features[depth_idx+1])
+                    dense_x[f'x_{depth_idx}_{depth_idx}'] = output
+                else:
+                    dense_l_i = depth_idx + layer_idx
+                    cat_features = [dense_x[f'x_{idx}_{dense_l_i}'] for idx in range(depth_idx+1, dense_l_i+1)]
+                    cat_features = torch.cat(cat_features + [features[dense_l_i+1]], dim=1)
+                    dense_x[f'x_{depth_idx}_{dense_l_i}'] =\
+                        self.blocks[f'x_{depth_idx}_{dense_l_i}'](dense_x[f'x_{depth_idx}_{dense_l_i-1}'], cat_features)
+        dense_x[f'x_{0}_{self.depth}'] = self.blocks[f'x_{0}_{self.depth}'](dense_x[f'x_{0}_{self.depth-1}'])
+        return dense_x[f'x_{0}_{self.depth}']
\ No newline at end of file
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/efficientunetplusplus/model.py b/segmentation_models_pytorch/segmentation_models_pytorch/efficientunetplusplus/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..f39e6d6da7558195c809750b767068ada2fb1cdf
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/efficientunetplusplus/model.py
@@ -0,0 +1,125 @@
+from typing import Optional, Union, List
+from .decoder import EfficientUnetPlusPlusDecoder
+from ..encoders import get_encoder
+from ..base import SegmentationModel
+from ..base import SegmentationHead, ClassificationHead
+from torchvision import transforms
+
+class EfficientUnetPlusPlus(SegmentationModel):
+    """The EfficientUNet++ is a fully convolutional neural network for ordinary and medical image semantic segmentation. 
+    Consists of an *encoder* and a *decoder*, connected by *skip connections*. The encoder extracts features of 
+    different spatial resolutions, which are fed to the decoder through skip connections. The decoder combines its 
+    own feature maps with the ones from skip connections to produce accurate segmentations masks.  The EfficientUNet++ 
+    decoder architecture is based on the UNet++, a model composed of nested U-Net-like decoder sub-networks. To 
+    increase performance and computational efficiency, the EfficientUNet++ replaces the UNet++'s blocks with 
+    inverted residual blocks with depthwise convolutions and embedded spatial and channel attention mechanisms.
+    Synergizes well with EfficientNet encoders. Due to their efficient visual representations (i.e., using few channels
+    to represent extracted features), EfficientNet encoders require few computation from the decoder.
+
+    Args:
+        encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone) to extract features
+        encoder_depth: Number of stages of the encoder, in range [3 ,5]. Each stage generate features two times smaller, 
+            in spatial dimensions, than the previous one (e.g., for depth=0 features will haves shapes [(N, C, H, W)]), 
+            for depth 1 features will have shapes [(N, C, H, W), (N, C, H // 2, W // 2)] and so on).
+            Default is 5
+        encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and 
+            other pretrained weights (see table with available weights for each encoder_name)
+        decoder_channels: List of integers which specify **in_channels** parameter for convolutions used in the decoder.
+            Length of the list should be the same as **encoder_depth**
+        in_channels: The number of input channels of the model, default is 3 (RGB images)
+        classes: The number of classes of the output mask. Can be thought of as the number of channels of the mask
+        activation: An activation function to apply after the final convolution layer.
+            Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, **callable** and **None**.
+            Default is **None**
+        aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is built 
+            on top of encoder if **aux_params** is not **None** (default). Supported params:
+                - classes (int): A number of classes
+                - pooling (str): One of "max", "avg". Default is "avg"
+                - dropout (float): Dropout factor in [0, 1)
+                - activation (str): An activation function to apply "sigmoid"/"softmax" (could be **None** to return logits)
+    Returns:
+        ``torch.nn.Module``: **EfficientUnet++**
+
+    Reference:
+        https://arxiv.org/abs/2106.11447
+    """
+
+    def __init__(
+        self,
+        encoder_name: str = "timm-efficientnet-b5",
+        encoder_depth: int = 5,
+        encoder_weights: Optional[str] = "imagenet",
+        decoder_channels: List[int] = (256, 128, 64, 32, 16),
+        squeeze_ratio: int = 1,
+        expansion_ratio: int = 1,
+        in_channels: int = 3,
+        classes: int = 1,
+        activation: Optional[Union[str, callable]] = None,
+        aux_params: Optional[dict] = None,
+    ):
+        super().__init__()
+        self.classes = classes
+        self.encoder = get_encoder(
+            encoder_name,
+            in_channels=in_channels,
+            depth=encoder_depth,
+            weights=encoder_weights,
+        )
+
+        self.decoder = EfficientUnetPlusPlusDecoder(
+            encoder_channels=self.encoder.out_channels,
+            decoder_channels=decoder_channels,
+            n_blocks=encoder_depth,
+            squeeze_ratio=squeeze_ratio,
+            expansion_ratio=expansion_ratio
+        )
+
+        self.segmentation_head = SegmentationHead(
+            in_channels=decoder_channels[-1],
+            out_channels=classes,
+            activation=activation,
+            kernel_size=3,
+        )
+
+        if aux_params is not None:
+            self.classification_head = ClassificationHead(
+                in_channels=self.encoder.out_channels[-1], **aux_params
+            )
+        else:
+            self.classification_head = None
+
+        self.name = "EfficientUNet++-{}".format(encoder_name)
+        self.initialize()
+
+    def predict(self, x):
+        """Inference method. Switch model to `eval` mode, call `.forward(x)` with `torch.no_grad()`
+
+        Args:
+            x: 4D torch tensor with shape (batch_size, channels, height, width)
+
+        Return:
+            prediction: 4D torch tensor with shape (batch_size, classes, height, width)
+
+        """
+        if self.training:
+            self.eval()
+
+        with torch.no_grad():
+            output = self.forward(x)
+
+        if self.classes > 1:
+            probs = torch.softmax(output, dim=1)
+        else:
+            probs = torch.sigmoid(output)
+
+        probs = probs.squeeze(0)
+        tf = transforms.Compose(
+            [
+                transforms.ToPILImage(),
+                transforms.Resize(x.size[1]),
+                transforms.ToTensor()
+            ]
+        )
+        full_mask = tf(probs.cpu())   
+
+        return full_mask
\ No newline at end of file
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/encoders/__init__.py b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..40abaac6e8e9d6044c861e98cb2a050129b3e976
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/__init__.py
@@ -0,0 +1,84 @@
+import functools
+import torch.utils.model_zoo as model_zoo
+
+from .resnet import resnet_encoders
+from .dpn import dpn_encoders
+from .vgg import vgg_encoders
+from .senet import senet_encoders
+from .densenet import densenet_encoders
+from .inceptionresnetv2 import inceptionresnetv2_encoders
+from .inceptionv4 import inceptionv4_encoders
+from .efficientnet import efficient_net_encoders
+from .mobilenet import mobilenet_encoders
+from .xception import xception_encoders
+# from .timm_efficientnet import timm_efficientnet_encoders
+from .timm_resnest import timm_resnest_encoders
+from .timm_res2net import timm_res2net_encoders
+from .timm_regnet import timm_regnet_encoders
+from .timm_sknet import timm_sknet_encoders
+from ._preprocessing import preprocess_input
+
+encoders = {}
+encoders.update(resnet_encoders)
+encoders.update(dpn_encoders)
+encoders.update(vgg_encoders)
+encoders.update(senet_encoders)
+encoders.update(densenet_encoders)
+encoders.update(inceptionresnetv2_encoders)
+encoders.update(inceptionv4_encoders)
+encoders.update(efficient_net_encoders)
+encoders.update(mobilenet_encoders)
+encoders.update(xception_encoders)
+# encoders.update(timm_efficientnet_encoders)
+encoders.update(timm_resnest_encoders)
+encoders.update(timm_res2net_encoders)
+encoders.update(timm_regnet_encoders)
+encoders.update(timm_sknet_encoders)
+
+
+def get_encoder(name, in_channels=3, depth=5, weights=None):
+
+    try:
+        Encoder = encoders[name]["encoder"]
+    except KeyError:
+        raise KeyError("Wrong encoder name `{}`, supported encoders: {}".format(name, list(encoders.keys())))
+
+    params = encoders[name]["params"]
+    params.update(depth=depth)
+    encoder = Encoder(**params)
+
+    if weights is not None:
+        try:
+            settings = encoders[name]["pretrained_settings"][weights]
+        except KeyError:
+            raise KeyError("Wrong pretrained weights `{}` for encoder `{}`. Available options are: {}".format(
+                weights, name, list(encoders[name]["pretrained_settings"].keys()),
+            ))
+        encoder.load_state_dict(model_zoo.load_url(settings["url"]))
+
+    encoder.set_in_channels(in_channels)
+
+    return encoder
+
+
+def get_encoder_names():
+    return list(encoders.keys())
+
+
+def get_preprocessing_params(encoder_name, pretrained="imagenet"):
+    settings = encoders[encoder_name]["pretrained_settings"]
+
+    if pretrained not in settings.keys():
+        raise ValueError("Available pretrained options {}".format(settings.keys()))
+
+    formatted_settings = {}
+    formatted_settings["input_space"] = settings[pretrained].get("input_space")
+    formatted_settings["input_range"] = settings[pretrained].get("input_range")
+    formatted_settings["mean"] = settings[pretrained].get("mean")
+    formatted_settings["std"] = settings[pretrained].get("std")
+    return formatted_settings
+
+
+def get_preprocessing_fn(encoder_name, pretrained="imagenet"):
+    params = get_preprocessing_params(encoder_name, pretrained=pretrained)
+    return functools.partial(preprocess_input, **params)
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/encoders/_base.py b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..f80bee3df20e53dc6f4f15cfba76874a0a6cb49a
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/_base.py
@@ -0,0 +1,41 @@
+import torch
+import torch.nn as nn
+from typing import List
+from collections import OrderedDict
+
+from . import _utils as utils
+
+
+class EncoderMixin:
+    """Add encoder functionality such as:
+        - output channels specification of feature tensors (produced by encoder)
+        - patching first convolution for arbitrary input channels
+    """
+
+    @property
+    def out_channels(self):
+        """Return channels dimensions for each tensor of forward output of encoder"""
+        return self._out_channels[: self._depth + 1]
+
+    def set_in_channels(self, in_channels):
+        """Change first convolution channels"""
+        if in_channels == 3:
+            return
+
+        self._in_channels = in_channels
+        if self._out_channels[0] == 3:
+            self._out_channels = tuple([in_channels] + list(self._out_channels)[1:])
+
+        utils.patch_first_conv(model=self, in_channels=in_channels)
+
+    def get_stages(self):
+        """Method should be overridden in encoder"""
+        raise NotImplementedError
+
+    def make_dilated(self, stage_list, dilation_list):
+        stages = self.get_stages()
+        for stage_indx, dilation_rate in zip(stage_list, dilation_list):
+            utils.replace_strides_with_dilation(
+                module=stages[stage_indx],
+                dilation_rate=dilation_rate,
+            )
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/encoders/_preprocessing.py b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/_preprocessing.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec19d542f1fd8033525ef056adf252041db26e15
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/_preprocessing.py
@@ -0,0 +1,23 @@
+import numpy as np
+
+
+def preprocess_input(
+    x, mean=None, std=None, input_space="RGB", input_range=None, **kwargs
+):
+
+    if input_space == "BGR":
+        x = x[..., ::-1].copy()
+
+    if input_range is not None:
+        if x.max() > 1 and input_range[1] == 1:
+            x = x / 255.0
+
+    if mean is not None:
+        mean = np.array(mean)
+        x = x - mean
+
+    if std is not None:
+        std = np.array(std)
+        x = x / std
+
+    return x
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/encoders/_utils.py b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..294a07aae54a803c9937324451ffccb7b3aaef0e
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/_utils.py
@@ -0,0 +1,50 @@
+import torch
+import torch.nn as nn
+
+
+def patch_first_conv(model, in_channels):
+    """Change first convolution layer input channels.
+    In case:
+        in_channels == 1 or in_channels == 2 -> reuse original weights
+        in_channels > 3 -> make random kaiming normal initialization
+    """
+
+    # get first conv
+    for module in model.modules():
+        if isinstance(module, nn.Conv2d):
+            break
+
+    # change input channels for first conv
+    module.in_channels = in_channels
+    weight = module.weight.detach()
+    reset = False
+
+    if in_channels == 1:
+        weight = weight.sum(1, keepdim=True)
+    elif in_channels == 2:
+        weight = weight[:, :2] * (3.0 / 2.0)
+    else:
+        reset = True
+        weight = torch.Tensor(
+            module.out_channels,
+            module.in_channels // module.groups,
+            *module.kernel_size
+        )
+
+    module.weight = nn.parameter.Parameter(weight)
+    if reset:
+        module.reset_parameters()
+
+
+def replace_strides_with_dilation(module, dilation_rate):
+    """Patch Conv2d modules replacing strides with dilation"""
+    for mod in module.modules():
+        if isinstance(mod, nn.Conv2d):
+            mod.stride = (1, 1)
+            mod.dilation = (dilation_rate, dilation_rate)
+            kh, kw = mod.kernel_size
+            mod.padding = ((kh // 2) * dilation_rate, (kh // 2) * dilation_rate)
+
+            # Kostyl for EfficientNet
+            if hasattr(mod, "static_padding"):
+                mod.static_padding = nn.Identity()
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/encoders/densenet.py b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/densenet.py
new file mode 100644
index 0000000000000000000000000000000000000000..45c8375d9d38a3aafe07a62b89f1b81afb195935
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/densenet.py
@@ -0,0 +1,146 @@
+""" Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin`
+
+Attributes:
+
+    _out_channels (list of int): specify number of channels for each encoder feature tensor
+    _depth (int): specify number of stages in decoder (in other words number of downsampling operations)
+    _in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3)
+
+Methods:
+
+    forward(self, x: torch.Tensor)
+        produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of
+        shape NCHW (features should be sorted in descending order according to spatial resolution, starting
+        with resolution same as input `x` tensor).
+
+        Input: `x` with shape (1, 3, 64, 64)
+        Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes
+                [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8),
+                (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ)
+
+        also should support number of features according to specified depth, e.g. if depth = 5,
+        number of feature tensors = 6 (one with same resolution as input and 5 downsampled),
+        depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled).
+"""
+
+import re
+import torch.nn as nn
+
+from pretrainedmodels.models.torchvision_models import pretrained_settings
+from torchvision.models.densenet import DenseNet
+
+from ._base import EncoderMixin
+
+
+class TransitionWithSkip(nn.Module):
+
+    def __init__(self, module):
+        super().__init__()
+        self.module = module
+
+    def forward(self, x):
+        for module in self.module:
+            x = module(x)
+            if isinstance(module, nn.ReLU):
+                skip = x
+        return x, skip
+
+
+class DenseNetEncoder(DenseNet, EncoderMixin):
+    def __init__(self, out_channels, depth=5, **kwargs):
+        super().__init__(**kwargs)
+        self._out_channels = out_channels
+        self._depth = depth
+        self._in_channels = 3
+        del self.classifier
+
+    def make_dilated(self, stage_list, dilation_list):
+        raise ValueError("DenseNet encoders do not support dilated mode "
+                         "due to pooling operation for downsampling!")
+
+    def get_stages(self):
+        return [
+            nn.Identity(),
+            nn.Sequential(self.features.conv0, self.features.norm0, self.features.relu0),
+            nn.Sequential(self.features.pool0, self.features.denseblock1,
+                          TransitionWithSkip(self.features.transition1)),
+            nn.Sequential(self.features.denseblock2, TransitionWithSkip(self.features.transition2)),
+            nn.Sequential(self.features.denseblock3, TransitionWithSkip(self.features.transition3)),
+            nn.Sequential(self.features.denseblock4, self.features.norm5)
+        ]
+
+    def forward(self, x):
+
+        stages = self.get_stages()
+
+        features = []
+        for i in range(self._depth + 1):
+            x = stages[i](x)
+            if isinstance(x, (list, tuple)):
+                x, skip = x
+                features.append(skip)
+            else:
+                features.append(x)
+
+        return features
+
+    def load_state_dict(self, state_dict):
+        pattern = re.compile(
+            r"^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$"
+        )
+        for key in list(state_dict.keys()):
+            res = pattern.match(key)
+            if res:
+                new_key = res.group(1) + res.group(2)
+                state_dict[new_key] = state_dict[key]
+                del state_dict[key]
+
+        # remove linear
+        state_dict.pop("classifier.bias")
+        state_dict.pop("classifier.weight")
+
+        super().load_state_dict(state_dict)
+
+
+densenet_encoders = {
+    "densenet121": {
+        "encoder": DenseNetEncoder,
+        "pretrained_settings": pretrained_settings["densenet121"],
+        "params": {
+            "out_channels": (3, 64, 256, 512, 1024, 1024),
+            "num_init_features": 64,
+            "growth_rate": 32,
+            "block_config": (6, 12, 24, 16),
+        },
+    },
+    "densenet169": {
+        "encoder": DenseNetEncoder,
+        "pretrained_settings": pretrained_settings["densenet169"],
+        "params": {
+            "out_channels": (3, 64, 256, 512, 1280, 1664),
+            "num_init_features": 64,
+            "growth_rate": 32,
+            "block_config": (6, 12, 32, 32),
+        },
+    },
+    "densenet201": {
+        "encoder": DenseNetEncoder,
+        "pretrained_settings": pretrained_settings["densenet201"],
+        "params": {
+            "out_channels": (3, 64, 256, 512, 1792, 1920),
+            "num_init_features": 64,
+            "growth_rate": 32,
+            "block_config": (6, 12, 48, 32),
+        },
+    },
+    "densenet161": {
+        "encoder": DenseNetEncoder,
+        "pretrained_settings": pretrained_settings["densenet161"],
+        "params": {
+            "out_channels": (3, 96, 384, 768, 2112, 2208),
+            "num_init_features": 96,
+            "growth_rate": 48,
+            "block_config": (6, 12, 36, 24),
+        },
+    },
+}
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/encoders/dpn.py b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/dpn.py
new file mode 100644
index 0000000000000000000000000000000000000000..a44d2db80586364914b2a0c296e311b566b33608
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/dpn.py
@@ -0,0 +1,170 @@
+""" Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin`
+
+Attributes:
+
+    _out_channels (list of int): specify number of channels for each encoder feature tensor
+    _depth (int): specify number of stages in decoder (in other words number of downsampling operations)
+    _in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3)
+
+Methods:
+
+    forward(self, x: torch.Tensor)
+        produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of
+        shape NCHW (features should be sorted in descending order according to spatial resolution, starting
+        with resolution same as input `x` tensor).
+
+        Input: `x` with shape (1, 3, 64, 64)
+        Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes
+                [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8),
+                (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ)
+
+        also should support number of features according to specified depth, e.g. if depth = 5,
+        number of feature tensors = 6 (one with same resolution as input and 5 downsampled),
+        depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled).
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from pretrainedmodels.models.dpn import DPN
+from pretrainedmodels.models.dpn import pretrained_settings
+
+from ._base import EncoderMixin
+
+
+class DPNEncoder(DPN, EncoderMixin):
+    def __init__(self, stage_idxs, out_channels, depth=5, **kwargs):
+        super().__init__(**kwargs)
+        self._stage_idxs = stage_idxs
+        self._depth = depth
+        self._out_channels = out_channels
+        self._in_channels = 3
+
+        del self.last_linear
+
+    def get_stages(self):
+        return [
+            nn.Identity(),
+            nn.Sequential(self.features[0].conv, self.features[0].bn, self.features[0].act),
+            nn.Sequential(self.features[0].pool, self.features[1 : self._stage_idxs[0]]),
+            self.features[self._stage_idxs[0] : self._stage_idxs[1]],
+            self.features[self._stage_idxs[1] : self._stage_idxs[2]],
+            self.features[self._stage_idxs[2] : self._stage_idxs[3]],
+        ]
+
+    def forward(self, x):
+
+        stages = self.get_stages()
+
+        features = []
+        for i in range(self._depth + 1):
+            x = stages[i](x)
+            if isinstance(x, (list, tuple)):
+                features.append(F.relu(torch.cat(x, dim=1), inplace=True))
+            else:
+                features.append(x)
+
+        return features
+
+    def load_state_dict(self, state_dict, **kwargs):
+        state_dict.pop("last_linear.bias")
+        state_dict.pop("last_linear.weight")
+        super().load_state_dict(state_dict, **kwargs)
+
+
+dpn_encoders = {
+    "dpn68": {
+        "encoder": DPNEncoder,
+        "pretrained_settings": pretrained_settings["dpn68"],
+        "params": {
+            "stage_idxs": (4, 8, 20, 24),
+            "out_channels": (3, 10, 144, 320, 704, 832),
+            "groups": 32,
+            "inc_sec": (16, 32, 32, 64),
+            "k_r": 128,
+            "k_sec": (3, 4, 12, 3),
+            "num_classes": 1000,
+            "num_init_features": 10,
+            "small": True,
+            "test_time_pool": True,
+        },
+    },
+    "dpn68b": {
+        "encoder": DPNEncoder,
+        "pretrained_settings": pretrained_settings["dpn68b"],
+        "params": {
+            "stage_idxs": (4, 8, 20, 24),
+            "out_channels": (3, 10, 144, 320, 704, 832),
+            "b": True,
+            "groups": 32,
+            "inc_sec": (16, 32, 32, 64),
+            "k_r": 128,
+            "k_sec": (3, 4, 12, 3),
+            "num_classes": 1000,
+            "num_init_features": 10,
+            "small": True,
+            "test_time_pool": True,
+        },
+    },
+    "dpn92": {
+        "encoder": DPNEncoder,
+        "pretrained_settings": pretrained_settings["dpn92"],
+        "params": {
+            "stage_idxs": (4, 8, 28, 32),
+            "out_channels": (3, 64, 336, 704, 1552, 2688),
+            "groups": 32,
+            "inc_sec": (16, 32, 24, 128),
+            "k_r": 96,
+            "k_sec": (3, 4, 20, 3),
+            "num_classes": 1000,
+            "num_init_features": 64,
+            "test_time_pool": True,
+        },
+    },
+    "dpn98": {
+        "encoder": DPNEncoder,
+        "pretrained_settings": pretrained_settings["dpn98"],
+        "params": {
+            "stage_idxs": (4, 10, 30, 34),
+            "out_channels": (3, 96, 336, 768, 1728, 2688),
+            "groups": 40,
+            "inc_sec": (16, 32, 32, 128),
+            "k_r": 160,
+            "k_sec": (3, 6, 20, 3),
+            "num_classes": 1000,
+            "num_init_features": 96,
+            "test_time_pool": True,
+        },
+    },
+    "dpn107": {
+        "encoder": DPNEncoder,
+        "pretrained_settings": pretrained_settings["dpn107"],
+        "params": {
+            "stage_idxs": (5, 13, 33, 37),
+            "out_channels": (3, 128, 376, 1152, 2432, 2688),
+            "groups": 50,
+            "inc_sec": (20, 64, 64, 128),
+            "k_r": 200,
+            "k_sec": (4, 8, 20, 3),
+            "num_classes": 1000,
+            "num_init_features": 128,
+            "test_time_pool": True,
+        },
+    },
+    "dpn131": {
+        "encoder": DPNEncoder,
+        "pretrained_settings": pretrained_settings["dpn131"],
+        "params": {
+            "stage_idxs": (5, 13, 41, 45),
+            "out_channels": (3, 128, 352, 832, 1984, 2688),
+            "groups": 40,
+            "inc_sec": (16, 32, 32, 128),
+            "k_r": 160,
+            "k_sec": (4, 8, 28, 3),
+            "num_classes": 1000,
+            "num_init_features": 128,
+            "test_time_pool": True,
+        },
+    },
+}
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/encoders/efficientnet.py b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/efficientnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..10fc2c4d793503b25e6bce10c2d856a325124e71
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/efficientnet.py
@@ -0,0 +1,178 @@
+""" Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin`
+
+Attributes:
+
+    _out_channels (list of int): specify number of channels for each encoder feature tensor
+    _depth (int): specify number of stages in decoder (in other words number of downsampling operations)
+    _in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3)
+
+Methods:
+
+    forward(self, x: torch.Tensor)
+        produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of
+        shape NCHW (features should be sorted in descending order according to spatial resolution, starting
+        with resolution same as input `x` tensor).
+
+        Input: `x` with shape (1, 3, 64, 64)
+        Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes
+                [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8),
+                (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ)
+
+        also should support number of features according to specified depth, e.g. if depth = 5,
+        number of feature tensors = 6 (one with same resolution as input and 5 downsampled),
+        depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled).
+"""
+import torch.nn as nn
+from efficientnet_pytorch import EfficientNet
+from efficientnet_pytorch.utils import url_map, url_map_advprop, get_model_params
+
+from ._base import EncoderMixin
+
+
+class EfficientNetEncoder(EfficientNet, EncoderMixin):
+    def __init__(self, stage_idxs, out_channels, model_name, depth=5):
+
+        blocks_args, global_params = get_model_params(model_name, override_params=None)
+        super().__init__(blocks_args, global_params)
+
+        self._stage_idxs = stage_idxs
+        self._out_channels = out_channels
+        self._depth = depth
+        self._in_channels = 3
+
+        del self._fc
+
+    def get_stages(self):
+        return [
+            nn.Identity(),
+            nn.Sequential(self._conv_stem, self._bn0, self._swish),
+            self._blocks[:self._stage_idxs[0]],
+            self._blocks[self._stage_idxs[0]:self._stage_idxs[1]],
+            self._blocks[self._stage_idxs[1]:self._stage_idxs[2]],
+            self._blocks[self._stage_idxs[2]:],
+        ]
+
+    def forward(self, x):
+        stages = self.get_stages()
+
+        block_number = 0.
+        drop_connect_rate = self._global_params.drop_connect_rate
+
+        features = []
+        for i in range(self._depth + 1):
+
+            # Identity and Sequential stages
+            if i < 2:
+                x = stages[i](x)
+
+            # Block stages need drop_connect rate
+            else:
+                for module in stages[i]:
+                    drop_connect = drop_connect_rate * block_number / len(self._blocks)
+                    block_number += 1.
+                    x = module(x, drop_connect)
+
+            features.append(x)
+
+        return features
+
+    def load_state_dict(self, state_dict, **kwargs):
+        state_dict.pop("_fc.bias")
+        state_dict.pop("_fc.weight")
+        super().load_state_dict(state_dict, **kwargs)
+
+
+def _get_pretrained_settings(encoder):
+    pretrained_settings = {
+        "imagenet": {
+            "mean": [0.485, 0.456, 0.406],
+            "std": [0.229, 0.224, 0.225],
+            "url": url_map[encoder],
+            "input_space": "RGB",
+            "input_range": [0, 1],
+        },
+        "advprop": {
+            "mean": [0.5, 0.5, 0.5],
+            "std": [0.5, 0.5, 0.5],
+            "url": url_map_advprop[encoder],
+            "input_space": "RGB",
+            "input_range": [0, 1],
+        }
+    }
+    return pretrained_settings
+
+
+efficient_net_encoders = {
+    "efficientnet-b0": {
+        "encoder": EfficientNetEncoder,
+        "pretrained_settings": _get_pretrained_settings("efficientnet-b0"),
+        "params": {
+            "out_channels": (3, 32, 24, 40, 112, 320),
+            "stage_idxs": (3, 5, 9, 16),
+            "model_name": "efficientnet-b0",
+        },
+    },
+    "efficientnet-b1": {
+        "encoder": EfficientNetEncoder,
+        "pretrained_settings": _get_pretrained_settings("efficientnet-b1"),
+        "params": {
+            "out_channels": (3, 32, 24, 40, 112, 320),
+            "stage_idxs": (5, 8, 16, 23),
+            "model_name": "efficientnet-b1",
+        },
+    },
+    "efficientnet-b2": {
+        "encoder": EfficientNetEncoder,
+        "pretrained_settings": _get_pretrained_settings("efficientnet-b2"),
+        "params": {
+            "out_channels": (3, 32, 24, 48, 120, 352),
+            "stage_idxs": (5, 8, 16, 23),
+            "model_name": "efficientnet-b2",
+        },
+    },
+    "efficientnet-b3": {
+        "encoder": EfficientNetEncoder,
+        "pretrained_settings": _get_pretrained_settings("efficientnet-b3"),
+        "params": {
+            "out_channels": (3, 40, 32, 48, 136, 384),
+            "stage_idxs": (5, 8, 18, 26),
+            "model_name": "efficientnet-b3",
+        },
+    },
+    "efficientnet-b4": {
+        "encoder": EfficientNetEncoder,
+        "pretrained_settings": _get_pretrained_settings("efficientnet-b4"),
+        "params": {
+            "out_channels": (3, 48, 32, 56, 160, 448),
+            "stage_idxs": (6, 10, 22, 32),
+            "model_name": "efficientnet-b4",
+        },
+    },
+    "efficientnet-b5": {
+        "encoder": EfficientNetEncoder,
+        "pretrained_settings": _get_pretrained_settings("efficientnet-b5"),
+        "params": {
+            "out_channels": (3, 48, 40, 64, 176, 512),
+            "stage_idxs": (8, 13, 27, 39),
+            "model_name": "efficientnet-b5",
+        },
+    },
+    "efficientnet-b6": {
+        "encoder": EfficientNetEncoder,
+        "pretrained_settings": _get_pretrained_settings("efficientnet-b6"),
+        "params": {
+            "out_channels": (3, 56, 40, 72, 200, 576),
+            "stage_idxs": (9, 15, 31, 45),
+            "model_name": "efficientnet-b6",
+        },
+    },
+    "efficientnet-b7": {
+        "encoder": EfficientNetEncoder,
+        "pretrained_settings": _get_pretrained_settings("efficientnet-b7"),
+        "params": {
+            "out_channels": (3, 64, 48, 80, 224, 640),
+            "stage_idxs": (11, 18, 38, 55),
+            "model_name": "efficientnet-b7",
+        },
+    },
+}
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/encoders/inceptionresnetv2.py b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/inceptionresnetv2.py
new file mode 100644
index 0000000000000000000000000000000000000000..167afe24b9e9fc9e2371327129f52fda563a17f0
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/inceptionresnetv2.py
@@ -0,0 +1,90 @@
+""" Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin`
+
+Attributes:
+
+    _out_channels (list of int): specify number of channels for each encoder feature tensor
+    _depth (int): specify number of stages in decoder (in other words number of downsampling operations)
+    _in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3)
+
+Methods:
+
+    forward(self, x: torch.Tensor)
+        produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of
+        shape NCHW (features should be sorted in descending order according to spatial resolution, starting
+        with resolution same as input `x` tensor).
+
+        Input: `x` with shape (1, 3, 64, 64)
+        Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes
+                [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8),
+                (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ)
+
+        also should support number of features according to specified depth, e.g. if depth = 5,
+        number of feature tensors = 6 (one with same resolution as input and 5 downsampled),
+        depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled).
+"""
+
+import torch.nn as nn
+from pretrainedmodels.models.inceptionresnetv2 import InceptionResNetV2
+from pretrainedmodels.models.inceptionresnetv2 import pretrained_settings
+
+from ._base import EncoderMixin
+
+
+class InceptionResNetV2Encoder(InceptionResNetV2, EncoderMixin):
+    def __init__(self, out_channels, depth=5, **kwargs):
+        super().__init__(**kwargs)
+
+        self._out_channels = out_channels
+        self._depth = depth
+        self._in_channels = 3
+
+        # correct paddings
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                if m.kernel_size == (3, 3):
+                    m.padding = (1, 1)
+            if isinstance(m, nn.MaxPool2d):
+                m.padding = (1, 1)
+
+        # remove linear layers
+        del self.avgpool_1a
+        del self.last_linear
+
+    def make_dilated(self, stage_list, dilation_list):
+        raise ValueError("InceptionResnetV2 encoder does not support dilated mode "
+                         "due to pooling operation for downsampling!")
+
+    def get_stages(self):
+        return [
+            nn.Identity(),
+            nn.Sequential(self.conv2d_1a, self.conv2d_2a, self.conv2d_2b),
+            nn.Sequential(self.maxpool_3a, self.conv2d_3b, self.conv2d_4a),
+            nn.Sequential(self.maxpool_5a, self.mixed_5b, self.repeat),
+            nn.Sequential(self.mixed_6a, self.repeat_1),
+            nn.Sequential(self.mixed_7a, self.repeat_2, self.block8, self.conv2d_7b),
+        ]
+
+    def forward(self, x):
+
+        stages = self.get_stages()
+
+        features = []
+        for i in range(self._depth + 1):
+            x = stages[i](x)
+            features.append(x)
+
+        return features
+
+    def load_state_dict(self, state_dict, **kwargs):
+        state_dict.pop("last_linear.bias")
+        state_dict.pop("last_linear.weight")
+        super().load_state_dict(state_dict, **kwargs)
+
+
+inceptionresnetv2_encoders = {
+    "inceptionresnetv2": {
+        "encoder": InceptionResNetV2Encoder,
+        "pretrained_settings": pretrained_settings["inceptionresnetv2"],
+        "params": {"out_channels": (3, 64, 192, 320, 1088, 1536), "num_classes": 1000},
+    }
+}
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/encoders/inceptionv4.py b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/inceptionv4.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ae59de7cc0f4bb4800a0875dfb705da1a38f6fa
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/inceptionv4.py
@@ -0,0 +1,93 @@
+""" Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin`
+
+Attributes:
+
+    _out_channels (list of int): specify number of channels for each encoder feature tensor
+    _depth (int): specify number of stages in decoder (in other words number of downsampling operations)
+    _in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3)
+
+Methods:
+
+    forward(self, x: torch.Tensor)
+        produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of
+        shape NCHW (features should be sorted in descending order according to spatial resolution, starting
+        with resolution same as input `x` tensor).
+
+        Input: `x` with shape (1, 3, 64, 64)
+        Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes
+                [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8),
+                (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ)
+
+        also should support number of features according to specified depth, e.g. if depth = 5,
+        number of feature tensors = 6 (one with same resolution as input and 5 downsampled),
+        depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled).
+"""
+
+import torch.nn as nn
+from pretrainedmodels.models.inceptionv4 import InceptionV4, BasicConv2d
+from pretrainedmodels.models.inceptionv4 import pretrained_settings
+
+from ._base import EncoderMixin
+
+
+class InceptionV4Encoder(InceptionV4, EncoderMixin):
+    def __init__(self, stage_idxs, out_channels, depth=5, **kwargs):
+        super().__init__(**kwargs)
+        self._stage_idxs = stage_idxs
+        self._out_channels = out_channels
+        self._depth = depth
+        self._in_channels = 3
+
+        # correct paddings
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                if m.kernel_size == (3, 3):
+                    m.padding = (1, 1)
+            if isinstance(m, nn.MaxPool2d):
+                m.padding = (1, 1)
+
+        # remove linear layers
+        del self.last_linear
+
+    def make_dilated(self, stage_list, dilation_list):
+        raise ValueError("InceptionV4 encoder does not support dilated mode "
+                         "due to pooling operation for downsampling!")
+
+    def get_stages(self):
+        return [
+            nn.Identity(),
+            self.features[: self._stage_idxs[0]],
+            self.features[self._stage_idxs[0]: self._stage_idxs[1]],
+            self.features[self._stage_idxs[1]: self._stage_idxs[2]],
+            self.features[self._stage_idxs[2]: self._stage_idxs[3]],
+            self.features[self._stage_idxs[3]:],
+        ]
+
+    def forward(self, x):
+
+        stages = self.get_stages()
+
+        features = []
+        for i in range(self._depth + 1):
+            x = stages[i](x)
+            features.append(x)
+
+        return features
+
+    def load_state_dict(self, state_dict, **kwargs):
+        state_dict.pop("last_linear.bias")
+        state_dict.pop("last_linear.weight")
+        super().load_state_dict(state_dict, **kwargs)
+
+
+inceptionv4_encoders = {
+    "inceptionv4": {
+        "encoder": InceptionV4Encoder,
+        "pretrained_settings": pretrained_settings["inceptionv4"],
+        "params": {
+            "stage_idxs": (3, 5, 9, 15),
+            "out_channels": (3, 64, 192, 384, 1024, 1536),
+            "num_classes": 1001,
+        },
+    }
+}
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/encoders/mobilenet.py b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/mobilenet.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee896af3ca6edbb940404c57dcf6447ba0a1f576
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/mobilenet.py
@@ -0,0 +1,83 @@
+""" Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin`
+
+Attributes:
+
+    _out_channels (list of int): specify number of channels for each encoder feature tensor
+    _depth (int): specify number of stages in decoder (in other words number of downsampling operations)
+    _in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3)
+
+Methods:
+
+    forward(self, x: torch.Tensor)
+        produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of
+        shape NCHW (features should be sorted in descending order according to spatial resolution, starting
+        with resolution same as input `x` tensor).
+
+        Input: `x` with shape (1, 3, 64, 64)
+        Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes
+                [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8),
+                (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ)
+
+        also should support number of features according to specified depth, e.g. if depth = 5,
+        number of feature tensors = 6 (one with same resolution as input and 5 downsampled),
+        depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled).
+"""
+
+import torchvision
+import torch.nn as nn
+
+from ._base import EncoderMixin
+
+
+class MobileNetV2Encoder(torchvision.models.MobileNetV2, EncoderMixin):
+
+    def __init__(self, out_channels, depth=5, **kwargs):
+        super().__init__(**kwargs)
+        self._depth = depth
+        self._out_channels = out_channels
+        self._in_channels = 3
+        del self.classifier
+
+    def get_stages(self):
+        return [
+            nn.Identity(),
+            self.features[:2],
+            self.features[2:4],
+            self.features[4:7],
+            self.features[7:14],
+            self.features[14:],
+        ]
+
+    def forward(self, x):
+        stages = self.get_stages()
+
+        features = []
+        for i in range(self._depth + 1):
+            x = stages[i](x)
+            features.append(x)
+
+        return features
+
+    def load_state_dict(self, state_dict, **kwargs):
+        state_dict.pop("classifier.1.bias")
+        state_dict.pop("classifier.1.weight")
+        super().load_state_dict(state_dict, **kwargs)
+
+
+mobilenet_encoders = {
+    "mobilenet_v2": {
+        "encoder": MobileNetV2Encoder,
+        "pretrained_settings": {
+            "imagenet": {
+                "mean": [0.485, 0.456, 0.406],
+                "std": [0.229, 0.224, 0.225],
+                "url": "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth",
+                "input_space": "RGB",
+                "input_range": [0, 1],
+            },
+        },
+        "params": {
+            "out_channels": (3, 16, 24, 32, 96, 1280),
+        },
+    },
+}
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/encoders/resnet.py b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/resnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae443fd7524a4abeffb794d029b5ffc62042bcbe
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/resnet.py
@@ -0,0 +1,238 @@
+""" Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin`
+
+Attributes:
+
+    _out_channels (list of int): specify number of channels for each encoder feature tensor
+    _depth (int): specify number of stages in decoder (in other words number of downsampling operations)
+    _in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3)
+
+Methods:
+
+    forward(self, x: torch.Tensor)
+        produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of
+        shape NCHW (features should be sorted in descending order according to spatial resolution, starting
+        with resolution same as input `x` tensor).
+
+        Input: `x` with shape (1, 3, 64, 64)
+        Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes
+                [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8),
+                (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ)
+
+        also should support number of features according to specified depth, e.g. if depth = 5,
+        number of feature tensors = 6 (one with same resolution as input and 5 downsampled),
+        depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled).
+"""
+from copy import deepcopy
+
+import torch.nn as nn
+
+from torchvision.models.resnet import ResNet
+from torchvision.models.resnet import BasicBlock
+from torchvision.models.resnet import Bottleneck
+from pretrainedmodels.models.torchvision_models import pretrained_settings
+
+from ._base import EncoderMixin
+
+
+class ResNetEncoder(ResNet, EncoderMixin):
+    def __init__(self, out_channels, depth=5, **kwargs):
+        super().__init__(**kwargs)
+        self._depth = depth
+        self._out_channels = out_channels
+        self._in_channels = 3
+
+        del self.fc
+        del self.avgpool
+
+    def get_stages(self):
+        return [
+            nn.Identity(),
+            nn.Sequential(self.conv1, self.bn1, self.relu),
+            nn.Sequential(self.maxpool, self.layer1),
+            self.layer2,
+            self.layer3,
+            self.layer4,
+        ]
+
+    def forward(self, x):
+        stages = self.get_stages()
+
+        features = []
+        for i in range(self._depth + 1):
+            x = stages[i](x)
+            features.append(x)
+
+        return features
+
+    def load_state_dict(self, state_dict, **kwargs):
+        state_dict.pop("fc.bias")
+        state_dict.pop("fc.weight")
+        super().load_state_dict(state_dict, **kwargs)
+
+
+new_settings = {
+    "resnet18": {
+        "ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet18-d92f0530.pth",
+        "swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet18-118f1556.pth"
+    },
+    "resnet50": {
+        "ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet50-08389792.pth",
+        "swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet50-16a12f1b.pth"
+    },
+    "resnext50_32x4d": {
+        "imagenet": "https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth",
+        "ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext50_32x4-ddb3e555.pth",
+        "swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext50_32x4-72679e44.pth",
+    },
+    "resnext101_32x4d": {
+        "ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x4-dc43570a.pth",
+        "swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x4-3f87e46b.pth"
+    },
+    "resnext101_32x8d": {
+        "imagenet": "https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth",
+        "instagram": "https://download.pytorch.org/models/ig_resnext101_32x8-c38310e5.pth",
+        "ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x8-2cfe2f8b.pth",
+        "swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x8-b4712904.pth",
+    },
+    "resnext101_32x16d": {
+        "instagram": "https://download.pytorch.org/models/ig_resnext101_32x16-c6f796b0.pth",
+        "ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x16-15fffa57.pth",
+        "swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x16-f3559a9c.pth",
+    },
+    "resnext101_32x32d": {
+        "instagram": "https://download.pytorch.org/models/ig_resnext101_32x32-e4b90b00.pth",
+    },
+    "resnext101_32x48d": {
+        "instagram": "https://download.pytorch.org/models/ig_resnext101_32x48-3e41cc8a.pth",
+    }
+}
+
+pretrained_settings = deepcopy(pretrained_settings)
+for model_name, sources in new_settings.items():
+    if model_name not in pretrained_settings:
+        pretrained_settings[model_name] = {}
+
+    for source_name, source_url in sources.items():
+        pretrained_settings[model_name][source_name] = {
+            "url": source_url,
+            'input_size': [3, 224, 224],
+            'input_range': [0, 1],
+            'mean': [0.485, 0.456, 0.406],
+            'std': [0.229, 0.224, 0.225],
+            'num_classes': 1000
+        }
+
+
+resnet_encoders = {
+    "resnet18": {
+        "encoder": ResNetEncoder,
+        "pretrained_settings": pretrained_settings["resnet18"],
+        "params": {
+            "out_channels": (3, 64, 64, 128, 256, 512),
+            "block": BasicBlock,
+            "layers": [2, 2, 2, 2],
+        },
+    },
+    "resnet34": {
+        "encoder": ResNetEncoder,
+        "pretrained_settings": pretrained_settings["resnet34"],
+        "params": {
+            "out_channels": (3, 64, 64, 128, 256, 512),
+            "block": BasicBlock,
+            "layers": [3, 4, 6, 3],
+        },
+    },
+    "resnet50": {
+        "encoder": ResNetEncoder,
+        "pretrained_settings": pretrained_settings["resnet50"],
+        "params": {
+            "out_channels": (3, 64, 256, 512, 1024, 2048),
+            "block": Bottleneck,
+            "layers": [3, 4, 6, 3],
+        },
+    },
+    "resnet101": {
+        "encoder": ResNetEncoder,
+        "pretrained_settings": pretrained_settings["resnet101"],
+        "params": {
+            "out_channels": (3, 64, 256, 512, 1024, 2048),
+            "block": Bottleneck,
+            "layers": [3, 4, 23, 3],
+        },
+    },
+    "resnet152": {
+        "encoder": ResNetEncoder,
+        "pretrained_settings": pretrained_settings["resnet152"],
+        "params": {
+            "out_channels": (3, 64, 256, 512, 1024, 2048),
+            "block": Bottleneck,
+            "layers": [3, 8, 36, 3],
+        },
+    },
+    "resnext50_32x4d": {
+        "encoder": ResNetEncoder,
+        "pretrained_settings": pretrained_settings["resnext50_32x4d"],
+        "params": {
+            "out_channels": (3, 64, 256, 512, 1024, 2048),
+            "block": Bottleneck,
+            "layers": [3, 4, 6, 3],
+            "groups": 32,
+            "width_per_group": 4,
+        },
+    },
+    "resnext101_32x4d": {
+        "encoder": ResNetEncoder,
+        "pretrained_settings": pretrained_settings["resnext101_32x4d"],
+        "params": {
+            "out_channels": (3, 64, 256, 512, 1024, 2048),
+            "block": Bottleneck,
+            "layers": [3, 4, 23, 3],
+            "groups": 32,
+            "width_per_group": 4,
+        },
+    },
+    "resnext101_32x8d": {
+        "encoder": ResNetEncoder,
+        "pretrained_settings": pretrained_settings["resnext101_32x8d"],
+        "params": {
+            "out_channels": (3, 64, 256, 512, 1024, 2048),
+            "block": Bottleneck,
+            "layers": [3, 4, 23, 3],
+            "groups": 32,
+            "width_per_group": 8,
+        },
+    },
+    "resnext101_32x16d": {
+        "encoder": ResNetEncoder,
+        "pretrained_settings": pretrained_settings["resnext101_32x16d"],
+        "params": {
+            "out_channels": (3, 64, 256, 512, 1024, 2048),
+            "block": Bottleneck,
+            "layers": [3, 4, 23, 3],
+            "groups": 32,
+            "width_per_group": 16,
+        },
+    },
+    "resnext101_32x32d": {
+        "encoder": ResNetEncoder,
+        "pretrained_settings": pretrained_settings["resnext101_32x32d"],
+        "params": {
+            "out_channels": (3, 64, 256, 512, 1024, 2048),
+            "block": Bottleneck,
+            "layers": [3, 4, 23, 3],
+            "groups": 32,
+            "width_per_group": 32,
+        },
+    },
+    "resnext101_32x48d": {
+        "encoder": ResNetEncoder,
+        "pretrained_settings": pretrained_settings["resnext101_32x48d"],
+        "params": {
+            "out_channels": (3, 64, 256, 512, 1024, 2048),
+            "block": Bottleneck,
+            "layers": [3, 4, 23, 3],
+            "groups": 32,
+            "width_per_group": 48,
+        },
+    },
+}
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/encoders/senet.py b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/senet.py
new file mode 100644
index 0000000000000000000000000000000000000000..800bb0dd2d700e232647d33c4fd77c0757010172
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/senet.py
@@ -0,0 +1,174 @@
+""" Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin`
+
+Attributes:
+
+    _out_channels (list of int): specify number of channels for each encoder feature tensor
+    _depth (int): specify number of stages in decoder (in other words number of downsampling operations)
+    _in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3)
+
+Methods:
+
+    forward(self, x: torch.Tensor)
+        produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of
+        shape NCHW (features should be sorted in descending order according to spatial resolution, starting
+        with resolution same as input `x` tensor).
+
+        Input: `x` with shape (1, 3, 64, 64)
+        Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes
+                [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8),
+                (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ)
+
+        also should support number of features according to specified depth, e.g. if depth = 5,
+        number of feature tensors = 6 (one with same resolution as input and 5 downsampled),
+        depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled).
+"""
+
+import torch.nn as nn
+
+from pretrainedmodels.models.senet import (
+    SENet,
+    SEBottleneck,
+    SEResNetBottleneck,
+    SEResNeXtBottleneck,
+    pretrained_settings,
+)
+from ._base import EncoderMixin
+
+
+class SENetEncoder(SENet, EncoderMixin):
+    def __init__(self, out_channels, depth=5, **kwargs):
+        super().__init__(**kwargs)
+
+        self._out_channels = out_channels
+        self._depth = depth
+        self._in_channels = 3
+
+        del self.last_linear
+        del self.avg_pool
+
+    def get_stages(self):
+        return [
+            nn.Identity(),
+            self.layer0[:-1],
+            nn.Sequential(self.layer0[-1], self.layer1),
+            self.layer2,
+            self.layer3,
+            self.layer4,
+        ]
+
+    def forward(self, x):
+        stages = self.get_stages()
+
+        features = []
+        for i in range(self._depth + 1):
+            x = stages[i](x)
+            features.append(x)
+
+        return features
+
+    def load_state_dict(self, state_dict, **kwargs):
+        state_dict.pop("last_linear.bias")
+        state_dict.pop("last_linear.weight")
+        super().load_state_dict(state_dict, **kwargs)
+
+
+senet_encoders = {
+    "senet154": {
+        "encoder": SENetEncoder,
+        "pretrained_settings": pretrained_settings["senet154"],
+        "params": {
+            "out_channels": (3, 128, 256, 512, 1024, 2048),
+            "block": SEBottleneck,
+            "dropout_p": 0.2,
+            "groups": 64,
+            "layers": [3, 8, 36, 3],
+            "num_classes": 1000,
+            "reduction": 16,
+        },
+    },
+    "se_resnet50": {
+        "encoder": SENetEncoder,
+        "pretrained_settings": pretrained_settings["se_resnet50"],
+        "params": {
+            "out_channels": (3, 64, 256, 512, 1024, 2048),
+            "block": SEResNetBottleneck,
+            "layers": [3, 4, 6, 3],
+            "downsample_kernel_size": 1,
+            "downsample_padding": 0,
+            "dropout_p": None,
+            "groups": 1,
+            "inplanes": 64,
+            "input_3x3": False,
+            "num_classes": 1000,
+            "reduction": 16,
+        },
+    },
+    "se_resnet101": {
+        "encoder": SENetEncoder,
+        "pretrained_settings": pretrained_settings["se_resnet101"],
+        "params": {
+            "out_channels": (3, 64, 256, 512, 1024, 2048),
+            "block": SEResNetBottleneck,
+            "layers": [3, 4, 23, 3],
+            "downsample_kernel_size": 1,
+            "downsample_padding": 0,
+            "dropout_p": None,
+            "groups": 1,
+            "inplanes": 64,
+            "input_3x3": False,
+            "num_classes": 1000,
+            "reduction": 16,
+        },
+    },
+    "se_resnet152": {
+        "encoder": SENetEncoder,
+        "pretrained_settings": pretrained_settings["se_resnet152"],
+        "params": {
+            "out_channels": (3, 64, 256, 512, 1024, 2048),
+            "block": SEResNetBottleneck,
+            "layers": [3, 8, 36, 3],
+            "downsample_kernel_size": 1,
+            "downsample_padding": 0,
+            "dropout_p": None,
+            "groups": 1,
+            "inplanes": 64,
+            "input_3x3": False,
+            "num_classes": 1000,
+            "reduction": 16,
+        },
+    },
+    "se_resnext50_32x4d": {
+        "encoder": SENetEncoder,
+        "pretrained_settings": pretrained_settings["se_resnext50_32x4d"],
+        "params": {
+            "out_channels": (3, 64, 256, 512, 1024, 2048),
+            "block": SEResNeXtBottleneck,
+            "layers": [3, 4, 6, 3],
+            "downsample_kernel_size": 1,
+            "downsample_padding": 0,
+            "dropout_p": None,
+            "groups": 32,
+            "inplanes": 64,
+            "input_3x3": False,
+            "num_classes": 1000,
+            "reduction": 16,
+        },
+    },
+    "se_resnext101_32x4d": {
+        "encoder": SENetEncoder,
+        "pretrained_settings": pretrained_settings["se_resnext101_32x4d"],
+        "params": {
+            "out_channels": (3, 64, 256, 512, 1024, 2048),
+            "block": SEResNeXtBottleneck,
+            "layers": [3, 4, 23, 3],
+            "downsample_kernel_size": 1,
+            "downsample_padding": 0,
+            "dropout_p": None,
+            "groups": 32,
+            "inplanes": 64,
+            "input_3x3": False,
+            "num_classes": 1000,
+            "reduction": 16,
+        },
+    },
+}
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/encoders/timm_regnet.py b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/timm_regnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..e02ad59bd3b1711f9959ec447f69bd9584badd0c
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/timm_regnet.py
@@ -0,0 +1,332 @@
+from ._base import EncoderMixin
+from timm.models.regnet import RegNet
+import torch.nn as nn
+
+
+class RegNetEncoder(RegNet, EncoderMixin):
+    def __init__(self, out_channels, depth=5, **kwargs):
+        super().__init__(**kwargs)
+        self._depth = depth
+        self._out_channels = out_channels
+        self._in_channels = 3
+
+        del self.head
+
+    def get_stages(self):
+        return [
+            nn.Identity(),
+            self.stem,
+            self.s1,
+            self.s2,
+            self.s3,
+            self.s4,
+        ]
+
+    def forward(self, x):
+        stages = self.get_stages()
+
+        features = []
+        for i in range(self._depth + 1):
+            x = stages[i](x)
+            features.append(x)
+
+        return features
+
+    def load_state_dict(self, state_dict, **kwargs):
+        state_dict.pop("head.fc.weight")
+        state_dict.pop("head.fc.bias")
+        super().load_state_dict(state_dict, **kwargs)
+
+
+regnet_weights = {
+    'timm-regnetx_002': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_002-e7e85e5c.pth',
+    },
+    'timm-regnetx_004': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_004-7d0e9424.pth',
+    },
+    'timm-regnetx_006':  {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_006-85ec1baa.pth',
+    },
+    'timm-regnetx_008': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_008-d8b470eb.pth',
+    },
+    'timm-regnetx_016': {
+         'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_016-65ca972a.pth',
+    },
+    'timm-regnetx_032': {
+         'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_032-ed0c7f7e.pth',
+    },
+    'timm-regnetx_040': {
+         'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_040-73c2a654.pth',
+    },
+    'timm-regnetx_064': {
+         'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_064-29278baa.pth',
+    },
+    'timm-regnetx_080': {
+         'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_080-7c7fcab1.pth',
+    },
+    'timm-regnetx_120': {
+         'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_120-65d5521e.pth',
+    },
+    'timm-regnetx_160': {
+         'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_160-c98c4112.pth',
+    },
+    'timm-regnetx_320': {
+         'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_320-8ea38b93.pth',
+    },
+    'timm-regnety_002': {
+         'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_002-e68ca334.pth',
+    },
+    'timm-regnety_004': {
+         'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_004-0db870e6.pth',
+    },
+    'timm-regnety_006': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_006-c67e57ec.pth',
+    },
+    'timm-regnety_008': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_008-dc900dbe.pth',
+    },
+    'timm-regnety_016': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_016-54367f74.pth',
+    },
+    'timm-regnety_032': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/regnety_032_ra-7f2439f9.pth'
+    },
+    'timm-regnety_040': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_040-f0d569f9.pth'
+    },
+    'timm-regnety_064': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_064-0a48325c.pth'
+    },
+    'timm-regnety_080': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_080-e7f3eb93.pth',
+    },
+    'timm-regnety_120': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_120-721ba79a.pth',
+    },
+    'timm-regnety_160': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_160-d64013cd.pth',
+    },
+    'timm-regnety_320': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_320-ba464b29.pth'
+    }
+}
+
+pretrained_settings = {}
+for model_name, sources in regnet_weights.items():
+    pretrained_settings[model_name] = {}
+    for source_name, source_url in sources.items():
+        pretrained_settings[model_name][source_name] = {
+            "url": source_url,
+            'input_size': [3, 224, 224],
+            'input_range': [0, 1],
+            'mean': [0.485, 0.456, 0.406],
+            'std': [0.229, 0.224, 0.225],
+            'num_classes': 1000
+        }
+
+# at this point I am too lazy to copy configs, so I just used the same configs from timm's repo
+
+
+def _mcfg(**kwargs):
+    cfg = dict(se_ratio=0., bottle_ratio=1., stem_width=32)
+    cfg.update(**kwargs)
+    return cfg
+
+
+timm_regnet_encoders = {
+    'timm-regnetx_002': {
+        'encoder': RegNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-regnetx_002"],
+        'params': {
+            'out_channels': (3, 32, 24, 56, 152, 368),
+            'cfg': _mcfg(w0=24, wa=36.44, wm=2.49, group_w=8, depth=13)
+        },
+    },
+    'timm-regnetx_004': {
+        'encoder': RegNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-regnetx_004"],
+        'params': {
+            'out_channels': (3, 32, 32, 64, 160, 384),
+            'cfg': _mcfg(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22)
+        },
+    },
+    'timm-regnetx_006': {
+        'encoder': RegNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-regnetx_006"],
+        'params': {
+            'out_channels': (3, 32, 48, 96, 240, 528),
+            'cfg': _mcfg(w0=48, wa=36.97, wm=2.24, group_w=24, depth=16)
+        },
+    },
+    'timm-regnetx_008': {
+        'encoder': RegNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-regnetx_008"],
+        'params': {
+            'out_channels': (3, 32, 64, 128, 288, 672),
+            'cfg': _mcfg(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16)
+        },
+    },
+    'timm-regnetx_016': {
+        'encoder': RegNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-regnetx_016"],
+        'params': {
+            'out_channels': (3, 32, 72, 168, 408, 912),
+            'cfg': _mcfg(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18)
+        },
+    },
+    'timm-regnetx_032': {
+        'encoder': RegNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-regnetx_032"],
+        'params': {
+            'out_channels': (3, 32, 96, 192, 432, 1008),
+            'cfg': _mcfg(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25)
+        },
+    },
+    'timm-regnetx_040': {
+        'encoder': RegNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-regnetx_040"],
+        'params': {
+            'out_channels': (3, 32, 80, 240, 560, 1360),
+            'cfg': _mcfg(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23)
+        },
+    },
+    'timm-regnetx_064': {
+        'encoder': RegNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-regnetx_064"],
+        'params': {
+            'out_channels': (3, 32, 168, 392, 784, 1624),
+            'cfg': _mcfg(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17)
+        },
+    },
+    'timm-regnetx_080': {
+        'encoder': RegNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-regnetx_080"],
+        'params': {
+            'out_channels': (3, 32, 80, 240, 720, 1920),
+            'cfg': _mcfg(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23)
+        },
+    },
+    'timm-regnetx_120': {
+        'encoder': RegNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-regnetx_120"],
+        'params': {
+            'out_channels': (3, 32, 224, 448, 896, 2240),
+            'cfg': _mcfg(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19)
+        },
+    },
+    'timm-regnetx_160': {
+        'encoder': RegNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-regnetx_160"],
+        'params': {
+            'out_channels': (3, 32, 256, 512, 896, 2048),
+            'cfg': _mcfg(w0=216, wa=55.59, wm=2.1, group_w=128, depth=22)
+        },
+    },
+    'timm-regnetx_320': {
+        'encoder': RegNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-regnetx_320"],
+        'params': {
+            'out_channels': (3, 32, 336, 672, 1344, 2520),
+            'cfg': _mcfg(w0=320, wa=69.86, wm=2.0, group_w=168, depth=23)
+        },
+    },
+    #regnety
+    'timm-regnety_002': {
+        'encoder': RegNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-regnety_002"],
+        'params': {
+            'out_channels': (3, 32, 24, 56, 152, 368),
+            'cfg': _mcfg(w0=24, wa=36.44, wm=2.49, group_w=8, depth=13, se_ratio=0.25)
+        },
+    },
+    'timm-regnety_004': {
+        'encoder': RegNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-regnety_004"],
+        'params': {
+            'out_channels': (3, 32, 48, 104, 208, 440),
+            'cfg': _mcfg(w0=48, wa=27.89, wm=2.09, group_w=8, depth=16, se_ratio=0.25)
+        },
+    },
+    'timm-regnety_006': {
+        'encoder': RegNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-regnety_006"],
+        'params': {
+            'out_channels': (3, 32, 48, 112, 256, 608),
+            'cfg': _mcfg(w0=48, wa=32.54, wm=2.32, group_w=16, depth=15, se_ratio=0.25)
+        },
+    },
+    'timm-regnety_008': {
+        'encoder': RegNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-regnety_008"],
+        'params': {
+            'out_channels': (3, 32, 64, 128, 320, 768),
+            'cfg': _mcfg(w0=56, wa=38.84, wm=2.4, group_w=16, depth=14, se_ratio=0.25)
+        },
+    },
+    'timm-regnety_016': {
+        'encoder': RegNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-regnety_016"],
+        'params': {
+            'out_channels': (3, 32, 48, 120, 336, 888),
+            'cfg': _mcfg(w0=48, wa=20.71, wm=2.65, group_w=24, depth=27, se_ratio=0.25)
+        },
+    },
+    'timm-regnety_032': {
+        'encoder': RegNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-regnety_032"],
+        'params': {
+            'out_channels': (3, 32, 72, 216, 576, 1512),
+            'cfg': _mcfg(w0=80, wa=42.63, wm=2.66, group_w=24, depth=21, se_ratio=0.25)
+        },
+    },
+    'timm-regnety_040': {
+        'encoder': RegNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-regnety_040"],
+        'params': {
+            'out_channels': (3, 32, 128, 192, 512, 1088),
+            'cfg': _mcfg(w0=96, wa=31.41, wm=2.24, group_w=64, depth=22, se_ratio=0.25)
+        },
+    },
+    'timm-regnety_064': {
+        'encoder': RegNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-regnety_064"],
+        'params': {
+            'out_channels': (3, 32, 144, 288, 576, 1296),
+            'cfg': _mcfg(w0=112, wa=33.22, wm=2.27, group_w=72, depth=25, se_ratio=0.25)
+        },
+    },
+    'timm-regnety_080': {
+        'encoder': RegNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-regnety_080"],
+        'params': {
+            'out_channels': (3, 32, 168, 448, 896, 2016),
+            'cfg': _mcfg(w0=192, wa=76.82, wm=2.19, group_w=56, depth=17, se_ratio=0.25)
+        },
+    },
+    'timm-regnety_120': {
+        'encoder': RegNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-regnety_120"],
+        'params': {
+            'out_channels': (3, 32, 224, 448, 896, 2240),
+            'cfg': _mcfg(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19, se_ratio=0.25)
+        },
+    },
+    'timm-regnety_160': {
+        'encoder': RegNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-regnety_160"],
+        'params': {
+            'out_channels': (3, 32, 224, 448, 1232, 3024),
+            'cfg': _mcfg(w0=200, wa=106.23, wm=2.48, group_w=112, depth=18, se_ratio=0.25)
+        },
+    },
+    'timm-regnety_320': {
+        'encoder': RegNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-regnety_320"],
+        'params': {
+            'out_channels': (3, 32, 232, 696, 1392, 3712),
+            'cfg': _mcfg(w0=232, wa=115.89, wm=2.53, group_w=232, depth=20, se_ratio=0.25)
+        },
+    },
+}
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/encoders/timm_res2net.py b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/timm_res2net.py
new file mode 100644
index 0000000000000000000000000000000000000000..d3766b9d5da2bf5671102c58bcf0cdde6e7f7ebc
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/timm_res2net.py
@@ -0,0 +1,163 @@
+from ._base import EncoderMixin
+from timm.models.resnet import ResNet
+from timm.models.res2net import Bottle2neck
+import torch.nn as nn
+
+
+class Res2NetEncoder(ResNet, EncoderMixin):
+    def __init__(self, out_channels, depth=5, **kwargs):
+        super().__init__(**kwargs)
+        self._depth = depth
+        self._out_channels = out_channels
+        self._in_channels = 3
+
+        del self.fc
+        del self.global_pool
+
+    def get_stages(self):
+        return [
+            nn.Identity(),
+            nn.Sequential(self.conv1, self.bn1, self.act1),
+            nn.Sequential(self.maxpool, self.layer1),
+            self.layer2,
+            self.layer3,
+            self.layer4,
+        ]
+
+    def make_dilated(self, stage_list, dilation_list):
+        raise ValueError("Res2Net encoders do not support dilated mode")
+
+    def forward(self, x):
+        stages = self.get_stages()
+
+        features = []
+        for i in range(self._depth + 1):
+            x = stages[i](x)
+            features.append(x)
+
+        return features
+
+    def load_state_dict(self, state_dict, **kwargs):
+        state_dict.pop("fc.bias")
+        state_dict.pop("fc.weight")
+        super().load_state_dict(state_dict, **kwargs)
+
+
+res2net_weights = {
+    'timm-res2net50_26w_4s': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_4s-06e79181.pth'
+    },
+    'timm-res2net50_48w_2s': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_48w_2s-afed724a.pth'
+    },
+    'timm-res2net50_14w_8s': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_14w_8s-6527dddc.pth',
+    },
+    'timm-res2net50_26w_6s': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_6s-19041792.pth',
+    },
+    'timm-res2net50_26w_8s': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_8s-2c7c9f12.pth',
+    },
+    'timm-res2net101_26w_4s': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net101_26w_4s-02a759a1.pth',
+    },
+    'timm-res2next50': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2next50_4s-6ef7e7bf.pth',
+    }
+}
+
+pretrained_settings = {}
+for model_name, sources in res2net_weights.items():
+    pretrained_settings[model_name] = {}
+    for source_name, source_url in sources.items():
+        pretrained_settings[model_name][source_name] = {
+            "url": source_url,
+            'input_size': [3, 224, 224],
+            'input_range': [0, 1],
+            'mean': [0.485, 0.456, 0.406],
+            'std': [0.229, 0.224, 0.225],
+            'num_classes': 1000
+        }
+
+
+timm_res2net_encoders = {
+    'timm-res2net50_26w_4s': {
+        'encoder': Res2NetEncoder,
+        "pretrained_settings": pretrained_settings["timm-res2net50_26w_4s"],
+        'params': {
+            'out_channels': (3, 64, 256, 512, 1024, 2048),
+            'block': Bottle2neck,
+            'layers': [3, 4, 6, 3],
+            'base_width': 26,
+            'block_args': {'scale': 4}
+        },
+    },
+    'timm-res2net101_26w_4s': {
+        'encoder': Res2NetEncoder,
+        "pretrained_settings": pretrained_settings["timm-res2net101_26w_4s"],
+        'params': {
+            'out_channels': (3, 64, 256, 512, 1024, 2048),
+            'block': Bottle2neck,
+            'layers': [3, 4, 23, 3],
+            'base_width': 26,
+            'block_args': {'scale': 4}
+        },
+    },
+    'timm-res2net50_26w_6s': {
+        'encoder': Res2NetEncoder,
+        "pretrained_settings": pretrained_settings["timm-res2net50_26w_6s"],
+        'params': {
+            'out_channels': (3, 64, 256, 512, 1024, 2048),
+            'block': Bottle2neck,
+            'layers': [3, 4, 6, 3],
+            'base_width': 26,
+            'block_args': {'scale': 6}
+        },
+    },
+    'timm-res2net50_26w_8s': {
+        'encoder': Res2NetEncoder,
+        "pretrained_settings": pretrained_settings["timm-res2net50_26w_8s"],
+        'params': {
+            'out_channels': (3, 64, 256, 512, 1024, 2048),
+            'block': Bottle2neck,
+            'layers': [3, 4, 6, 3],
+            'base_width': 26,
+            'block_args': {'scale': 8}
+        },
+    },
+    'timm-res2net50_48w_2s': {
+        'encoder': Res2NetEncoder,
+        "pretrained_settings": pretrained_settings["timm-res2net50_48w_2s"],
+        'params': {
+            'out_channels': (3, 64, 256, 512, 1024, 2048),
+            'block': Bottle2neck,
+            'layers': [3, 4, 6, 3],
+            'base_width': 48,
+            'block_args': {'scale': 2}
+        },
+    },
+    'timm-res2net50_14w_8s': {
+        'encoder': Res2NetEncoder,
+        "pretrained_settings": pretrained_settings["timm-res2net50_14w_8s"],
+        'params': {
+            'out_channels': (3, 64, 256, 512, 1024, 2048),
+            'block': Bottle2neck,
+            'layers': [3, 4, 6, 3],
+            'base_width': 14,
+            'block_args': {'scale': 8}
+        },
+    },
+    'timm-res2next50': {
+        'encoder': Res2NetEncoder,
+        "pretrained_settings": pretrained_settings["timm-res2next50"],
+        'params': {
+            'out_channels': (3, 64, 256, 512, 1024, 2048),
+            'block': Bottle2neck,
+            'layers': [3, 4, 6, 3],
+            'base_width': 4,
+            'cardinality': 8,
+            'block_args': {'scale': 4}
+        },
+    }
+}
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/encoders/timm_resnest.py b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/timm_resnest.py
new file mode 100644
index 0000000000000000000000000000000000000000..77c558c935f458e46b15197bb2f14a546be0aacf
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/timm_resnest.py
@@ -0,0 +1,208 @@
+from ._base import EncoderMixin
+from timm.models.resnet import ResNet
+from timm.models.resnest import ResNestBottleneck
+import torch.nn as nn
+
+
+class ResNestEncoder(ResNet, EncoderMixin):
+    def __init__(self, out_channels, depth=5, **kwargs):
+        super().__init__(**kwargs)
+        self._depth = depth
+        self._out_channels = out_channels
+        self._in_channels = 3
+
+        del self.fc
+        del self.global_pool
+
+    def get_stages(self):
+        return [
+            nn.Identity(),
+            nn.Sequential(self.conv1, self.bn1, self.act1),
+            nn.Sequential(self.maxpool, self.layer1),
+            self.layer2,
+            self.layer3,
+            self.layer4,
+        ]
+
+    def make_dilated(self, stage_list, dilation_list):
+        raise ValueError("ResNest encoders do not support dilated mode")
+
+    def forward(self, x):
+        stages = self.get_stages()
+
+        features = []
+        for i in range(self._depth + 1):
+            x = stages[i](x)
+            features.append(x)
+
+        return features
+
+    def load_state_dict(self, state_dict, **kwargs):
+        state_dict.pop("fc.bias")
+        state_dict.pop("fc.weight")
+        super().load_state_dict(state_dict, **kwargs)
+
+
+resnest_weights = {
+    'timm-resnest14d': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_resnest14-9c8fe254.pth'
+    },
+    'timm-resnest26d': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_resnest26-50eb607c.pth'
+    },
+    'timm-resnest50d': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest50-528c19ca.pth',
+    },
+    'timm-resnest101e': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest101-22405ba7.pth',
+    },
+    'timm-resnest200e': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest200-75117900.pth',
+    },
+    'timm-resnest269e': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest269-0cc87c48.pth',
+    },
+    'timm-resnest50d_4s2x40d': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest50_fast_4s2x40d-41d14ed0.pth',
+    },
+    'timm-resnest50d_1s4x24d': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest50_fast_1s4x24d-d4a4f76f.pth',
+    }
+}
+
+pretrained_settings = {}
+for model_name, sources in resnest_weights.items():
+    pretrained_settings[model_name] = {}
+    for source_name, source_url in sources.items():
+        pretrained_settings[model_name][source_name] = {
+            "url": source_url,
+            'input_size': [3, 224, 224],
+            'input_range': [0, 1],
+            'mean': [0.485, 0.456, 0.406],
+            'std': [0.229, 0.224, 0.225],
+            'num_classes': 1000
+        }
+
+
+timm_resnest_encoders = {
+    'timm-resnest14d': {
+        'encoder': ResNestEncoder,
+        "pretrained_settings": pretrained_settings["timm-resnest14d"],
+        'params': {
+            'out_channels': (3, 64, 256, 512, 1024, 2048),
+            'block': ResNestBottleneck,
+            'layers': [1, 1, 1, 1],
+            'stem_type': 'deep',
+            'stem_width': 32,
+            'avg_down': True,
+            'base_width': 64,
+            'cardinality': 1,
+            'block_args': {'radix': 2, 'avd': True, 'avd_first': False}
+        }
+    },
+    'timm-resnest26d': {
+        'encoder': ResNestEncoder,
+        "pretrained_settings": pretrained_settings["timm-resnest26d"],
+        'params': {
+            'out_channels': (3, 64, 256, 512, 1024, 2048),
+            'block': ResNestBottleneck,
+            'layers': [2, 2, 2, 2],
+            'stem_type': 'deep',
+            'stem_width': 32,
+            'avg_down': True,
+            'base_width': 64,
+            'cardinality': 1,
+            'block_args': {'radix': 2, 'avd': True, 'avd_first': False}
+        }
+    },
+    'timm-resnest50d': {
+        'encoder': ResNestEncoder,
+        "pretrained_settings": pretrained_settings["timm-resnest50d"],
+        'params': {
+            'out_channels': (3, 64, 256, 512, 1024, 2048),
+            'block': ResNestBottleneck,
+            'layers': [3, 4, 6, 3],
+            'stem_type': 'deep',
+            'stem_width': 32,
+            'avg_down': True,
+            'base_width': 64,
+            'cardinality': 1,
+            'block_args': {'radix': 2, 'avd': True, 'avd_first': False}
+        }
+    },
+    'timm-resnest101e': {
+        'encoder': ResNestEncoder,
+        "pretrained_settings": pretrained_settings["timm-resnest101e"],
+        'params': {
+            'out_channels': (3, 128, 256, 512, 1024, 2048),
+            'block': ResNestBottleneck,
+            'layers': [3, 4, 23, 3],
+            'stem_type': 'deep',
+            'stem_width': 64,
+            'avg_down': True,
+            'base_width': 64,
+            'cardinality': 1,
+            'block_args': {'radix': 2, 'avd': True, 'avd_first': False}
+        }
+    },
+    'timm-resnest200e': {
+        'encoder': ResNestEncoder,
+        "pretrained_settings": pretrained_settings["timm-resnest200e"],
+        'params': {
+            'out_channels': (3, 128, 256, 512, 1024, 2048),
+            'block': ResNestBottleneck,
+            'layers': [3, 24, 36, 3],
+            'stem_type': 'deep',
+            'stem_width': 64,
+            'avg_down': True,
+            'base_width': 64,
+            'cardinality': 1,
+            'block_args': {'radix': 2, 'avd': True, 'avd_first': False}
+        }
+    },
+    'timm-resnest269e': {
+        'encoder': ResNestEncoder,
+        "pretrained_settings": pretrained_settings["timm-resnest269e"],
+        'params': {
+            'out_channels': (3, 128, 256, 512, 1024, 2048),
+            'block': ResNestBottleneck,
+            'layers': [3, 30, 48, 8],
+            'stem_type': 'deep',
+            'stem_width': 64,
+            'avg_down': True,
+            'base_width': 64,
+            'cardinality': 1,
+            'block_args': {'radix': 2, 'avd': True, 'avd_first': False}
+        },
+    },
+    'timm-resnest50d_4s2x40d': {
+        'encoder': ResNestEncoder,
+        "pretrained_settings": pretrained_settings["timm-resnest50d_4s2x40d"],
+        'params': {
+            'out_channels': (3, 64, 256, 512, 1024, 2048),
+            'block': ResNestBottleneck,
+            'layers': [3, 4, 6, 3],
+            'stem_type': 'deep',
+            'stem_width': 32,
+            'avg_down': True,
+            'base_width': 40,
+            'cardinality': 2,
+            'block_args': {'radix': 4, 'avd': True, 'avd_first': True}
+        }
+    },
+    'timm-resnest50d_1s4x24d': {
+        'encoder': ResNestEncoder,
+        "pretrained_settings": pretrained_settings["timm-resnest50d_1s4x24d"],
+        'params': {
+            'out_channels': (3, 64, 256, 512, 1024, 2048),
+            'block': ResNestBottleneck,
+            'layers': [3, 4, 6, 3],
+            'stem_type': 'deep',
+            'stem_width': 32,
+            'avg_down': True,
+            'base_width': 24,
+            'cardinality': 4,
+            'block_args': {'radix': 1, 'avd': True, 'avd_first': True}
+        }
+    }
+}
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/encoders/timm_sknet.py b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/timm_sknet.py
new file mode 100644
index 0000000000000000000000000000000000000000..bfb7572de8cee03cd916ba0faab2ae0c53e59f84
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/timm_sknet.py
@@ -0,0 +1,103 @@
+from ._base import EncoderMixin
+from timm.models.resnet import ResNet
+from timm.models.sknet import SelectiveKernelBottleneck, SelectiveKernelBasic
+import torch.nn as nn
+
+
+class SkNetEncoder(ResNet, EncoderMixin):
+    def __init__(self, out_channels, depth=5, **kwargs):
+        super().__init__(**kwargs)
+        self._depth = depth
+        self._out_channels = out_channels
+        self._in_channels = 3
+
+        del self.fc
+        del self.global_pool
+
+    def get_stages(self):
+        return [
+            nn.Identity(),
+            nn.Sequential(self.conv1, self.bn1, self.act1),
+            nn.Sequential(self.maxpool, self.layer1),
+            self.layer2,
+            self.layer3,
+            self.layer4,
+        ]
+
+    def forward(self, x):
+        stages = self.get_stages()
+
+        features = []
+        for i in range(self._depth + 1):
+            x = stages[i](x)
+            features.append(x)
+
+        return features
+
+    def load_state_dict(self, state_dict, **kwargs):
+        state_dict.pop("fc.bias")
+        state_dict.pop("fc.weight")
+        super().load_state_dict(state_dict, **kwargs)
+
+
+sknet_weights = {
+    'timm-skresnet18': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/skresnet18_ra-4eec2804.pth'
+    },
+    'timm-skresnet34': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/skresnet34_ra-bdc0ccde.pth'
+    },
+    'timm-skresnext50_32x4d': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/skresnext50_ra-f40e40bf.pth',
+    }
+}
+
+pretrained_settings = {}
+for model_name, sources in sknet_weights.items():
+    pretrained_settings[model_name] = {}
+    for source_name, source_url in sources.items():
+        pretrained_settings[model_name][source_name] = {
+            "url": source_url,
+            'input_size': [3, 224, 224],
+            'input_range': [0, 1],
+            'mean': [0.485, 0.456, 0.406],
+            'std': [0.229, 0.224, 0.225],
+            'num_classes': 1000
+        }
+
+timm_sknet_encoders = {
+    'timm-skresnet18': {
+        'encoder': SkNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-skresnet18"],
+        'params': {
+            'out_channels': (3, 64, 64, 128, 256, 512),
+            'block': SelectiveKernelBasic,
+            'layers': [2, 2, 2, 2],
+            'zero_init_last_bn': False,
+            'block_args': {'sk_kwargs': {'min_attn_channels': 16, 'attn_reduction': 8, 'split_input': True}}
+        }
+    },
+    'timm-skresnet34': {
+        'encoder': SkNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-skresnet34"],
+        'params': {
+            'out_channels': (3, 64, 64, 128, 256, 512),
+            'block': SelectiveKernelBasic,
+            'layers': [3, 4, 6, 3],
+            'zero_init_last_bn': False,
+            'block_args': {'sk_kwargs': {'min_attn_channels': 16, 'attn_reduction': 8, 'split_input': True}}
+        }
+    },
+    'timm-skresnext50_32x4d': {
+        'encoder': SkNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-skresnext50_32x4d"],
+        'params': {
+            'out_channels': (3, 64, 256, 512, 1024, 2048),
+            'block': SelectiveKernelBottleneck,
+            'layers': [3, 4, 6, 3],
+            'zero_init_last_bn': False,
+            'cardinality': 32,
+            'base_width': 4
+        }
+    }
+}
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/encoders/vgg.py b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/vgg.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb0e8ae84dab61760f1be4ca476492e12682599c
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/vgg.py
@@ -0,0 +1,157 @@
+""" Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin`
+
+Attributes:
+
+    _out_channels (list of int): specify number of channels for each encoder feature tensor
+    _depth (int): specify number of stages in decoder (in other words number of downsampling operations)
+    _in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3)
+
+Methods:
+
+    forward(self, x: torch.Tensor)
+        produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of
+        shape NCHW (features should be sorted in descending order according to spatial resolution, starting
+        with resolution same as input `x` tensor).
+
+        Input: `x` with shape (1, 3, 64, 64)
+        Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes
+                [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8),
+                (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ)
+
+        also should support number of features according to specified depth, e.g. if depth = 5,
+        number of feature tensors = 6 (one with same resolution as input and 5 downsampled),
+        depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled).
+"""
+
+import torch.nn as nn
+from torchvision.models.vgg import VGG
+from torchvision.models.vgg import make_layers
+from pretrainedmodels.models.torchvision_models import pretrained_settings
+
+from ._base import EncoderMixin
+
+# fmt: off
+cfg = {
+    'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
+    'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
+    'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
+    'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
+}
+# fmt: on
+
+
+class VGGEncoder(VGG, EncoderMixin):
+    def __init__(self, out_channels, config, batch_norm=False, depth=5, **kwargs):
+        super().__init__(make_layers(config, batch_norm=batch_norm), **kwargs)
+        self._out_channels = out_channels
+        self._depth = depth
+        self._in_channels = 3
+        del self.classifier
+
+    def make_dilated(self, stage_list, dilation_list):
+        raise ValueError("'VGG' models do not support dilated mode due to Max Pooling"
+                         " operations for downsampling!")
+
+    def get_stages(self):
+        stages = []
+        stage_modules = []
+        for module in self.features:
+            if isinstance(module, nn.MaxPool2d):
+                stages.append(nn.Sequential(*stage_modules))
+                stage_modules = []
+            stage_modules.append(module)
+        stages.append(nn.Sequential(*stage_modules))
+        return stages
+
+    def forward(self, x):
+        stages = self.get_stages()
+
+        features = []
+        for i in range(self._depth + 1):
+            x = stages[i](x)
+            features.append(x)
+
+        return features
+
+    def load_state_dict(self, state_dict, **kwargs):
+        keys = list(state_dict.keys())
+        for k in keys:
+            if k.startswith("classifier"):
+                state_dict.pop(k)
+        super().load_state_dict(state_dict, **kwargs)
+
+
+vgg_encoders = {
+    "vgg11": {
+        "encoder": VGGEncoder,
+        "pretrained_settings": pretrained_settings["vgg11"],
+        "params": {
+            "out_channels": (64, 128, 256, 512, 512, 512),
+            "config": cfg["A"],
+            "batch_norm": False,
+        },
+    },
+    "vgg11_bn": {
+        "encoder": VGGEncoder,
+        "pretrained_settings": pretrained_settings["vgg11_bn"],
+        "params": {
+            "out_channels": (64, 128, 256, 512, 512, 512),
+            "config": cfg["A"],
+            "batch_norm": True,
+        },
+    },
+    "vgg13": {
+        "encoder": VGGEncoder,
+        "pretrained_settings": pretrained_settings["vgg13"],
+        "params": {
+            "out_channels": (64, 128, 256, 512, 512, 512),
+            "config": cfg["B"],
+            "batch_norm": False,
+        },
+    },
+    "vgg13_bn": {
+        "encoder": VGGEncoder,
+        "pretrained_settings": pretrained_settings["vgg13_bn"],
+        "params": {
+            "out_channels": (64, 128, 256, 512, 512, 512),
+            "config": cfg["B"],
+            "batch_norm": True,
+        },
+    },
+    "vgg16": {
+        "encoder": VGGEncoder,
+        "pretrained_settings": pretrained_settings["vgg16"],
+        "params": {
+            "out_channels": (64, 128, 256, 512, 512, 512),
+            "config": cfg["D"],
+            "batch_norm": False,
+        },
+    },
+    "vgg16_bn": {
+        "encoder": VGGEncoder,
+        "pretrained_settings": pretrained_settings["vgg16_bn"],
+        "params": {
+            "out_channels": (64, 128, 256, 512, 512, 512),
+            "config": cfg["D"],
+            "batch_norm": True,
+        },
+    },
+    "vgg19": {
+        "encoder": VGGEncoder,
+        "pretrained_settings": pretrained_settings["vgg19"],
+        "params": {
+            "out_channels": (64, 128, 256, 512, 512, 512),
+            "config": cfg["E"],
+            "batch_norm": False,
+        },
+    },
+    "vgg19_bn": {
+        "encoder": VGGEncoder,
+        "pretrained_settings": pretrained_settings["vgg19_bn"],
+        "params": {
+            "out_channels": (64, 128, 256, 512, 512, 512),
+            "config": cfg["E"],
+            "batch_norm": True,
+        },
+    },
+}
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/encoders/xception.py b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/xception.py
new file mode 100644
index 0000000000000000000000000000000000000000..4527b5a6ece45b1207478dfdea248cd8e94c97b7
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/encoders/xception.py
@@ -0,0 +1,66 @@
+import re
+import torch.nn as nn
+
+from pretrainedmodels.models.xception import pretrained_settings
+from pretrainedmodels.models.xception import Xception
+
+from ._base import EncoderMixin
+
+
+class XceptionEncoder(Xception, EncoderMixin):
+
+    def __init__(self, out_channels, *args, depth=5, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        self._out_channels = out_channels
+        self._depth = depth
+        self._in_channels = 3
+
+        # modify padding to maintain output shape
+        self.conv1.padding = (1, 1)
+        self.conv2.padding = (1, 1)
+
+        del self.fc
+
+    def make_dilated(self, stage_list, dilation_list):
+        raise ValueError("Xception encoder does not support dilated mode "
+                         "due to pooling operation for downsampling!")
+
+    def get_stages(self):
+        return [
+            nn.Identity(),
+            nn.Sequential(self.conv1, self.bn1, self.relu, self.conv2, self.bn2, self.relu),
+            self.block1,
+            self.block2,
+            nn.Sequential(self.block3, self.block4, self.block5, self.block6, self.block7,
+                          self.block8, self.block9, self.block10, self.block11),
+            nn.Sequential(self.block12, self.conv3, self.bn3, self.relu, self.conv4, self.bn4),
+        ]
+
+    def forward(self, x):
+        stages = self.get_stages()
+
+        features = []
+        for i in range(self._depth + 1):
+            x = stages[i](x)
+            features.append(x)
+
+        return features
+
+    def load_state_dict(self, state_dict):
+        # remove linear
+        state_dict.pop('fc.bias')
+        state_dict.pop('fc.weight')
+
+        super().load_state_dict(state_dict)
+
+
+xception_encoders = {
+    'xception': {
+        'encoder': XceptionEncoder,
+        'pretrained_settings': pretrained_settings['xception'],
+        'params': {
+            'out_channels': (3, 64, 128, 256, 728, 2048),
+        }
+    },
+}
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/fpn/__init__.py b/segmentation_models_pytorch/segmentation_models_pytorch/fpn/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..62ba22d0eb9bcb08f237a010895e8503a2034655
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/fpn/__init__.py
@@ -0,0 +1 @@
+from .model import FPN
\ No newline at end of file
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/fpn/decoder.py b/segmentation_models_pytorch/segmentation_models_pytorch/fpn/decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..00f748e7146830dccfbe37a09eb41d016cd0f464
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/fpn/decoder.py
@@ -0,0 +1,119 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Conv3x3GNReLU(nn.Module):
+    def __init__(self, in_channels, out_channels, upsample=False):
+        super().__init__()
+        self.upsample = upsample
+        self.block = nn.Sequential(
+            nn.Conv2d(
+                in_channels, out_channels, (3, 3), stride=1, padding=1, bias=False
+            ),
+            nn.GroupNorm(32, out_channels),
+            nn.ReLU(inplace=True),
+        )
+
+    def forward(self, x):
+        x = self.block(x)
+        if self.upsample:
+            x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
+        return x
+
+
+class FPNBlock(nn.Module):
+    def __init__(self, pyramid_channels, skip_channels):
+        super().__init__()
+        self.skip_conv = nn.Conv2d(skip_channels, pyramid_channels, kernel_size=1)
+
+    def forward(self, x, skip=None):
+        x = F.interpolate(x, scale_factor=2, mode="nearest")
+        skip = self.skip_conv(skip)
+        x = x + skip
+        return x
+
+
+class SegmentationBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, n_upsamples=0):
+        super().__init__()
+
+        blocks = [Conv3x3GNReLU(in_channels, out_channels, upsample=bool(n_upsamples))]
+
+        if n_upsamples > 1:
+            for _ in range(1, n_upsamples):
+                blocks.append(Conv3x3GNReLU(out_channels, out_channels, upsample=True))
+
+        self.block = nn.Sequential(*blocks)
+
+    def forward(self, x):
+        return self.block(x)
+
+
+class MergeBlock(nn.Module):
+    def __init__(self, policy):
+        super().__init__()
+        if policy not in ["add", "cat"]:
+            raise ValueError(
+                "`merge_policy` must be one of: ['add', 'cat'], got {}".format(
+                    policy
+                )
+            )
+        self.policy = policy
+
+    def forward(self, x):
+        if self.policy == 'add':
+            return sum(x)
+        elif self.policy == 'cat':
+            return torch.cat(x, dim=1)
+        else:
+            raise ValueError(
+                "`merge_policy` must be one of: ['add', 'cat'], got {}".format(self.policy)
+            )
+
+
+class FPNDecoder(nn.Module):
+    def __init__(
+            self,
+            encoder_channels,
+            encoder_depth=5,
+            pyramid_channels=256,
+            segmentation_channels=128,
+            dropout=0.2,
+            merge_policy="add",
+    ):
+        super().__init__()
+
+        self.out_channels = segmentation_channels if merge_policy == "add" else segmentation_channels * 4
+        if encoder_depth < 3:
+            raise ValueError("Encoder depth for FPN decoder cannot be less than 3, got {}.".format(encoder_depth))
+
+        encoder_channels = encoder_channels[::-1]
+        encoder_channels = encoder_channels[:encoder_depth + 1]
+
+        self.p5 = nn.Conv2d(encoder_channels[0], pyramid_channels, kernel_size=1)
+        self.p4 = FPNBlock(pyramid_channels, encoder_channels[1])
+        self.p3 = FPNBlock(pyramid_channels, encoder_channels[2])
+        self.p2 = FPNBlock(pyramid_channels, encoder_channels[3])
+
+        self.seg_blocks = nn.ModuleList([
+            SegmentationBlock(pyramid_channels, segmentation_channels, n_upsamples=n_upsamples)
+            for n_upsamples in [3, 2, 1, 0]
+        ])
+
+        self.merge = MergeBlock(merge_policy)
+        self.dropout = nn.Dropout2d(p=dropout, inplace=True)
+
+    def forward(self, *features):
+        c2, c3, c4, c5 = features[-4:]
+
+        p5 = self.p5(c5)
+        p4 = self.p4(p5, c4)
+        p3 = self.p3(p4, c3)
+        p2 = self.p2(p3, c2)
+
+        feature_pyramid = [seg_block(p) for seg_block, p in zip(self.seg_blocks, [p5, p4, p3, p2])]
+        x = self.merge(feature_pyramid)
+        x = self.dropout(x)
+
+        return x
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/fpn/model.py b/segmentation_models_pytorch/segmentation_models_pytorch/fpn/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..d30297084387f483607e24684824d5c5e7fcd4cf
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/fpn/model.py
@@ -0,0 +1,95 @@
+from typing import Optional, Union
+from .decoder import FPNDecoder
+from ..base import SegmentationModel, SegmentationHead, ClassificationHead
+from ..encoders import get_encoder
+
+
+class FPN(SegmentationModel):
+    """FPN_ is a fully convolution neural network for image semantic segmentation.
+
+    Args:
+        encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone)
+            to extract features of different spatial resolution
+        encoder_depth: A number of stages used in encoder in range [3, 5]. Each stage generate features 
+            two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features
+            with shapes [(N, C, H, W),], for depth 1 - [(N, C, H, W), (N, C, H // 2, W // 2)] and so on).
+            Default is 5
+        encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and 
+            other pretrained weights (see table with available weights for each encoder_name)
+        decoder_pyramid_channels: A number of convolution filters in Feature Pyramid of FPN_
+        decoder_segmentation_channels: A number of convolution filters in segmentation blocks of FPN_
+        decoder_merge_policy: Determines how to merge pyramid features inside FPN. Available options are **add** and **cat**
+        decoder_dropout: Spatial dropout rate in range (0, 1) for feature pyramid in FPN_
+        in_channels: A number of input channels for the model, default is 3 (RGB images)
+        classes: A number of classes for output mask (or you can think as a number of channels of output mask)
+        activation: An activation function to apply after the final convolution layer.
+            Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, **callable** and **None**.
+            Default is **None**
+        upsampling: Final upsampling factor. Default is 4 to preserve input-output spatial shape identity
+        aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build 
+            on top of encoder if **aux_params** is not **None** (default). Supported params:
+                - classes (int): A number of classes
+                - pooling (str): One of "max", "avg". Default is "avg"
+                - dropout (float): Dropout factor in [0, 1)
+                - activation (str): An activation function to apply "sigmoid"/"softmax" (could be **None** to return logits)
+
+    Returns:
+        ``torch.nn.Module``: **FPN**
+
+    .. _FPN:
+        http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf
+
+    Reference:
+        http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf
+    """
+
+    def __init__(
+        self,
+        encoder_name: str = "resnet34",
+        encoder_depth: int = 5,
+        encoder_weights: Optional[str] = "imagenet",
+        decoder_pyramid_channels: int = 256,
+        decoder_segmentation_channels: int = 128,
+        decoder_merge_policy: str = "add",
+        decoder_dropout: float = 0.2,
+        in_channels: int = 3,
+        classes: int = 1,
+        activation: Optional[str] = None,
+        upsampling: int = 4,
+        aux_params: Optional[dict] = None,
+    ):
+        super().__init__()
+
+        self.encoder = get_encoder(
+            encoder_name,
+            in_channels=in_channels,
+            depth=encoder_depth,
+            weights=encoder_weights,
+        )
+
+        self.decoder = FPNDecoder(
+            encoder_channels=self.encoder.out_channels,
+            encoder_depth=encoder_depth,
+            pyramid_channels=decoder_pyramid_channels,
+            segmentation_channels=decoder_segmentation_channels,
+            dropout=decoder_dropout,
+            merge_policy=decoder_merge_policy,
+        )
+
+        self.segmentation_head = SegmentationHead(
+            in_channels=self.decoder.out_channels,
+            out_channels=classes,
+            activation=activation,
+            kernel_size=1,
+            upsampling=upsampling,
+        )
+
+        if aux_params is not None:
+            self.classification_head = ClassificationHead(
+                in_channels=self.encoder.out_channels[-1], **aux_params
+            )
+        else:
+            self.classification_head = None
+
+        self.name = "fpn-{}".format(encoder_name)
+        self.initialize()
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/linknet/__init__.py b/segmentation_models_pytorch/segmentation_models_pytorch/linknet/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a26d57217858cf07e82ca10dbbadae0a72e0998
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/linknet/__init__.py
@@ -0,0 +1 @@
+from .model import Linknet
\ No newline at end of file
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/linknet/decoder.py b/segmentation_models_pytorch/segmentation_models_pytorch/linknet/decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7e05ebe1ef3761c8ba00c70571931e33f46787f
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/linknet/decoder.py
@@ -0,0 +1,70 @@
+import torch.nn as nn
+
+from ..base import modules
+
+
+class TransposeX2(nn.Sequential):
+
+    def __init__(self, in_channels, out_channels, use_batchnorm=True):
+        super().__init__()
+        layers = [
+            nn.ConvTranspose2d(in_channels, out_channels, kernel_size=4, stride=2, padding=1),
+            nn.ReLU(inplace=True)
+        ]
+
+        if use_batchnorm:
+            layers.insert(1, nn.BatchNorm2d(out_channels))
+
+        super().__init__(*layers)
+
+
+class DecoderBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, use_batchnorm=True):
+        super().__init__()
+
+        self.block = nn.Sequential(
+            modules.Conv2dReLU(in_channels, in_channels // 4, kernel_size=1, use_batchnorm=use_batchnorm),
+            TransposeX2(in_channels // 4, in_channels // 4, use_batchnorm=use_batchnorm),
+            modules.Conv2dReLU(in_channels // 4, out_channels, kernel_size=1, use_batchnorm=use_batchnorm),
+        )
+
+    def forward(self, x, skip=None):
+        x = self.block(x)
+        if skip is not None:
+            x = x + skip
+        return x
+
+
+class LinknetDecoder(nn.Module):
+
+    def __init__(
+            self,
+            encoder_channels,
+            prefinal_channels=32,
+            n_blocks=5,
+            use_batchnorm=True,
+    ):
+        super().__init__()
+
+        encoder_channels = encoder_channels[1:]  # remove first skip
+        encoder_channels = encoder_channels[::-1]  # reverse channels to start from head of encoder
+
+        channels = list(encoder_channels) + [prefinal_channels]
+
+        self.blocks = nn.ModuleList([
+            DecoderBlock(channels[i], channels[i + 1], use_batchnorm=use_batchnorm)
+            for i in range(n_blocks)
+        ])
+
+    def forward(self, *features):
+        features = features[1:]  # remove first skip
+        features = features[::-1]  # reverse channels to start from head of encoder
+
+        x = features[0]
+        skips = features[1:]
+
+        for i, decoder_block in enumerate(self.blocks):
+            skip = skips[i] if i < len(skips) else None
+            x = decoder_block(x, skip)
+
+        return x
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/linknet/model.py b/segmentation_models_pytorch/segmentation_models_pytorch/linknet/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..dfe3d0a89a5da47178435642479beb634b83dade
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/linknet/model.py
@@ -0,0 +1,89 @@
+from typing import Optional, Union
+from .decoder import LinknetDecoder
+from ..base import SegmentationHead, SegmentationModel, ClassificationHead
+from ..encoders import get_encoder
+
+
+class Linknet(SegmentationModel):
+    """Linknet_ is a fully convolution neural network for image semantic segmentation. Consist of *encoder* 
+    and *decoder* parts connected with *skip connections*. Encoder extract features of different spatial 
+    resolution (skip connections) which are used by decoder to define accurate segmentation mask. Use *sum*
+    for fusing decoder blocks with skip connections.
+
+    Note:
+        This implementation by default has 4 skip connections (original - 3).
+
+    Args:
+        encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone)
+            to extract features of different spatial resolution
+        encoder_depth: A number of stages used in encoder in range [3, 5]. Each stage generate features 
+            two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features
+            with shapes [(N, C, H, W),], for depth 1 - [(N, C, H, W), (N, C, H // 2, W // 2)] and so on).
+            Default is 5
+        encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and 
+            other pretrained weights (see table with available weights for each encoder_name)
+        decoder_use_batchnorm: If **True**, BatchNorm2d layer between Conv2D and Activation layers
+            is used. If **"inplace"** InplaceABN will be used, allows to decrease memory consumption.
+            Available options are **True, False, "inplace"**
+        in_channels: A number of input channels for the model, default is 3 (RGB images)
+        classes: A number of classes for output mask (or you can think as a number of channels of output mask)
+        activation: An activation function to apply after the final convolution layer.
+            Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, **callable** and **None**.
+            Default is **None**
+        aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build 
+            on top of encoder if **aux_params** is not **None** (default). Supported params:
+                - classes (int): A number of classes
+                - pooling (str): One of "max", "avg". Default is "avg"
+                - dropout (float): Dropout factor in [0, 1)
+                - activation (str): An activation function to apply "sigmoid"/"softmax" (could be **None** to return logits)
+
+    Returns:
+        ``torch.nn.Module``: **Linknet**
+
+    .. _Linknet:
+        https://arxiv.org/abs/1707.03718
+
+    Reference:
+        https://arxiv.org/abs/1707.03718
+    """
+
+    def __init__(
+        self,
+        encoder_name: str = "resnet34",
+        encoder_depth: int = 5,
+        encoder_weights: Optional[str] = "imagenet",
+        decoder_use_batchnorm: bool = True,
+        in_channels: int = 3,
+        classes: int = 1,
+        activation: Optional[Union[str, callable]] = None,
+        aux_params: Optional[dict] = None,
+    ):
+        super().__init__()
+
+        self.encoder = get_encoder(
+            encoder_name,
+            in_channels=in_channels,
+            depth=encoder_depth,
+            weights=encoder_weights,
+        )
+
+        self.decoder = LinknetDecoder(
+            encoder_channels=self.encoder.out_channels,
+            n_blocks=encoder_depth,
+            prefinal_channels=32,
+            use_batchnorm=decoder_use_batchnorm,
+        )
+
+        self.segmentation_head = SegmentationHead(
+            in_channels=32, out_channels=classes, activation=activation, kernel_size=1
+        )
+
+        if aux_params is not None:
+            self.classification_head = ClassificationHead(
+                in_channels=self.encoder.out_channels[-1], **aux_params
+            )
+        else:
+            self.classification_head = None
+
+        self.name = "link-{}".format(encoder_name)
+        self.initialize()
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/losses/__init__.py b/segmentation_models_pytorch/segmentation_models_pytorch/losses/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..98e863d7c226b20768927fb118bc152442cc025b
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/losses/__init__.py
@@ -0,0 +1,8 @@
+from .constants import BINARY_MODE, MULTICLASS_MODE, MULTILABEL_MODE
+
+from .jaccard import JaccardLoss
+from .dice import DiceLoss
+from .focal import FocalLoss
+from .lovasz import LovaszLoss
+from .soft_bce import SoftBCEWithLogitsLoss
+from .soft_ce import SoftCrossEntropyLoss
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/losses/_functional.py b/segmentation_models_pytorch/segmentation_models_pytorch/losses/_functional.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec7de1e4e416d0f6cb981f351cb23292728e052d
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/losses/_functional.py
@@ -0,0 +1,254 @@
+import math
+import numpy as np
+
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+
+
+__all__ = [
+    "focal_loss_with_logits",
+    "softmax_focal_loss_with_logits",
+    "soft_jaccard_score",
+    "soft_dice_score",
+    "wing_loss",
+]
+
+
+def to_tensor(x, dtype=None) -> torch.Tensor:
+    if isinstance(x, torch.Tensor):
+        if dtype is not None:
+            x = x.type(dtype)
+        return x
+    if isinstance(x, np.ndarray):
+        x = torch.from_numpy(x)
+        if dtype is not None:
+            x = x.type(dtype)
+        return x
+    if isinstance(x, (list, tuple)):
+        x = np.ndarray(x)
+        x = torch.from_numpy(x)
+        if dtype is not None:
+            x = x.type(dtype)
+        return x
+
+
+def focal_loss_with_logits(
+    output: torch.Tensor,
+    target: torch.Tensor,
+    gamma: float = 2.0,
+    alpha: Optional[float] = 0.25,
+    reduction: str = "mean",
+    normalized: bool = False,
+    reduced_threshold: Optional[float] = None,
+    eps: float = 1e-6,
+) -> torch.Tensor:
+    """Compute binary focal loss between target and output logits.
+    See :class:`~pytorch_toolbelt.losses.FocalLoss` for details.
+
+    Args:
+        output: Tensor of arbitrary shape (predictions of the model)
+        target: Tensor of the same shape as input
+        gamma: Focal loss power factor
+        alpha: Weight factor to balance positive and negative samples. Alpha must be in [0...1] range,
+            high values will give more weight to positive class.
+        reduction (string, optional): Specifies the reduction to apply to the output:
+            'none' | 'mean' | 'sum' | 'batchwise_mean'. 'none': no reduction will be applied,
+            'mean': the sum of the output will be divided by the number of
+            elements in the output, 'sum': the output will be summed. Note: :attr:`size_average`
+            and :attr:`reduce` are in the process of being deprecated, and in the meantime,
+            specifying either of those two args will override :attr:`reduction`.
+            'batchwise_mean' computes mean loss per sample in batch. Default: 'mean'
+        normalized (bool): Compute normalized focal loss (https://arxiv.org/pdf/1909.07829.pdf).
+        reduced_threshold (float, optional): Compute reduced focal loss (https://arxiv.org/abs/1903.01347).
+
+    References:
+        https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/loss/losses.py
+    """
+    target = target.type(output.type())
+
+    logpt = F.binary_cross_entropy_with_logits(output, target, reduction="none")
+    pt = torch.exp(-logpt)
+
+    # compute the loss
+    if reduced_threshold is None:
+        focal_term = (1.0 - pt).pow(gamma)
+    else:
+        focal_term = ((1.0 - pt) / reduced_threshold).pow(gamma)
+        focal_term[pt < reduced_threshold] = 1
+
+    loss = focal_term * logpt
+
+    if alpha is not None:
+        loss *= alpha * target + (1 - alpha) * (1 - target)
+
+    if normalized:
+        norm_factor = focal_term.sum().clamp_min(eps)
+        loss /= norm_factor
+
+    if reduction == "mean":
+        loss = loss.mean()
+    if reduction == "sum":
+        loss = loss.sum()
+    if reduction == "batchwise_mean":
+        loss = loss.sum(0)
+
+    return loss
+
+
+def softmax_focal_loss_with_logits(
+    output: torch.Tensor,
+    target: torch.Tensor,
+    gamma: float = 2.0,
+    reduction="mean",
+    normalized=False,
+    reduced_threshold: Optional[float] = None,
+    eps: float = 1e-6,
+) -> torch.Tensor:
+    """Softmax version of focal loss between target and output logits.
+    See :class:`~pytorch_toolbelt.losses.FocalLoss` for details.
+
+    Args:
+        output: Tensor of shape [B, C, *] (Similar to nn.CrossEntropyLoss)
+        target: Tensor of shape [B, *] (Similar to nn.CrossEntropyLoss)
+        reduction (string, optional): Specifies the reduction to apply to the output:
+            'none' | 'mean' | 'sum' | 'batchwise_mean'. 'none': no reduction will be applied,
+            'mean': the sum of the output will be divided by the number of
+            elements in the output, 'sum': the output will be summed. Note: :attr:`size_average`
+            and :attr:`reduce` are in the process of being deprecated, and in the meantime,
+            specifying either of those two args will override :attr:`reduction`.
+            'batchwise_mean' computes mean loss per sample in batch. Default: 'mean'
+        normalized (bool): Compute normalized focal loss (https://arxiv.org/pdf/1909.07829.pdf).
+        reduced_threshold (float, optional): Compute reduced focal loss (https://arxiv.org/abs/1903.01347).
+    """
+    log_softmax = F.log_softmax(output, dim=1)
+
+    loss = F.nll_loss(log_softmax, target, reduction="none")
+    pt = torch.exp(-loss)
+
+    # compute the loss
+    if reduced_threshold is None:
+        focal_term = (1.0 - pt).pow(gamma)
+    else:
+        focal_term = ((1.0 - pt) / reduced_threshold).pow(gamma)
+        focal_term[pt < reduced_threshold] = 1
+
+    loss = focal_term * loss
+
+    if normalized:
+        norm_factor = focal_term.sum().clamp_min(eps)
+        loss = loss / norm_factor
+
+    if reduction == "mean":
+        loss = loss.mean()
+    if reduction == "sum":
+        loss = loss.sum()
+    if reduction == "batchwise_mean":
+        loss = loss.sum(0)
+
+    return loss
+
+
+def soft_jaccard_score(
+    output: torch.Tensor, target: torch.Tensor, smooth: float = 0.0, eps: float = 1e-7, dims=None
+) -> torch.Tensor:
+    assert output.size() == target.size()
+    if dims is not None:
+        intersection = torch.sum(output * target, dim=dims)
+        cardinality = torch.sum(output + target, dim=dims)
+    else:
+        intersection = torch.sum(output * target)
+        cardinality = torch.sum(output + target)
+
+    union = cardinality - intersection
+    jaccard_score = (intersection + smooth) / (union + smooth).clamp_min(eps)
+    return jaccard_score
+
+
+def soft_dice_score(
+    output: torch.Tensor, target: torch.Tensor, smooth: float = 0.0, eps: float = 1e-7, dims=None
+) -> torch.Tensor:
+    assert output.size() == target.size()
+    if dims is not None:
+        intersection = torch.sum(output * target, dim=dims)
+        cardinality = torch.sum(output + target, dim=dims)
+    else:
+        intersection = torch.sum(output * target)
+        cardinality = torch.sum(output + target)
+    dice_score = (2.0 * intersection + smooth) / (cardinality + smooth).clamp_min(eps)
+    return dice_score
+
+
+def wing_loss(output: torch.Tensor, target: torch.Tensor, width=5, curvature=0.5, reduction="mean"):
+    """
+    https://arxiv.org/pdf/1711.06753.pdf
+    :param output:
+    :param target:
+    :param width:
+    :param curvature:
+    :param reduction:
+    :return:
+    """
+    diff_abs = (target - output).abs()
+    loss = diff_abs.clone()
+
+    idx_smaller = diff_abs < width
+    idx_bigger = diff_abs >= width
+
+    loss[idx_smaller] = width * torch.log(1 + diff_abs[idx_smaller] / curvature)
+
+    C = width - width * math.log(1 + width / curvature)
+    loss[idx_bigger] = loss[idx_bigger] - C
+
+    if reduction == "sum":
+        loss = loss.sum()
+
+    if reduction == "mean":
+        loss = loss.mean()
+
+    return loss
+
+
+def label_smoothed_nll_loss(
+    lprobs: torch.Tensor, target: torch.Tensor, epsilon: float, ignore_index=None, reduction="mean", dim=-1
+) -> torch.Tensor:
+    """
+    Source: https://github.com/pytorch/fairseq/blob/master/fairseq/criterions/label_smoothed_cross_entropy.py
+    :param lprobs: Log-probabilities of predictions (e.g after log_softmax)
+    :param target:
+    :param epsilon:
+    :param ignore_index:
+    :param reduction:
+    :return:
+    """
+    if target.dim() == lprobs.dim() - 1:
+        target = target.unsqueeze(dim)
+
+    if ignore_index is not None:
+        pad_mask = target.eq(ignore_index)
+        target = target.masked_fill(pad_mask, 0)
+        nll_loss = -lprobs.gather(dim=dim, index=target)
+        smooth_loss = -lprobs.sum(dim=dim, keepdim=True)
+
+        # nll_loss.masked_fill_(pad_mask, 0.0)
+        # smooth_loss.masked_fill_(pad_mask, 0.0)
+        nll_loss = nll_loss.masked_fill(pad_mask, 0.0)
+        smooth_loss = smooth_loss.masked_fill(pad_mask, 0.0)
+    else:
+        nll_loss = -lprobs.gather(dim=dim, index=target)
+        smooth_loss = -lprobs.sum(dim=dim, keepdim=True)
+
+        nll_loss = nll_loss.squeeze(dim)
+        smooth_loss = smooth_loss.squeeze(dim)
+
+    if reduction == "sum":
+        nll_loss = nll_loss.sum()
+        smooth_loss = smooth_loss.sum()
+    if reduction == "mean":
+        nll_loss = nll_loss.mean()
+        smooth_loss = smooth_loss.mean()
+
+    eps_i = epsilon / lprobs.size(dim)
+    loss = (1.0 - epsilon) * nll_loss + eps_i * smooth_loss
+    return loss
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/losses/constants.py b/segmentation_models_pytorch/segmentation_models_pytorch/losses/constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..9640190a4a83044964fec261ca597bd0cc864288
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/losses/constants.py
@@ -0,0 +1,18 @@
+#: Loss binary mode suppose you are solving binary segmentation task.
+#: That mean yor have only one class which pixels are labled as **1**,
+#: the rest pixels are background and labeled as **0**.
+#: Target mask shape - (N, H, W), model output mask shape (N, 1, H, W).
+BINARY_MODE: str = "binary"
+
+#: Loss multiclass mode suppose you are solving multi-**class** segmentation task.
+#: That mean you have *C = 1..N* classes which have unique label values,
+#: classes are mutually exclusive and all pixels are labeled with theese values.
+#: Target mask shape - (N, H, W), model output mask shape (N, C, H, W).  
+MULTICLASS_MODE: str = "multiclass"
+
+#: Loss multilabel mode suppose you are solving multi-**label** segmentation task.
+#: That mean you have *C = 1..N* classes which pixels are labeled as **1**,
+#: classes are not mutually exclusive and each class have its own *channel*,
+#: pixels in each channel which are not belong to class labeled as **0**.
+#: Target mask shape - (N, C, H, W), model output mask shape (N, C, H, W).
+MULTILABEL_MODE: str = "multilabel"
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/losses/dice.py b/segmentation_models_pytorch/segmentation_models_pytorch/losses/dice.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e853d00336008a824bb2d3820c3e1be8dfa9001
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/losses/dice.py
@@ -0,0 +1,107 @@
+from typing import Optional, List
+
+import torch
+import torch.nn.functional as F
+from torch.nn.modules.loss import _Loss
+from ._functional import soft_dice_score, to_tensor
+from .constants import BINARY_MODE, MULTICLASS_MODE, MULTILABEL_MODE
+
+__all__ = ["DiceLoss"]
+
+
+class DiceLoss(_Loss):
+
+    def __init__(
+        self,
+        mode: str,
+        classes: Optional[List[int]] = None,
+        log_loss: bool = False,
+        from_logits: bool = True,
+        smooth: float = 0.0,
+        ignore_index: Optional[int] = None,
+        eps: float = 1e-7,
+    ):
+        """Implementation of Dice loss for image segmentation task.
+        It supports binary, multiclass and multilabel cases
+
+        Args:
+            mode: Loss mode 'binary', 'multiclass' or 'multilabel'
+            classes:  List of classes that contribute in loss computation. By default, all channels are included.
+            log_loss: If True, loss computed as `- log(dice_coeff)`, otherwise `1 - dice_coeff`
+            from_logits: If True, assumes input is raw logits
+            smooth: Smoothness constant for dice coefficient (a)
+            ignore_index: Label that indicates ignored pixels (does not contribute to loss)
+            eps: A small epsilon for numerical stability to avoid zero division error 
+                (denominator will be always greater or equal to eps)
+
+        Shape
+             - **y_pred** - torch.Tensor of shape (N, C, H, W)
+             - **y_true** - torch.Tensor of shape (N, H, W) or (N, C, H, W)
+
+        Reference
+            https://github.com/BloodAxe/pytorch-toolbelt
+        """
+        assert mode in {BINARY_MODE, MULTILABEL_MODE, MULTICLASS_MODE}
+        super(DiceLoss, self).__init__()
+        self.mode = mode
+        if classes is not None:
+            assert mode != BINARY_MODE, "Masking classes is not supported with mode=binary"
+            classes = to_tensor(classes, dtype=torch.long)
+
+        self.classes = classes
+        self.from_logits = from_logits
+        self.smooth = smooth
+        self.eps = eps
+        self.log_loss = log_loss
+
+    def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor:
+
+        assert y_true.size(0) == y_pred.size(0)
+
+        if self.from_logits:
+            # Apply activations to get [0..1] class probabilities
+            # Using Log-Exp as this gives more numerically stable result and does not cause vanishing gradient on
+            # extreme values 0 and 1
+            if self.mode == MULTICLASS_MODE:
+                y_pred = y_pred.log_softmax(dim=1).exp()
+            else:
+                y_pred = F.logsigmoid(y_pred).exp()
+
+        bs = y_true.size(0)
+        num_classes = y_pred.size(1)
+        dims = (0, 2)
+
+        if self.mode == BINARY_MODE:
+            y_true = y_true.view(bs, 1, -1)
+            y_pred = y_pred.view(bs, 1, -1)
+
+        if self.mode == MULTICLASS_MODE:
+            y_true = y_true.view(bs, -1)
+            y_pred = y_pred.view(bs, num_classes, -1)
+
+            y_true = F.one_hot(y_true, num_classes)  # N,H*W -> N,H*W, C
+            y_true = y_true.permute(0, 2, 1)  # H, C, H*W
+
+        if self.mode == MULTILABEL_MODE:
+            y_true = y_true.view(bs, num_classes, -1)
+            y_pred = y_pred.view(bs, num_classes, -1)
+
+        scores = soft_dice_score(y_pred, y_true.type_as(y_pred), smooth=self.smooth, eps=self.eps, dims=dims)
+
+        if self.log_loss:
+            loss = -torch.log(scores.clamp_min(self.eps))
+        else:
+            loss = 1.0 - scores
+
+        # Dice loss is undefined for non-empty classes
+        # So we zero contribution of channel that does not have true pixels
+        # NOTE: A better workaround would be to use loss term `mean(y_pred)`
+        # for this case, however it will be a modified jaccard loss
+
+        mask = y_true.sum(dims) > 0
+        loss *= mask.to(loss.dtype)
+
+        if self.classes is not None:
+            loss = loss[self.classes]
+
+        return loss.mean()
\ No newline at end of file
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/losses/focal.py b/segmentation_models_pytorch/segmentation_models_pytorch/losses/focal.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5c9a670c4cb61fea1b0f4a3bab0e230ff905a45
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/losses/focal.py
@@ -0,0 +1,90 @@
+from typing import Optional
+from functools import partial
+
+import torch
+from torch.nn.modules.loss import _Loss
+from ._functional import focal_loss_with_logits
+from .constants import BINARY_MODE, MULTICLASS_MODE, MULTILABEL_MODE
+
+__all__ = ["FocalLoss"]
+
+
+class FocalLoss(_Loss):
+
+    def __init__(
+        self,
+        mode: str,
+        alpha: Optional[float] = None,
+        gamma: Optional[float] = 2.,
+        ignore_index: Optional[int] = None, 
+        reduction: Optional[str] = "mean",
+        normalized: bool = False,
+        reduced_threshold: Optional[float] = None,
+    ):
+        """Compute Focal loss
+
+        Args:
+            mode: Loss mode 'binary', 'multiclass' or 'multilabel'
+            alpha: Prior probability of having positive value in target.
+            gamma: Power factor for dampening weight (focal strength).
+            ignore_index: If not None, targets may contain values to be ignored.
+                Target values equal to ignore_index will be ignored from loss computation.
+            normalized: Compute normalized focal loss (https://arxiv.org/pdf/1909.07829.pdf).
+            reduced_threshold: Switch to reduced focal loss. Note, when using this mode you should use `reduction="sum"`.
+        
+        Shape
+             - **y_pred** - torch.Tensor of shape (N, C, H, W)
+             - **y_true** - torch.Tensor of shape (N, H, W) or (N, C, H, W)
+
+        Reference
+            https://github.com/BloodAxe/pytorch-toolbelt
+
+        """
+        assert mode in {BINARY_MODE, MULTILABEL_MODE, MULTICLASS_MODE}
+        super().__init__()
+
+        self.mode = mode
+        self.ignore_index = ignore_index
+        self.focal_loss_fn = partial(
+            focal_loss_with_logits,
+            alpha=alpha,
+            gamma=gamma,
+            reduced_threshold=reduced_threshold,
+            reduction=reduction,
+            normalized=normalized,
+        )
+
+    def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor:
+
+        if self.mode in {BINARY_MODE, MULTILABEL_MODE}:
+            y_true = y_true.view(-1)
+            y_pred = y_pred.view(-1)
+
+            if self.ignore_index is not None:
+                # Filter predictions with ignore label from loss computation
+                not_ignored = y_true != self.ignore_index
+                y_pred = y_pred[not_ignored]
+                y_true = y_true[not_ignored]
+
+            loss = self.focal_loss_fn(y_pred, y_true)
+
+        elif self.mode == MULTICLASS_MODE:
+
+            num_classes = y_pred.size(1)
+            loss = 0
+
+            # Filter anchors with -1 label from loss computation
+            if self.ignore_index is not None:
+                not_ignored = y_true != self.ignore_index
+
+            for cls in range(num_classes):
+                cls_y_true = (y_true == cls).long()
+                cls_y_pred = y_pred[:, cls, ...]
+
+                if self.ignore_index is not None:
+                    cls_y_true = cls_y_true[not_ignored]
+                    cls_y_pred = cls_y_pred[not_ignored]
+
+                loss += self.focal_loss_fn(cls_y_pred, cls_y_true)
+
+        return loss
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/losses/jaccard.py b/segmentation_models_pytorch/segmentation_models_pytorch/losses/jaccard.py
new file mode 100644
index 0000000000000000000000000000000000000000..33b776cf15bb8ab7e4c0d931e571b8ae0f95bcbc
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/losses/jaccard.py
@@ -0,0 +1,107 @@
+from typing import Optional, List
+
+import torch
+import torch.nn.functional as F
+from torch.nn.modules.loss import _Loss
+from ._functional import soft_jaccard_score, to_tensor
+from .constants import BINARY_MODE, MULTICLASS_MODE, MULTILABEL_MODE
+
+__all__ = ["JaccardLoss"]
+
+
+class JaccardLoss(_Loss):
+
+    def __init__(
+        self,
+        mode: str,
+        classes: Optional[List[int]] = None,
+        log_loss: bool = False,
+        from_logits: bool = True,
+        smooth: float = 0.,
+        eps: float = 1e-7,
+    ):
+        """Implementation of Jaccard loss for image segmentation task.
+        It supports binary, multiclass and multilabel cases
+
+        Args:
+            mode: Loss mode 'binary', 'multiclass' or 'multilabel'
+            classes:  List of classes that contribute in loss computation. By default, all channels are included.
+            log_loss: If True, loss computed as `- log(jaccard_coeff)`, otherwise `1 - jaccard_coeff`
+            from_logits: If True, assumes input is raw logits
+            smooth: Smoothness constant for dice coefficient
+            ignore_index: Label that indicates ignored pixels (does not contribute to loss)
+            eps: A small epsilon for numerical stability to avoid zero division error 
+                (denominator will be always greater or equal to eps)
+
+        Shape
+             - **y_pred** - torch.Tensor of shape (N, C, H, W)
+             - **y_true** - torch.Tensor of shape (N, H, W) or (N, C, H, W)
+
+        Reference
+            https://github.com/BloodAxe/pytorch-toolbelt
+        """
+        assert mode in {BINARY_MODE, MULTILABEL_MODE, MULTICLASS_MODE}
+        super(JaccardLoss, self).__init__()
+
+        self.mode = mode
+        if classes is not None:
+            assert mode != BINARY_MODE, "Masking classes is not supported with mode=binary"
+            classes = to_tensor(classes, dtype=torch.long)
+
+        self.classes = classes
+        self.from_logits = from_logits
+        self.smooth = smooth
+        self.eps = eps
+        self.log_loss = log_loss
+
+    def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor:
+
+        assert y_true.size(0) == y_pred.size(0)
+
+        if self.from_logits:
+            # Apply activations to get [0..1] class probabilities
+            # Using Log-Exp as this gives more numerically stable result and does not cause vanishing gradient on
+            # extreme values 0 and 1
+            if self.mode == MULTICLASS_MODE:
+                y_pred = y_pred.log_softmax(dim=1).exp()
+            else:
+                y_pred = F.logsigmoid(y_pred).exp()
+
+        bs = y_true.size(0)
+        num_classes = y_pred.size(1)
+        dims = (0, 2)
+
+        if self.mode == BINARY_MODE:
+            y_true = y_true.view(bs, 1, -1)
+            y_pred = y_pred.view(bs, 1, -1)
+
+        if self.mode == MULTICLASS_MODE:
+            y_true = y_true.view(bs, -1)
+            y_pred = y_pred.view(bs, num_classes, -1)
+
+            y_true = F.one_hot(y_true, num_classes)  # N,H*W -> N,H*W, C
+            y_true = y_true.permute(0, 2, 1)  # H, C, H*W
+
+        if self.mode == MULTILABEL_MODE:
+            y_true = y_true.view(bs, num_classes, -1)
+            y_pred = y_pred.view(bs, num_classes, -1)
+
+        scores = soft_jaccard_score(y_pred, y_true.type(y_pred.dtype), smooth=self.smooth, eps=self.eps, dims=dims)
+
+        if self.log_loss:
+            loss = -torch.log(scores.clamp_min(self.eps))
+        else:
+            loss = 1.0 - scores
+
+        # IoU loss is defined for non-empty classes
+        # So we zero contribution of channel that does not have true pixels
+        # NOTE: A better workaround would be to use loss term `mean(y_pred)`
+        # for this case, however it will be a modified jaccard loss
+
+        mask = y_true.sum(dims) > 0
+        loss *= mask.float()
+
+        if self.classes is not None:
+            loss = loss[self.classes]
+
+        return loss.mean()
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/losses/lovasz.py b/segmentation_models_pytorch/segmentation_models_pytorch/losses/lovasz.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7615e38ee83b381a4bd4051991ac6c5b0caeb7e
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/losses/lovasz.py
@@ -0,0 +1,228 @@
+"""
+Lovasz-Softmax and Jaccard hinge loss in PyTorch
+Maxim Berman 2018 ESAT-PSI KU Leuven (MIT License)
+"""
+
+from __future__ import print_function, division
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from torch.autograd import Variable
+from torch.nn.modules.loss import _Loss
+from .constants import BINARY_MODE, MULTICLASS_MODE, MULTILABEL_MODE
+
+try:
+    from itertools import ifilterfalse
+except ImportError:  # py3k
+    from itertools import filterfalse as ifilterfalse
+
+__all__ = ["LovaszLoss"]
+
+
+def _lovasz_grad(gt_sorted):
+    """Compute gradient of the Lovasz extension w.r.t sorted errors
+    See Alg. 1 in paper
+    """
+    p = len(gt_sorted)
+    gts = gt_sorted.sum()
+    intersection = gts - gt_sorted.float().cumsum(0)
+    union = gts + (1 - gt_sorted).float().cumsum(0)
+    jaccard = 1.0 - intersection / union
+    if p > 1:  # cover 1-pixel case
+        jaccard[1:p] = jaccard[1:p] - jaccard[0:-1]
+    return jaccard
+
+
+def _lovasz_hinge(logits, labels, per_image=True, ignore=None):
+    """
+    Binary Lovasz hinge loss
+        logits: [B, H, W] Variable, logits at each pixel (between -infinity and +infinity)
+        labels: [B, H, W] Tensor, binary ground truth masks (0 or 1)
+        per_image: compute the loss per image instead of per batch
+        ignore: void class id
+    """
+    if per_image:
+        loss = mean(
+            _lovasz_hinge_flat(*_flatten_binary_scores(log.unsqueeze(0), lab.unsqueeze(0), ignore))
+            for log, lab in zip(logits, labels)
+        )
+    else:
+        loss = _lovasz_hinge_flat(*_flatten_binary_scores(logits, labels, ignore))
+    return loss
+
+
+def _lovasz_hinge_flat(logits, labels):
+    """Binary Lovasz hinge loss
+    Args:
+        logits: [P] Variable, logits at each prediction (between -infinity and +infinity)
+        labels: [P] Tensor, binary ground truth labels (0 or 1)
+        ignore: label to ignore
+    """
+    if len(labels) == 0:
+        # only void pixels, the gradients should be 0
+        return logits.sum() * 0.0
+    signs = 2.0 * labels.float() - 1.0
+    errors = 1.0 - logits * Variable(signs)
+    errors_sorted, perm = torch.sort(errors, dim=0, descending=True)
+    perm = perm.data
+    gt_sorted = labels[perm]
+    grad = _lovasz_grad(gt_sorted)
+    loss = torch.dot(F.relu(errors_sorted), Variable(grad))
+    return loss
+
+
+def _flatten_binary_scores(scores, labels, ignore=None):
+    """Flattens predictions in the batch (binary case)
+    Remove labels equal to 'ignore'
+    """
+    scores = scores.view(-1)
+    labels = labels.view(-1)
+    if ignore is None:
+        return scores, labels
+    valid = labels != ignore
+    vscores = scores[valid]
+    vlabels = labels[valid]
+    return vscores, vlabels
+
+
+# --------------------------- MULTICLASS LOSSES ---------------------------
+
+
+def _lovasz_softmax(probas, labels, classes="present", per_image=False, ignore=None):
+    """Multi-class Lovasz-Softmax loss
+    Args:
+        @param probas: [B, C, H, W] Variable, class probabilities at each prediction (between 0 and 1).
+        Interpreted as binary (sigmoid) output with outputs of size [B, H, W].
+        @param labels: [B, H, W] Tensor, ground truth labels (between 0 and C - 1)
+        @param classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average.
+        @param per_image: compute the loss per image instead of per batch
+        @param ignore: void class labels
+    """
+    if per_image:
+        loss = mean(
+            _lovasz_softmax_flat(*_flatten_probas(prob.unsqueeze(0), lab.unsqueeze(0), ignore), classes=classes)
+            for prob, lab in zip(probas, labels)
+        )
+    else:
+        loss = _lovasz_softmax_flat(*_flatten_probas(probas, labels, ignore), classes=classes)
+    return loss
+
+
+def _lovasz_softmax_flat(probas, labels, classes="present"):
+    """Multi-class Lovasz-Softmax loss
+    Args:
+        @param probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1)
+        @param labels: [P] Tensor, ground truth labels (between 0 and C - 1)
+        @param classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average.
+    """
+    if probas.numel() == 0:
+        # only void pixels, the gradients should be 0
+        return probas * 0.0
+    C = probas.size(1)
+    losses = []
+    class_to_sum = list(range(C)) if classes in ["all", "present"] else classes
+    for c in class_to_sum:
+        fg = (labels == c).type_as(probas)  # foreground for class c
+        if classes == "present" and fg.sum() == 0:
+            continue
+        if C == 1:
+            if len(classes) > 1:
+                raise ValueError("Sigmoid output possible only with 1 class")
+            class_pred = probas[:, 0]
+        else:
+            class_pred = probas[:, c]
+        errors = (fg - class_pred).abs()
+        errors_sorted, perm = torch.sort(errors, 0, descending=True)
+        perm = perm.data
+        fg_sorted = fg[perm]
+        losses.append(torch.dot(errors_sorted, _lovasz_grad(fg_sorted)))
+    return mean(losses)
+
+
+def _flatten_probas(probas, labels, ignore=None):
+    """Flattens predictions in the batch
+    """
+    if probas.dim() == 3:
+        # assumes output of a sigmoid layer
+        B, H, W = probas.size()
+        probas = probas.view(B, 1, H, W)
+
+    C = probas.size(1)
+    probas = torch.movedim(probas, 0, -1)  # [B, C, Di, Dj, Dk...] -> [B, C, Di...Dk, C]
+    probas = probas.contiguous().view(-1, C)  # [P, C]
+
+    labels = labels.view(-1)
+    if ignore is None:
+        return probas, labels
+    valid = labels != ignore
+    vprobas = probas[valid]
+    vlabels = labels[valid]
+    return vprobas, vlabels
+
+
+# --------------------------- HELPER FUNCTIONS ---------------------------
+def isnan(x):
+    return x != x
+
+
+def mean(values, ignore_nan=False, empty=0):
+    """Nanmean compatible with generators.
+    """
+    values = iter(values)
+    if ignore_nan:
+        values = ifilterfalse(isnan, values)
+    try:
+        n = 1
+        acc = next(values)
+    except StopIteration:
+        if empty == "raise":
+            raise ValueError("Empty mean")
+        return empty
+    for n, v in enumerate(values, 2):
+        acc += v
+    if n == 1:
+        return acc
+    return acc / n
+
+
+class LovaszLoss(_Loss):
+    def __init__(
+        self,
+        mode: str,
+        per_image: bool = False,
+        ignore_index: Optional[int] = None,
+        from_logits: bool = True,
+    ):
+        """Implementation of Lovasz loss for image segmentation task.
+        It supports binary, multiclass and multilabel cases
+
+        Args:
+            mode: Loss mode 'binary', 'multiclass' or 'multilabel'
+            ignore_index: Label that indicates ignored pixels (does not contribute to loss)
+            per_image: If True loss computed per each image and then averaged, else computed per whole batch
+        
+        Shape
+             - **y_pred** - torch.Tensor of shape (N, C, H, W)
+             - **y_true** - torch.Tensor of shape (N, H, W) or (N, C, H, W)
+
+        Reference
+            https://github.com/BloodAxe/pytorch-toolbelt
+        """
+        assert mode in {BINARY_MODE, MULTILABEL_MODE, MULTICLASS_MODE}
+        super().__init__()
+
+        self.mode = mode
+        self.ignore_index = ignore_index
+        self.per_image = per_image
+
+    def forward(self, y_pred, y_true):
+
+        if self.mode in {BINARY_MODE, MULTILABEL_MODE}:
+            loss = _lovasz_hinge(y_pred, y_true, per_image=self.per_image, ignore=self.ignore_index)
+        elif self.mode == MULTICLASS_MODE:
+            y_pred = y_pred.softmax(dim=1)
+            loss = _lovasz_softmax(y_pred, y_true, per_image=self.per_image, ignore=self.ignore_index)    
+        else:
+            raise ValueError("Wrong mode {}.".format(self.mode))
+        return loss
\ No newline at end of file
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/losses/soft_bce.py b/segmentation_models_pytorch/segmentation_models_pytorch/losses/soft_bce.py
new file mode 100644
index 0000000000000000000000000000000000000000..b48d67608fd4f2cd565d382434968777fb263bd4
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/losses/soft_bce.py
@@ -0,0 +1,72 @@
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn, Tensor
+
+__all__ = ["SoftBCEWithLogitsLoss"]
+
+
+class SoftBCEWithLogitsLoss(nn.Module):
+
+    __constants__ = ["weight", "pos_weight", "reduction", "ignore_index", "smooth_factor"]
+
+    def __init__(
+        self,
+        weight: Optional[torch.Tensor] = None,
+        ignore_index: Optional[int] = -100,
+        reduction: str = "mean",
+        smooth_factor: Optional[float] = None,
+        pos_weight: Optional[torch.Tensor] = None,
+    ):
+        """Drop-in replacement for torch.nn.BCEWithLogitsLoss with few additions: ignore_index and label_smoothing
+        
+        Args:
+            ignore_index: Specifies a target value that is ignored and does not contribute to the input gradient. 
+            smooth_factor: Factor to smooth target (e.g. if smooth_factor=0.1 then [1, 0, 1] -> [0.9, 0.1, 0.9])
+        
+        Shape
+             - **y_pred** - torch.Tensor of shape NxCxHxW
+             - **y_true** - torch.Tensor of shape NxHxW or Nx1xHxW
+
+        Reference
+            https://github.com/BloodAxe/pytorch-toolbelt
+
+        """
+        super().__init__()
+        self.ignore_index = ignore_index
+        self.reduction = reduction
+        self.smooth_factor = smooth_factor
+        self.register_buffer("weight", weight)
+        self.register_buffer("pos_weight", pos_weight)
+
+    def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            y_pred: torch.Tensor of shape (N, C, H, W)
+            y_true: torch.Tensor of shape (N, H, W)  or (N, 1, H, W)
+        
+        Returns:
+            loss: torch.Tensor
+        """
+
+        if self.smooth_factor is not None:
+            soft_targets = (1 - y_true) * self.smooth_factor + y_true * (1 - self.smooth_factor)
+        else:
+            soft_targets = y_true
+
+        loss = F.binary_cross_entropy_with_logits(
+            y_pred, soft_targets, self.weight, pos_weight=self.pos_weight, reduction="none"
+        )
+
+        if self.ignore_index is not None:
+            not_ignored_mask = y_true != self.ignore_index
+            loss *= not_ignored_mask.type_as(loss)
+
+        if self.reduction == "mean":
+            loss = loss.mean()
+
+        if self.reduction == "sum":
+            loss = loss.sum()
+
+        return loss
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/losses/soft_ce.py b/segmentation_models_pytorch/segmentation_models_pytorch/losses/soft_ce.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd196104dbef2c2faa48118c1512a3e6c7cb805b
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/losses/soft_ce.py
@@ -0,0 +1,48 @@
+from typing import Optional
+from torch import nn, Tensor
+import torch
+import torch.nn.functional as F
+from ._functional import label_smoothed_nll_loss
+
+__all__ = ["SoftCrossEntropyLoss"]
+
+
+class SoftCrossEntropyLoss(nn.Module):
+
+    __constants__ = ["reduction", "ignore_index", "smooth_factor"]
+
+    def __init__(
+        self,
+        reduction: str = "mean",
+        smooth_factor: Optional[float] = None,
+        ignore_index: Optional[int] = -100,
+        dim: int = 1,
+    ):
+        """Drop-in replacement for torch.nn.CrossEntropyLoss with label_smoothing
+        
+        Args:
+            smooth_factor: Factor to smooth target (e.g. if smooth_factor=0.1 then [1, 0, 0] -> [0.9, 0.05, 0.05])
+        
+        Shape
+             - **y_pred** - torch.Tensor of shape (N, C, H, W)
+             - **y_true** - torch.Tensor of shape (N, H, W)
+
+        Reference
+            https://github.com/BloodAxe/pytorch-toolbelt
+        """
+        super().__init__()
+        self.smooth_factor = smooth_factor
+        self.ignore_index = ignore_index
+        self.reduction = reduction
+        self.dim = dim
+
+    def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor:
+        log_prob = F.log_softmax(y_pred, dim=self.dim)
+        return label_smoothed_nll_loss(
+            log_prob,
+            y_true,
+            epsilon=self.smooth_factor,
+            ignore_index=self.ignore_index,
+            reduction=self.reduction,
+            dim=self.dim,
+        )
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/manet/__init__.py b/segmentation_models_pytorch/segmentation_models_pytorch/manet/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3bdc788d300d6aa95b3894f2bba78214fd437e3
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/manet/__init__.py
@@ -0,0 +1 @@
+from .model import MAnet
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/manet/decoder.py b/segmentation_models_pytorch/segmentation_models_pytorch/manet/decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d587671ea32170d744b182c2f926f383a569177
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/manet/decoder.py
@@ -0,0 +1,188 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from ..base import modules as md
+
+
+class PAB(nn.Module):
+    def __init__(self, in_channels, out_channels, pab_channels=64):
+        super(PAB, self).__init__()
+        # Series of 1x1 conv to generate attention feature maps
+        self.pab_channels = pab_channels
+        self.in_channels = in_channels
+        self.top_conv = nn.Conv2d(in_channels, pab_channels, kernel_size=1)
+        self.center_conv = nn.Conv2d(in_channels, pab_channels, kernel_size=1)
+        self.bottom_conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1)
+        self.map_softmax = nn.Softmax(dim=1)
+        self.out_conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1)
+
+    def forward(self, x):
+        bsize = x.size()[0]
+        h = x.size()[2]
+        w = x.size()[3]
+        x_top = self.top_conv(x)
+        x_center = self.center_conv(x)
+        x_bottom = self.bottom_conv(x)
+
+        x_top = x_top.flatten(2)
+        x_center = x_center.flatten(2).transpose(1, 2)
+        x_bottom = x_bottom.flatten(2).transpose(1, 2)
+
+        sp_map = torch.matmul(x_center, x_top)
+        sp_map = self.map_softmax(sp_map.view(bsize, -1)).view(bsize, h*w, h*w)
+        sp_map = torch.matmul(sp_map, x_bottom)
+        sp_map = sp_map.reshape(bsize, self.in_channels, h, w)
+        x = x + sp_map
+        x = self.out_conv(x)
+        return x
+
+
+class MFAB(nn.Module):
+    def __init__(self, in_channels, skip_channels, out_channels, use_batchnorm=True, reduction=16):
+        # MFAB is just a modified version of SE-blocks, one for skip, one for input
+        super(MFAB, self).__init__()
+        self.hl_conv = nn.Sequential(
+            md.Conv2dReLU(
+                in_channels,
+                in_channels,
+                kernel_size=3,
+                padding=1,
+                use_batchnorm=use_batchnorm,
+            ),
+            md.Conv2dReLU(
+                in_channels,
+                skip_channels,
+                kernel_size=1,
+                use_batchnorm=use_batchnorm,
+            )
+        )
+        self.SE_ll = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1),
+            nn.Conv2d(skip_channels, skip_channels // reduction, 1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(skip_channels // reduction, skip_channels, 1),
+            nn.Sigmoid(),
+        )
+        self.SE_hl = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1),
+            nn.Conv2d(skip_channels, skip_channels // reduction, 1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(skip_channels // reduction, skip_channels, 1),
+            nn.Sigmoid(),
+        )
+        self.conv1 = md.Conv2dReLU(
+            skip_channels + skip_channels,  # we transform C-prime form high level to C from skip connection
+            out_channels,
+            kernel_size=3,
+            padding=1,
+            use_batchnorm=use_batchnorm,
+        )
+        self.conv2 = md.Conv2dReLU(
+            out_channels,
+            out_channels,
+            kernel_size=3,
+            padding=1,
+            use_batchnorm=use_batchnorm,
+        )
+
+    def forward(self, x, skip=None):
+        x = self.hl_conv(x)
+        x = F.interpolate(x, scale_factor=2, mode="nearest")
+        attention_hl = self.SE_hl(x)
+        if skip is not None:
+            attention_ll = self.SE_ll(skip)
+            attention_hl = attention_hl + attention_ll
+            x = x * attention_hl
+            x = torch.cat([x, skip], dim=1)
+        x = self.conv1(x)
+        x = self.conv2(x)
+        return x
+
+
+class DecoderBlock(nn.Module):
+    def __init__(
+            self,
+            in_channels,
+            skip_channels,
+            out_channels,
+            use_batchnorm=True
+    ):
+        super().__init__()
+        self.conv1 = md.Conv2dReLU(
+            in_channels + skip_channels,
+            out_channels,
+            kernel_size=3,
+            padding=1,
+            use_batchnorm=use_batchnorm,
+        )
+        self.conv2 = md.Conv2dReLU(
+            out_channels,
+            out_channels,
+            kernel_size=3,
+            padding=1,
+            use_batchnorm=use_batchnorm,
+        )
+
+    def forward(self, x, skip=None):
+        x = F.interpolate(x, scale_factor=2, mode="nearest")
+        if skip is not None:
+            x = torch.cat([x, skip], dim=1)
+        x = self.conv1(x)
+        x = self.conv2(x)
+        return x
+
+
+class MAnetDecoder(nn.Module):
+    def __init__(
+            self,
+            encoder_channels,
+            decoder_channels,
+            n_blocks=5,
+            reduction=16,
+            use_batchnorm=True,
+            pab_channels=64
+    ):
+        super().__init__()
+
+        if n_blocks != len(decoder_channels):
+            raise ValueError(
+                "Model depth is {}, but you provide `decoder_channels` for {} blocks.".format(
+                    n_blocks, len(decoder_channels)
+                )
+            )
+
+        encoder_channels = encoder_channels[1:]  # remove first skip with same spatial resolution
+        encoder_channels = encoder_channels[::-1]  # reverse channels to start from head of encoder
+
+        # computing blocks input and output channels
+        head_channels = encoder_channels[0]
+        in_channels = [head_channels] + list(decoder_channels[:-1])
+        skip_channels = list(encoder_channels[1:]) + [0]
+        out_channels = decoder_channels
+
+        self.center = PAB(head_channels, head_channels, pab_channels=pab_channels)
+
+        # combine decoder keyword arguments
+        kwargs = dict(use_batchnorm=use_batchnorm)  # no attention type here
+        blocks = [
+            MFAB(in_ch, skip_ch, out_ch, reduction=reduction, **kwargs) if skip_ch > 0 else
+            DecoderBlock(in_ch, skip_ch, out_ch, **kwargs)
+            for in_ch, skip_ch, out_ch in zip(in_channels, skip_channels, out_channels)
+        ]
+        # for the last we dont have skip connection -> use simple decoder block
+        self.blocks = nn.ModuleList(blocks)
+
+    def forward(self, *features):
+
+        features = features[1:]    # remove first skip with same spatial resolution
+        features = features[::-1]  # reverse channels to start from head of encoder
+
+        head = features[0]
+        skips = features[1:]
+
+        x = self.center(head)
+        for i, decoder_block in enumerate(self.blocks):
+            skip = skips[i] if i < len(skips) else None
+            x = decoder_block(x, skip)
+
+        return x
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/manet/model.py b/segmentation_models_pytorch/segmentation_models_pytorch/manet/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8fbc504bed7e3ecd36ffa2859f04b4d5047c2b1
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/manet/model.py
@@ -0,0 +1,98 @@
+from typing import Optional, Union, List
+from .decoder import MAnetDecoder
+from ..encoders import get_encoder
+from ..base import SegmentationModel
+from ..base import SegmentationHead, ClassificationHead
+
+
+class MAnet(SegmentationModel):
+    """MAnet_ :  Multi-scale Attention Net. The MA-Net can capture rich contextual dependencies based on the attention mechanism, 
+    using two blocks:
+     - Position-wise Attention Block (PAB), which captures the spatial dependencies between pixels in a global view
+     - Multi-scale Fusion Attention Block (MFAB), which  captures the channel dependencies between any feature map by
+       multi-scale semantic feature fusion
+
+    Args:
+        encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone)
+            to extract features of different spatial resolution
+        encoder_depth: A number of stages used in encoder in range [3, 5]. Each stage generate features 
+            two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features
+            with shapes [(N, C, H, W),], for depth 1 - [(N, C, H, W), (N, C, H // 2, W // 2)] and so on).
+            Default is 5
+        encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and 
+            other pretrained weights (see table with available weights for each encoder_name)
+        decoder_channels: List of integers which specify **in_channels** parameter for convolutions used in decoder.
+            Length of the list should be the same as **encoder_depth**
+        decoder_use_batchnorm: If **True**, BatchNorm2d layer between Conv2D and Activation layers
+            is used. If **"inplace"** InplaceABN will be used, allows to decrease memory consumption.
+            Available options are **True, False, "inplace"**
+        decoder_pab_channels: A number of channels for PAB module in decoder. 
+            Default is 64.
+        in_channels: A number of input channels for the model, default is 3 (RGB images)
+        classes: A number of classes for output mask (or you can think as a number of channels of output mask)
+        activation: An activation function to apply after the final convolution layer.
+            Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, **callable** and **None**.
+            Default is **None**
+        aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build
+            on top of encoder if **aux_params** is not **None** (default). Supported params:
+                - classes (int): A number of classes
+                - pooling (str): One of "max", "avg". Default is "avg"
+                - dropout (float): Dropout factor in [0, 1)
+                - activation (str): An activation function to apply "sigmoid"/"softmax" (could be **None** to return logits)
+
+    Returns:
+        ``torch.nn.Module``: **MAnet**
+
+    .. _MAnet:
+        https://ieeexplore.ieee.org/abstract/document/9201310
+
+    Reference:
+        https://ieeexplore.ieee.org/abstract/document/9201310
+    """
+
+    def __init__(
+        self,
+        encoder_name: str = "resnet34",
+        encoder_depth: int = 5,
+        encoder_weights: Optional[str] = "imagenet",
+        decoder_use_batchnorm: bool = True,
+        decoder_channels: List[int] = (256, 128, 64, 32, 16),
+        decoder_pab_channels: int = 64,
+        in_channels: int = 3,
+        classes: int = 1,
+        activation: Optional[Union[str, callable]] = None,
+        aux_params: Optional[dict] = None
+    ):
+        super().__init__()
+
+        self.encoder = get_encoder(
+            encoder_name,
+            in_channels=in_channels,
+            depth=encoder_depth,
+            weights=encoder_weights,
+        )
+
+        self.decoder = MAnetDecoder(
+            encoder_channels=self.encoder.out_channels,
+            decoder_channels=decoder_channels,
+            n_blocks=encoder_depth,
+            use_batchnorm=decoder_use_batchnorm,
+            pab_channels=decoder_pab_channels
+        )
+
+        self.segmentation_head = SegmentationHead(
+            in_channels=decoder_channels[-1],
+            out_channels=classes,
+            activation=activation,
+            kernel_size=3,
+        )
+
+        if aux_params is not None:
+            self.classification_head = ClassificationHead(
+                in_channels=self.encoder.out_channels[-1], **aux_params
+            )
+        else:
+            self.classification_head = None
+
+        self.name = "manet-{}".format(encoder_name)
+        self.initialize()
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/pan/__init__.py b/segmentation_models_pytorch/segmentation_models_pytorch/pan/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9917f8f14fdc1bbfe63846412d6f5db774617478
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/pan/__init__.py
@@ -0,0 +1 @@
+from .model import PAN
\ No newline at end of file
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/pan/decoder.py b/segmentation_models_pytorch/segmentation_models_pytorch/pan/decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..06493c78f864ca677c5e9bb484fd039ab3498b7b
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/pan/decoder.py
@@ -0,0 +1,166 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class ConvBnRelu(nn.Module):
+    def __init__(
+            self,
+            in_channels: int,
+            out_channels: int,
+            kernel_size: int,
+            stride: int = 1,
+            padding: int = 0,
+            dilation: int = 1,
+            groups: int = 1,
+            bias: bool = True,
+            add_relu: bool = True,
+            interpolate: bool = False
+    ):
+        super(ConvBnRelu, self).__init__()
+        self.conv = nn.Conv2d(
+            in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
+            stride=stride, padding=padding, dilation=dilation, bias=bias, groups=groups
+        )
+        self.add_relu = add_relu
+        self.interpolate = interpolate
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.activation = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        if self.add_relu:
+            x = self.activation(x)
+        if self.interpolate:
+            x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True)
+        return x
+
+
+class FPABlock(nn.Module):
+    def __init__(
+            self,
+            in_channels,
+            out_channels,
+            upscale_mode='bilinear'
+    ):
+        super(FPABlock, self).__init__()
+
+        self.upscale_mode = upscale_mode
+        if self.upscale_mode == 'bilinear':
+            self.align_corners = True
+        else:
+            self.align_corners = False
+
+        # global pooling branch
+        self.branch1 = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1),
+            ConvBnRelu(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1, padding=0)
+        )
+
+        # midddle branch
+        self.mid = nn.Sequential(
+            ConvBnRelu(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1, padding=0)
+        )
+        self.down1 = nn.Sequential(
+            nn.MaxPool2d(kernel_size=2, stride=2),
+            ConvBnRelu(in_channels=in_channels, out_channels=1, kernel_size=7, stride=1, padding=3)
+        )
+        self.down2 = nn.Sequential(
+            nn.MaxPool2d(kernel_size=2, stride=2),
+            ConvBnRelu(in_channels=1, out_channels=1, kernel_size=5, stride=1, padding=2)
+        )
+        self.down3 = nn.Sequential(
+            nn.MaxPool2d(kernel_size=2, stride=2),
+            ConvBnRelu(in_channels=1, out_channels=1, kernel_size=3, stride=1, padding=1),
+            ConvBnRelu(in_channels=1, out_channels=1, kernel_size=3, stride=1, padding=1),
+        )
+        self.conv2 = ConvBnRelu(in_channels=1, out_channels=1, kernel_size=5, stride=1, padding=2)
+        self.conv1 = ConvBnRelu(in_channels=1, out_channels=1, kernel_size=7, stride=1, padding=3)
+
+    def forward(self, x):
+        h, w = x.size(2), x.size(3)
+        b1 = self.branch1(x)
+        upscale_parameters = dict(
+            mode=self.upscale_mode,
+            align_corners=self.align_corners
+        )
+        b1 = F.interpolate(b1, size=(h, w), **upscale_parameters)
+
+        mid = self.mid(x)
+        x1 = self.down1(x)
+        x2 = self.down2(x1)
+        x3 = self.down3(x2)
+        x3 = F.interpolate(x3, size=(h // 4, w // 4), **upscale_parameters)
+
+        x2 = self.conv2(x2)
+        x = x2 + x3
+        x = F.interpolate(x, size=(h // 2, w // 2), **upscale_parameters)
+
+        x1 = self.conv1(x1)
+        x = x + x1
+        x = F.interpolate(x, size=(h, w), **upscale_parameters)
+
+        x = torch.mul(x, mid)
+        x = x + b1
+        return x
+
+
+class GAUBlock(nn.Module):
+    def __init__(
+            self,
+            in_channels: int,
+            out_channels: int,
+            upscale_mode: str = 'bilinear'
+    ):
+        super(GAUBlock, self).__init__()
+
+        self.upscale_mode = upscale_mode
+        self.align_corners = True if upscale_mode == 'bilinear' else None
+
+        self.conv1 = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1),
+            ConvBnRelu(in_channels=out_channels, out_channels=out_channels, kernel_size=1, add_relu=False),
+            nn.Sigmoid()
+        )
+        self.conv2 = ConvBnRelu(in_channels=in_channels, out_channels=out_channels, kernel_size=3, padding=1)
+
+    def forward(self, x, y):
+        """
+        Args:
+            x: low level feature
+            y: high level feature
+        """
+        h, w = x.size(2), x.size(3)
+        y_up = F.interpolate(
+            y, size=(h, w), mode=self.upscale_mode, align_corners=self.align_corners
+        )
+        x = self.conv2(x)
+        y = self.conv1(y)
+        z = torch.mul(x, y)
+        return y_up + z
+
+
+class PANDecoder(nn.Module):
+
+    def __init__(
+            self,
+            encoder_channels,
+            decoder_channels,
+            upscale_mode: str = 'bilinear'
+    ):
+        super().__init__()
+
+        self.fpa = FPABlock(in_channels=encoder_channels[-1], out_channels=decoder_channels)
+        self.gau3 = GAUBlock(in_channels=encoder_channels[-2], out_channels=decoder_channels, upscale_mode=upscale_mode)
+        self.gau2 = GAUBlock(in_channels=encoder_channels[-3], out_channels=decoder_channels, upscale_mode=upscale_mode)
+        self.gau1 = GAUBlock(in_channels=encoder_channels[-4], out_channels=decoder_channels, upscale_mode=upscale_mode)
+
+    def forward(self, *features):
+        bottleneck = features[-1]
+        x5 = self.fpa(bottleneck)         # 1/32
+        x4 = self.gau3(features[-2], x5)  # 1/16
+        x3 = self.gau2(features[-3], x4)  # 1/8
+        x2 = self.gau1(features[-4], x3)  # 1/4
+
+        return x2
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/pan/model.py b/segmentation_models_pytorch/segmentation_models_pytorch/pan/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..712a9befa50d75b1a10cd2764b0930ed4fbccbc3
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/pan/model.py
@@ -0,0 +1,94 @@
+from typing import Optional, Union
+from .decoder import PANDecoder
+from ..encoders import get_encoder
+from ..base import SegmentationModel
+from ..base import SegmentationHead, ClassificationHead
+
+
+class PAN(SegmentationModel):
+    """ Implementation of PAN_ (Pyramid Attention Network).
+
+    Note:
+        Currently works with shape of input tensor >= [B x C x 128 x 128] for pytorch <= 1.1.0
+        and with shape of input tensor >= [B x C x 256 x 256] for pytorch == 1.3.1
+
+    Args:
+        encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone)
+            to extract features of different spatial resolution
+        encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and 
+            other pretrained weights (see table with available weights for each encoder_name)
+        encoder_dilation: Flag to use dilation in encoder last layer. Doesn't work with ***ception***, **vgg***, 
+            **densenet*`** backbones, default is **True**
+        decoder_channels: A number of convolution layer filters in decoder blocks
+        in_channels: A number of input channels for the model, default is 3 (RGB images)
+        classes: A number of classes for output mask (or you can think as a number of channels of output mask)
+        activation: An activation function to apply after the final convolution layer.
+            Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, **callable** and **None**.
+            Default is **None**
+        upsampling: Final upsampling factor. Default is 4 to preserve input-output spatial shape identity
+        aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build 
+            on top of encoder if **aux_params** is not **None** (default). Supported params:
+                - classes (int): A number of classes
+                - pooling (str): One of "max", "avg". Default is "avg"
+                - dropout (float): Dropout factor in [0, 1)
+                - activation (str): An activation function to apply "sigmoid"/"softmax" (could be **None** to return logits)
+
+    Returns:
+        ``torch.nn.Module``: **PAN**
+
+    .. _PAN:
+        https://arxiv.org/abs/1805.10180
+
+    Reference:
+        https://arxiv.org/abs/1805.10180
+    """
+
+    def __init__(
+            self,
+            encoder_name: str = "resnet34",
+            encoder_weights: Optional[str] = "imagenet",
+            encoder_dilation: bool = True,
+            decoder_channels: int = 32,
+            in_channels: int = 3,
+            classes: int = 1,
+            activation: Optional[Union[str, callable]] = None,
+            upsampling: int = 4,
+            aux_params: Optional[dict] = None
+    ):
+        super().__init__()
+
+        self.encoder = get_encoder(
+            encoder_name,
+            in_channels=in_channels,
+            depth=5,
+            weights=encoder_weights,
+        )
+
+        if encoder_dilation:
+            self.encoder.make_dilated(
+                stage_list=[5],
+                dilation_list=[2]
+            )
+
+        self.decoder = PANDecoder(
+            encoder_channels=self.encoder.out_channels,
+            decoder_channels=decoder_channels,
+        )
+
+        self.segmentation_head = SegmentationHead(
+            in_channels=decoder_channels,
+            out_channels=classes,
+            activation=activation,
+            kernel_size=3,
+            upsampling=upsampling
+        )
+
+        if aux_params is not None:
+            self.classification_head = ClassificationHead(
+                in_channels=self.encoder.out_channels[-1], **aux_params
+            )
+        else:
+            self.classification_head = None
+
+        self.name = "pan-{}".format(encoder_name)
+        self.initialize()
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/pspnet/__init__.py b/segmentation_models_pytorch/segmentation_models_pytorch/pspnet/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ac0664d91b5ea14c666032fb42696595aee4199
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/pspnet/__init__.py
@@ -0,0 +1 @@
+from .model import PSPNet
\ No newline at end of file
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/pspnet/decoder.py b/segmentation_models_pytorch/segmentation_models_pytorch/pspnet/decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..79feba851cac561426164e53dac351be22b59608
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/pspnet/decoder.py
@@ -0,0 +1,72 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..base import modules
+
+
+class PSPBlock(nn.Module):
+
+    def __init__(self, in_channels, out_channels, pool_size, use_bathcnorm=True):
+        super().__init__()
+        if pool_size == 1:
+            use_bathcnorm = False  # PyTorch does not support BatchNorm for 1x1 shape
+        self.pool = nn.Sequential(
+            nn.AdaptiveAvgPool2d(output_size=(pool_size, pool_size)),
+            modules.Conv2dReLU(in_channels, out_channels, (1, 1), use_batchnorm=use_bathcnorm)
+        )
+
+    def forward(self, x):
+        h, w = x.size(2), x.size(3)
+        x = self.pool(x)
+        x = F.interpolate(x, size=(h, w), mode='bilinear', align_corners=True)
+        return x
+
+
+class PSPModule(nn.Module):
+    def __init__(self, in_channels, sizes=(1, 2, 3, 6), use_bathcnorm=True):
+        super().__init__()
+
+        self.blocks = nn.ModuleList([
+            PSPBlock(in_channels, in_channels // len(sizes), size, use_bathcnorm=use_bathcnorm) for size in sizes
+        ])
+
+    def forward(self, x):
+        xs = [block(x) for block in self.blocks] + [x]
+        x = torch.cat(xs, dim=1)
+        return x
+
+
+class PSPDecoder(nn.Module):
+
+    def __init__(
+            self,
+            encoder_channels,
+            use_batchnorm=True,
+            out_channels=512,
+            dropout=0.2,
+    ):
+        super().__init__()
+
+        self.psp = PSPModule(
+            in_channels=encoder_channels[-1],
+            sizes=(1, 2, 3, 6),
+            use_bathcnorm=use_batchnorm,
+        )
+
+        self.conv = modules.Conv2dReLU(
+            in_channels=encoder_channels[-1] * 2,
+            out_channels=out_channels,
+            kernel_size=1,
+            use_batchnorm=use_batchnorm,
+        )
+
+        self.dropout = nn.Dropout2d(p=dropout)
+
+    def forward(self, *features):
+        x = features[-1]
+        x = self.psp(x)
+        x = self.conv(x)
+        x = self.dropout(x)
+
+        return x
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/pspnet/model.py b/segmentation_models_pytorch/segmentation_models_pytorch/pspnet/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..95f8cfe0464f8ee9a2a84bc2b6040586e23e6443
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/pspnet/model.py
@@ -0,0 +1,99 @@
+from typing import Optional, Union
+
+from .decoder import PSPDecoder
+from ..encoders import get_encoder
+
+from ..base import SegmentationModel
+from ..base import SegmentationHead, ClassificationHead
+
+
+class PSPNet(SegmentationModel):
+    """PSPNet_ is a fully convolution neural network for image semantic segmentation. Consist of 
+    *encoder* and *Spatial Pyramid* (decoder). Spatial Pyramid build on top of encoder and does not 
+    use "fine-features" (features of high spatial resolution). PSPNet can be used for multiclass segmentation
+    of high resolution images, however it is not good for detecting small objects and producing accurate, pixel-level mask. 
+
+    Args:
+        encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone)
+            to extract features of different spatial resolution
+        encoder_depth: A number of stages used in encoder in range [3, 5]. Each stage generate features 
+            two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features
+            with shapes [(N, C, H, W),], for depth 1 - [(N, C, H, W), (N, C, H // 2, W // 2)] and so on).
+            Default is 5
+        encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and 
+            other pretrained weights (see table with available weights for each encoder_name)
+        psp_out_channels: A number of filters in Spatial Pyramid
+        psp_use_batchnorm: If **True**, BatchNorm2d layer between Conv2D and Activation layers
+            is used. If **"inplace"** InplaceABN will be used, allows to decrease memory consumption.
+            Available options are **True, False, "inplace"**
+        psp_dropout: Spatial dropout rate in [0, 1) used in Spatial Pyramid
+        in_channels: A number of input channels for the model, default is 3 (RGB images)
+        classes: A number of classes for output mask (or you can think as a number of channels of output mask)
+        activation: An activation function to apply after the final convolution layer.
+            Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, **callable** and **None**.
+            Default is **None**
+        upsampling: Final upsampling factor. Default is 8 to preserve input-output spatial shape identity
+        aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build 
+            on top of encoder if **aux_params** is not **None** (default). Supported params:
+                - classes (int): A number of classes
+                - pooling (str): One of "max", "avg". Default is "avg"
+                - dropout (float): Dropout factor in [0, 1)
+                - activation (str): An activation function to apply "sigmoid"/"softmax" (could be **None** to return logits)
+
+    Returns:
+        ``torch.nn.Module``: **PSPNet**
+
+    .. _PSPNet:
+        https://arxiv.org/abs/1612.01105
+
+    Reference:
+        https://arxiv.org/abs/1612.01105
+    """
+
+    def __init__(
+        self,
+        encoder_name: str = "resnet34",
+        encoder_weights: Optional[str] = "imagenet",
+        encoder_depth: int = 3,
+        psp_out_channels: int = 512,
+        psp_use_batchnorm: bool = True,
+        psp_dropout: float = 0.2,
+        in_channels: int = 3,
+        classes: int = 1,
+        activation: Optional[Union[str, callable]] = None,
+        upsampling: int = 8,
+        aux_params: Optional[dict] = None,
+    ):
+        super().__init__()
+
+        self.encoder = get_encoder(
+            encoder_name,
+            in_channels=in_channels,
+            depth=encoder_depth,
+            weights=encoder_weights,
+        )
+
+        self.decoder = PSPDecoder(
+            encoder_channels=self.encoder.out_channels,
+            use_batchnorm=psp_use_batchnorm,
+            out_channels=psp_out_channels,
+            dropout=psp_dropout,
+        )
+
+        self.segmentation_head = SegmentationHead(
+            in_channels=psp_out_channels,
+            out_channels=classes,
+            kernel_size=3,
+            activation=activation,
+            upsampling=upsampling,
+        )
+
+        if aux_params:
+            self.classification_head = ClassificationHead(
+                in_channels=self.encoder.out_channels[-1], **aux_params
+            )
+        else:
+            self.classification_head = None
+
+        self.name = "psp-{}".format(encoder_name)
+        self.initialize()
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/resunet/__init__.py b/segmentation_models_pytorch/segmentation_models_pytorch/resunet/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4185a94dba53d7c258ec5390db7ba531804a71a6
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/resunet/__init__.py
@@ -0,0 +1 @@
+from .model import ResUnet
\ No newline at end of file
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/resunet/decoder.py b/segmentation_models_pytorch/segmentation_models_pytorch/resunet/decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..d247c34513acb42c34deafdf9e93f2588232290a
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/resunet/decoder.py
@@ -0,0 +1,123 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..base import modules as md
+
+class DecoderBlock(nn.Module):
+    def __init__(
+            self,
+            in_channels,
+            skip_channels,
+            out_channels,
+            use_batchnorm=True,
+            attention_type=None,
+    ):
+        super().__init__()
+        self.conv1 = md.PreActivatedConv2dReLU(
+            in_channels + skip_channels,
+            out_channels,
+            kernel_size=3,
+            padding=1,
+            use_batchnorm=use_batchnorm,
+        )
+        self.attention1 = md.Attention(attention_type, in_channels=in_channels + skip_channels)
+        self.conv2 = md.PreActivatedConv2dReLU(
+            out_channels,
+            out_channels,
+            kernel_size=3,
+            padding=1,
+            use_batchnorm=use_batchnorm,
+        )
+        self.attention2 = md.Attention(attention_type, in_channels=out_channels)
+        self.identity_conv = nn.Conv2d(in_channels + skip_channels, out_channels, kernel_size=1)
+
+    def forward(self, x, skip=None):
+        x = F.interpolate(x, scale_factor=2, mode="nearest")
+        if skip is not None:
+            x = torch.cat([x, skip], dim=1)
+            identity = x
+            x = self.attention1(x)
+        else:
+            identity = x
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.attention2(x)
+        identity = self.identity_conv(identity)
+        return x + identity
+
+class CenterBlock(nn.Sequential):
+    def __init__(self, in_channels, out_channels, use_batchnorm=True):
+        conv1 = md.PreActivatedConv2dReLU(
+            in_channels,
+            out_channels,
+            kernel_size=3,
+            padding=1,
+            use_batchnorm=use_batchnorm,
+        )
+        conv2 = md.PreActivatedConv2dReLU(
+            out_channels,
+            out_channels,
+            kernel_size=3,
+            padding=1,
+            use_batchnorm=use_batchnorm,
+        )
+        super().__init__(conv1, conv2)
+
+class ResUnetDecoder(nn.Module):
+    def __init__(
+            self,
+            encoder_channels,
+            decoder_channels,
+            n_blocks=5,
+            use_batchnorm=True,
+            attention_type=None,
+            center=False,
+    ):
+        super().__init__()
+
+        if n_blocks != len(decoder_channels):
+            raise ValueError(
+                "Model depth is {}, but you provide `decoder_channels` for {} blocks.".format(
+                    n_blocks, len(decoder_channels)
+                )
+            )
+
+        encoder_channels = encoder_channels[1:]  # remove first skip with same spatial resolution
+        encoder_channels = encoder_channels[::-1]  # reverse channels to start from head of encoder
+
+        # computing blocks input and output channels
+        head_channels = encoder_channels[0]
+        in_channels = [head_channels] + list(decoder_channels[:-1])
+        skip_channels = list(encoder_channels[1:]) + [0]
+        out_channels = decoder_channels
+
+        if center:
+            self.center = CenterBlock(
+                head_channels, head_channels, use_batchnorm=use_batchnorm
+            )
+        else:
+            self.center = nn.Identity()
+
+        # combine decoder keyword arguments
+        kwargs = dict(use_batchnorm=use_batchnorm, attention_type=attention_type)
+        blocks = [
+            DecoderBlock(in_ch, skip_ch, out_ch, **kwargs)
+            for in_ch, skip_ch, out_ch in zip(in_channels, skip_channels, out_channels)
+        ]
+        self.blocks = nn.ModuleList(blocks)
+
+    def forward(self, *features):
+
+        features = features[1:]    # remove first skip with same spatial resolution
+        features = features[::-1]  # reverse channels to start from head of encoder
+
+        head = features[0]
+        skips = features[1:]
+
+        x = self.center(head)
+        for i, decoder_block in enumerate(self.blocks):
+            skip = skips[i] if i < len(skips) else None
+            x = decoder_block(x, skip)
+
+        return x
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/resunet/model.py b/segmentation_models_pytorch/segmentation_models_pytorch/resunet/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..399aa579cf45b92cfdff510a81bd4ad8b97ca799
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/resunet/model.py
@@ -0,0 +1,98 @@
+from typing import Optional, Union, List
+from .decoder import ResUnetDecoder
+from ..encoders import get_encoder
+from ..base import SegmentationModel
+from ..base import SegmentationHead, ClassificationHead
+
+
+class ResUnet(SegmentationModel):
+    """ResUnet_ is a fully convolution neural network for image semantic segmentation. Consist of *encoder* 
+    and *decoder* parts connected with *skip connections*. Encoder extract features of different spatial 
+    resolution (skip connections) which are used by decoder to define accurate segmentation mask. Use *concatenation*
+    for fusing decoder blocks with skip connections. Use residual connections inside each decoder block.
+
+    Args:
+        encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone) to extract features
+        encoder_depth: Number of stages of the encoder, in range [3 ,5]. Each stage generate features two times smaller, 
+            in spatial dimensions, than the previous one (e.g., for depth=0 features will haves shapes [(N, C, H, W)]), 
+            for depth 1 features will have shapes [(N, C, H, W), (N, C, H // 2, W // 2)] and so on).
+            Default is 5
+        encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and 
+            other pretrained weights (see table with available weights for each encoder_name)
+        decoder_channels: List of integers which specify **in_channels** parameter for convolutions used in the decoder.
+            Length of the list should be the same as **encoder_depth**
+        decoder_use_batchnorm: If **True**, BatchNorm2d layer between Conv2D and Activation layers
+            is used. If **"inplace"** InplaceABN will be used, allows to decrease memory consumption.
+            Available options are **True, False, "inplace"**
+        decoder_attention_type: Attention module used in decoder of the model. Available options are **None**, **se** and **scse**.
+            SE paper - https://arxiv.org/abs/1709.01507
+            SCSE paper - https://arxiv.org/abs/1808.08127
+        in_channels: The number of input channels of the model, default is 3 (RGB images)
+        classes: The number of classes of the output mask. Can be thought of as the number of channels of the mask
+        activation: An activation function to apply after the final convolution layer.
+            Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, **callable** and **None**.
+            Default is **None**
+        aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build 
+            on top of encoder if **aux_params** is not **None** (default). Supported params:
+                - classes (int): A number of classes
+                - pooling (str): One of "max", "avg". Default is "avg"
+                - dropout (float): Dropout factor in [0, 1)
+                - activation (str): An activation function to apply "sigmoid"/"softmax" (could be **None** to return logits)
+
+    Returns:
+        ``torch.nn.Module``: ResUnet
+
+    .. _ResUnet:
+        https://arxiv.org/abs/1711.10684
+
+    Reference:
+        https://arxiv.org/abs/1711.10684
+    """
+
+    def __init__(
+        self,
+        encoder_name: str = "resnet34",
+        encoder_depth: int = 5,
+        encoder_weights: Optional[str] = "imagenet",
+        decoder_use_batchnorm: bool = True,
+        decoder_channels: List[int] = (256, 128, 64, 32, 16),
+        decoder_attention_type: Optional[str] = None,
+        in_channels: int = 3,
+        classes: int = 1,
+        activation: Optional[Union[str, callable]] = None,
+        aux_params: Optional[dict] = None,
+    ):
+        super().__init__()
+
+        self.encoder = get_encoder(
+            encoder_name,
+            in_channels=in_channels,
+            depth=encoder_depth,
+            weights=encoder_weights,
+        )
+
+        self.decoder = ResUnetDecoder(
+            encoder_channels=self.encoder.out_channels,
+            decoder_channels=decoder_channels,
+            n_blocks=encoder_depth,
+            use_batchnorm=decoder_use_batchnorm,
+            center=True if encoder_name.startswith("vgg") else False,
+            attention_type=decoder_attention_type,
+        )
+
+        self.segmentation_head = SegmentationHead(
+            in_channels=decoder_channels[-1],
+            out_channels=classes,
+            activation=activation,
+            kernel_size=1,
+        )
+
+        if aux_params is not None:
+            self.classification_head = ClassificationHead(
+                in_channels=self.encoder.out_channels[-1], **aux_params
+            )
+        else:
+            self.classification_head = None
+
+        self.name = "resunet-{}".format(encoder_name)
+        self.initialize()
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/resunetplusplus/__init__.py b/segmentation_models_pytorch/segmentation_models_pytorch/resunetplusplus/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..34a0a6eccbc1e09650e29aaf9a83a8f313b77b23
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/resunetplusplus/__init__.py
@@ -0,0 +1 @@
+from .model import ResUnetPlusPlus
\ No newline at end of file
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/resunetplusplus/decoder.py b/segmentation_models_pytorch/segmentation_models_pytorch/resunetplusplus/decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..d69de4a35e553ecb7f1df2fa8b04aa48aea97e60
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/resunetplusplus/decoder.py
@@ -0,0 +1,185 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..base import modules as md
+
+class ASPP(nn.Module):
+    """ASPP described in https://arxiv.org/pdf/1706.05587.pdf but without the concatenation of 1x1, original feature maps and global average pooling"""
+    def __init__(self, in_channels, out_channels, rate=[6, 12, 18]):
+        super(ASPP, self).__init__()
+        
+        # Dilation rates of 6, 12 and 18 for the Atrous Spatial Pyramid Pooling blocks
+        self.aspp_block1 = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=rate[0], dilation=rate[0]),
+            nn.ReLU(inplace=True),
+            nn.BatchNorm2d(out_channels)
+        )
+        self.aspp_block2 = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=rate[1], dilation=rate[1]),
+            nn.ReLU(inplace=True),
+            nn.BatchNorm2d(out_channels)
+        )
+        self.aspp_block3 = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=rate[2], dilation=rate[2]),
+            nn.ReLU(inplace=True),
+            nn.BatchNorm2d(out_channels)
+        )
+        self.aspp_block4 = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.BatchNorm2d(out_channels)
+        )
+        
+        self.output = nn.Conv2d((len(rate)+1) * out_channels, out_channels, kernel_size=1)
+        self._init_weights()
+
+    def forward(self, x):
+
+        x1 = self.aspp_block1(x)
+        x2 = self.aspp_block2(x)
+        x3 = self.aspp_block3(x)
+        x4 = self.aspp_block4(x)
+        out = torch.cat([x1, x2, x3, x4], dim=1)
+        
+        return self.output(out)
+
+    def _init_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight)
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+class AttentionBlock(nn.Module):
+    def __init__(self, skip_channels, in_channels, out_channels):
+        super(AttentionBlock, self).__init__()
+
+        if skip_channels != 0:
+            self.encoder_conv = nn.Sequential(
+                nn.BatchNorm2d(skip_channels),
+                nn.ReLU(),
+                nn.Conv2d(skip_channels, out_channels, kernel_size=3, padding=1), 
+                nn.MaxPool2d(kernel_size=2, stride=2) # Attention is used before upsampling, so the encoder feature maps need to be downsampled
+            )
+
+        self.decoder_conv = nn.Sequential(
+            nn.BatchNorm2d(in_channels),
+            nn.ReLU(), 
+            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
+        )
+
+        self.attn_conv = nn.Sequential(
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(),
+            nn.Conv2d(in_channels = out_channels, out_channels = in_channels, kernel_size = 1),
+            nn.AdaptiveAvgPool2d(1)
+        )
+
+    def forward(self, x, skip=None):
+        # Apply BN, ReLU and 3x3 conv to incoming feature maps to obtain the desired number of feature maps and be able to sum them
+        if skip is not None:
+            out = self.encoder_conv(skip) + self.decoder_conv(x) 
+        else:
+            out = self.decoder_conv(x)
+        out = self.attn_conv(out) # Compute a BCHW attention mask
+        return out * x # Apply the attention mask to the input coming from the decoder
+
+class DecoderBlock(nn.Module):
+    def __init__(
+            self,
+            in_channels,
+            skip_channels,
+            out_channels,
+            use_batchnorm=True,
+            attention_type=None,
+    ):
+        super().__init__()
+        self.attention0 = AttentionBlock(skip_channels, in_channels, in_channels)
+        self.conv1 = md.PreActivatedConv2dReLU(
+            in_channels + skip_channels,
+            out_channels,
+            kernel_size=3,
+            padding=1,
+            use_batchnorm=use_batchnorm,
+        )
+        self.attention1 = md.Attention(attention_type, in_channels=in_channels + skip_channels)
+        self.conv2 = md.PreActivatedConv2dReLU(
+            out_channels,
+            out_channels,
+            kernel_size=3,
+            padding=1,
+            use_batchnorm=use_batchnorm,
+        )
+        self.identity_conv = nn.Conv2d(in_channels + skip_channels, out_channels, kernel_size=1)
+
+    def forward(self, x, skip=None):
+        x = self.attention0(x, skip)
+        x = F.interpolate(x, scale_factor=2, mode="nearest")
+        if skip is not None:
+            x = torch.cat([x, skip], dim=1)
+            identity = x
+            x = self.attention1(x)
+        else:
+            identity = x
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.attention2(x)
+        identity = self.identity_conv(identity)
+        return x + identity
+
+
+class ResUnetPlusPlusDecoder(nn.Module):
+    def __init__(
+            self,
+            encoder_channels,
+            decoder_channels,
+            n_blocks=5,
+            use_batchnorm=True,
+            attention_type=None,
+    ):
+        super().__init__()
+
+        if n_blocks != len(decoder_channels):
+            raise ValueError(
+                "Model depth is {}, but you provide `decoder_channels` for {} blocks.".format(
+                    n_blocks, len(decoder_channels)
+                )
+            )
+
+        encoder_channels = encoder_channels[1:]  # remove first skip with same spatial resolution
+        encoder_channels = encoder_channels[::-1]  # reverse channels to start from head of encoder
+
+        # computing blocks input and output channels
+        head_channels = encoder_channels[0]
+        in_channels = [2*head_channels] + [i*2 for i in decoder_channels[:-1]]
+        skip_channels = list(encoder_channels[1:]) + [0]
+        out_channels = [i*2 for i in decoder_channels] #decoder_channels
+
+        self.center = ASPP(head_channels, in_channels[0])
+
+        # combine decoder keyword arguments
+        kwargs = dict(use_batchnorm=use_batchnorm, attention_type=attention_type)
+        blocks = [
+            DecoderBlock(in_ch, skip_ch, out_ch, **kwargs)
+            for in_ch, skip_ch, out_ch in zip(in_channels, skip_channels, out_channels)
+        ]
+        self.blocks = nn.ModuleList(blocks)
+        self.final_aspp = ASPP(out_channels[-1], out_channels[-1]//2)
+
+    def forward(self, *features):
+
+        features = features[1:]    # remove first skip with same spatial resolution
+        features = features[::-1]  # reverse channels to start from head of encoder
+
+        head = features[0]
+        skips = features[1:]
+
+        x = self.center(head)
+        for i, decoder_block in enumerate(self.blocks):
+            skip = skips[i] if i < len(skips) else None
+            x = decoder_block(x, skip)
+        x = self.final_aspp(x)
+
+        return x
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/resunetplusplus/model.py b/segmentation_models_pytorch/segmentation_models_pytorch/resunetplusplus/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1856d2d9fd6e9a95fb538960d62f65816ed8fa4
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/resunetplusplus/model.py
@@ -0,0 +1,99 @@
+from typing import Optional, Union, List
+from .decoder import ResUnetPlusPlusDecoder
+from ..encoders import get_encoder
+from ..base import SegmentationModel
+from ..base import SegmentationHead, ClassificationHead
+
+
+class ResUnetPlusPlus(SegmentationModel):
+    """ResUnet++ is a fully convolution neural network for image semantic segmentation. Consist of *encoder* 
+    and *decoder* parts connected with *skip connections*. The encoder extracts features of different spatial 
+    resolution (skip connections) which are used by decoder to define accurate segmentation mask. 
+    
+    Applies attention to the skip connection feature maps, based on themselves and the decoder feature maps. 
+    The skip connection feature maps are then fused with the decoder feature maps through *concatenation*. 
+    Uses an Atrous Spatial Pyramid Pooling (ASPP) bridge module and residual connections inside each decoder 
+    blocks.
+
+    Args:
+        encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone) to extract features
+        encoder_depth: Number of stages of the encoder, in range [3 ,5]. Each stage generate features two times smaller, 
+            in spatial dimensions, than the previous one (e.g., for depth=0 features will haves shapes [(N, C, H, W)]), 
+            for depth 1 features will have shapes [(N, C, H, W), (N, C, H // 2, W // 2)] and so on).
+            Default is 5
+        encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and 
+            other pretrained weights (see table with available weights for each encoder_name)
+        decoder_channels: List of integers which specify **in_channels** parameter for convolutions used in the decoder.
+            Length of the list should be the same as **encoder_depth**
+        decoder_use_batchnorm: If **True**, BatchNorm2d layer between Conv2D and Activation layers
+            is used. If **"inplace"** InplaceABN will be used, allows to decrease memory consumption.
+            Available options are **True, False, "inplace"**
+        decoder_attention_type: Attention module used in decoder of the model (in addition to the built-in attention used to
+            process skip connection feature maps). Available options are **None**, **se** and **scse**.
+            SE paper - https://arxiv.org/abs/1709.01507
+            SCSE paper - https://arxiv.org/abs/1808.08127
+        in_channels: The number of input channels of the model, default is 3 (RGB images)
+        classes: The number of classes of the output mask. Can be thought of as the number of channels of the mask
+        activation: An activation function to apply after the final convolution layer.
+            Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, **callable** and **None**.
+            Default is **None**
+        aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build 
+            on top of encoder if **aux_params** is not **None** (default). Supported params:
+                - classes (int): A number of classes
+                - pooling (str): One of "max", "avg". Default is "avg"
+                - dropout (float): Dropout factor in [0, 1)
+                - activation (str): An activation function to apply "sigmoid"/"softmax" (could be **None** to return logits)
+
+    Returns:
+        ``torch.nn.Module``: ResUnetPlusPlus
+
+    Reference:
+        https://arxiv.org/abs/1911.07067
+    """
+
+    def __init__(
+        self,
+        encoder_name: str = "resnet34",
+        encoder_depth: int = 5,
+        encoder_weights: Optional[str] = "imagenet",
+        decoder_use_batchnorm: bool = True,
+        decoder_channels: List[int] = (256, 128, 64, 32, 16),
+        decoder_attention_type: Optional[str] = None,
+        in_channels: int = 3,
+        classes: int = 1,
+        activation: Optional[Union[str, callable]] = None,
+        aux_params: Optional[dict] = None,
+    ):
+        super().__init__()
+
+        self.encoder = get_encoder(
+            encoder_name,
+            in_channels=in_channels,
+            depth=encoder_depth,
+            weights=encoder_weights,
+        )
+
+        self.decoder = ResUnetPlusPlusDecoder(
+            encoder_channels=self.encoder.out_channels,
+            decoder_channels=decoder_channels,
+            n_blocks=encoder_depth,
+            use_batchnorm=decoder_use_batchnorm,
+            attention_type=decoder_attention_type,
+        )
+
+        self.segmentation_head = SegmentationHead(
+            in_channels=decoder_channels[-1],
+            out_channels=classes,
+            activation=activation,
+            kernel_size=1,
+        )
+
+        if aux_params is not None:
+            self.classification_head = ClassificationHead(
+                in_channels=self.encoder.out_channels[-1], **aux_params
+            )
+        else:
+            self.classification_head = None
+
+        self.name = "resunet++-{}".format(encoder_name)
+        self.initialize()
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/unet/__init__.py b/segmentation_models_pytorch/segmentation_models_pytorch/unet/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba6878e0c9f397907149a4dc6f96b6020e2a7f22
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/unet/__init__.py
@@ -0,0 +1 @@
+from .model import Unet
\ No newline at end of file
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/unet/decoder.py b/segmentation_models_pytorch/segmentation_models_pytorch/unet/decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..46aeee79c7b2c9333dac8624099d62d17118170a
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/unet/decoder.py
@@ -0,0 +1,122 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..base import modules as md
+
+
+class DecoderBlock(nn.Module):
+    def __init__(
+            self,
+            in_channels,
+            skip_channels,
+            out_channels,
+            use_batchnorm=True,
+            attention_type=None,
+    ):
+        super().__init__()
+
+        self.conv1 = md.Conv2dReLU(
+            in_channels + skip_channels,
+            out_channels,
+            kernel_size=3,
+            padding=1,
+            use_batchnorm=use_batchnorm,
+        )
+        self.attention1 = md.Attention(attention_type, in_channels=in_channels + skip_channels)
+        self.conv2 = md.Conv2dReLU(
+            out_channels,
+            out_channels,
+            kernel_size=3,
+            padding=1,
+            use_batchnorm=use_batchnorm,
+        )
+        self.attention2 = md.Attention(attention_type, in_channels=out_channels)
+
+    def forward(self, x, skip=None):
+        x = F.interpolate(x, scale_factor=2, mode="nearest")
+        if skip is not None:
+            x = torch.cat([x, skip], dim=1)
+            x = self.attention1(x)
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.attention2(x)
+        return x
+
+
+class CenterBlock(nn.Sequential):
+    def __init__(self, in_channels, out_channels, use_batchnorm=True):
+        conv1 = md.Conv2dReLU(
+            in_channels,
+            out_channels,
+            kernel_size=3,
+            padding=1,
+            use_batchnorm=use_batchnorm,
+        )
+        conv2 = md.Conv2dReLU(
+            out_channels,
+            out_channels,
+            kernel_size=3,
+            padding=1,
+            use_batchnorm=use_batchnorm,
+        )
+        super().__init__(conv1, conv2)
+
+
+class UnetDecoder(nn.Module):
+    def __init__(
+            self,
+            encoder_channels,
+            decoder_channels,
+            n_blocks=5,
+            use_batchnorm=True,
+            attention_type=None,
+            center=False,
+    ):
+        super().__init__()
+
+        if n_blocks != len(decoder_channels):
+            raise ValueError(
+                "Model depth is {}, but you provide `decoder_channels` for {} blocks.".format(
+                    n_blocks, len(decoder_channels)
+                )
+            )
+
+        encoder_channels = encoder_channels[1:]  # remove first skip with same spatial resolution
+        encoder_channels = encoder_channels[::-1]  # reverse channels to start from head of encoder
+
+        # computing blocks input and output channels
+        head_channels = encoder_channels[0]
+        in_channels = [head_channels] + list(decoder_channels[:-1])
+        skip_channels = list(encoder_channels[1:]) + [0]
+        out_channels = decoder_channels
+
+        if center:
+            self.center = CenterBlock(
+                head_channels, head_channels, use_batchnorm=use_batchnorm
+            )
+        else:
+            self.center = nn.Identity()
+
+        # combine decoder keyword arguments
+        kwargs = dict(use_batchnorm=use_batchnorm, attention_type=attention_type)
+        blocks = [
+            DecoderBlock(in_ch, skip_ch, out_ch, **kwargs)
+            for in_ch, skip_ch, out_ch in zip(in_channels, skip_channels, out_channels)
+        ]
+        self.blocks = nn.ModuleList(blocks)
+
+    def forward(self, *features):
+
+        features = features[1:]    # remove first skip with same spatial resolution
+        features = features[::-1]  # reverse channels to start from head of encoder
+
+        head = features[0]
+        skips = features[1:]
+
+        x = self.center(head)
+        for i, decoder_block in enumerate(self.blocks):
+            skip = skips[i] if i < len(skips) else None
+            x = decoder_block(x, skip)
+
+        return x
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/unet/model.py b/segmentation_models_pytorch/segmentation_models_pytorch/unet/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..580a2af4ab77959fa3b7e62d177a2e4947fb8fcd
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/unet/model.py
@@ -0,0 +1,96 @@
+from typing import Optional, Union, List
+from .decoder import UnetDecoder
+from ..encoders import get_encoder
+from ..base import SegmentationModel
+from ..base import SegmentationHead, ClassificationHead
+
+
+class Unet(SegmentationModel):
+    """Unet_ is a fully convolution neural network for image semantic segmentation. Consist of *encoder* 
+    and *decoder* parts connected with *skip connections*. Encoder extract features of different spatial 
+    resolution (skip connections) which are used by decoder to define accurate segmentation mask. Use *concatenation*
+    for fusing decoder blocks with skip connections.
+
+    Args:
+        encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone)
+            to extract features of different spatial resolution
+        encoder_depth: A number of stages used in encoder in range [3, 5]. Each stage generate features 
+            two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features
+            with shapes [(N, C, H, W),], for depth 1 - [(N, C, H, W), (N, C, H // 2, W // 2)] and so on).
+            Default is 5
+        encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and 
+            other pretrained weights (see table with available weights for each encoder_name)
+        decoder_channels: List of integers which specify **in_channels** parameter for convolutions used in decoder.
+            Length of the list should be the same as **encoder_depth**
+        decoder_use_batchnorm: If **True**, BatchNorm2d layer between Conv2D and Activation layers
+            is used. If **"inplace"** InplaceABN will be used, allows to decrease memory consumption.
+            Available options are **True, False, "inplace"**
+        decoder_attention_type: Attention module used in decoder of the model. Available options are **None**, **se** and **scse**.
+            SE paper - https://arxiv.org/abs/1709.01507
+            SCSE paper - https://arxiv.org/abs/1808.08127
+        in_channels: A number of input channels for the model, default is 3 (RGB images)
+        classes: A number of classes for output mask (or you can think as a number of channels of output mask)
+        activation: An activation function to apply after the final convolution layer.
+            Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, **callable** and **None**.
+            Default is **None**
+        aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build 
+            on top of encoder if **aux_params** is not **None** (default). Supported params:
+                - classes (int): A number of classes
+                - pooling (str): One of "max", "avg". Default is "avg"
+                - dropout (float): Dropout factor in [0, 1)
+                - activation (str): An activation function to apply "sigmoid"/"softmax" (could be **None** to return logits)
+
+    Returns:
+        ``torch.nn.Module``: Unet
+
+    .. _Unet:
+        https://arxiv.org/abs/1505.04597
+    """
+
+    def __init__(
+        self,
+        encoder_name: str = "resnet34",
+        encoder_depth: int = 5,
+        encoder_weights: Optional[str] = "imagenet",
+        decoder_use_batchnorm: bool = True,
+        decoder_channels: List[int] = (256, 128, 64, 32, 16),
+        decoder_attention_type: Optional[str] = None,
+        in_channels: int = 3,
+        classes: int = 1,
+        activation: Optional[Union[str, callable]] = None,
+        aux_params: Optional[dict] = None,
+    ):
+        super().__init__()
+
+        self.encoder = get_encoder(
+            encoder_name,
+            in_channels=in_channels,
+            depth=encoder_depth,
+            weights=encoder_weights,
+        )
+
+        self.decoder = UnetDecoder(
+            encoder_channels=self.encoder.out_channels,
+            decoder_channels=decoder_channels,
+            n_blocks=encoder_depth,
+            use_batchnorm=decoder_use_batchnorm,
+            center=True if encoder_name.startswith("vgg") else False,
+            attention_type=decoder_attention_type,
+        )
+
+        self.segmentation_head = SegmentationHead(
+            in_channels=decoder_channels[-1],
+            out_channels=classes,
+            activation=activation,
+            kernel_size=3,
+        )
+
+        if aux_params is not None:
+            self.classification_head = ClassificationHead(
+                in_channels=self.encoder.out_channels[-1], **aux_params
+            )
+        else:
+            self.classification_head = None
+
+        self.name = "u-{}".format(encoder_name)
+        self.initialize()
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/unetplusplus/__init__.py b/segmentation_models_pytorch/segmentation_models_pytorch/unetplusplus/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..bda62b70a30d92622616f7279d153bc4b68f6b54
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/unetplusplus/__init__.py
@@ -0,0 +1 @@
+from .model import UnetPlusPlus
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/unetplusplus/decoder.py b/segmentation_models_pytorch/segmentation_models_pytorch/unetplusplus/decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff7ce23f2834f68250ef149aabf3e78f79dcfa96
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/unetplusplus/decoder.py
@@ -0,0 +1,149 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..base import modules as md
+
+class BilinearAdditiveUpsampling(nn.Module):
+    def __init__(self, channel_factor=2, scale_factor=2):
+        super().__init__()
+        self.up = nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False)
+        self.channel_factor = channel_factor
+        self.scale_factor = 2
+
+    def forward(self, x):
+        x = self.up(x)
+        n, c, h, w = x.size()
+        x = x.reshape(n, c//self.channel_factor, self.channel_factor, h, w).sum(2)
+        return x
+
+class DecoderBlock(nn.Module):
+    def __init__(
+            self,
+            in_channels,
+            skip_channels,
+            out_channels,
+            use_batchnorm=True,
+            attention_type=None,
+            weight_standardization=False
+    ):
+        super().__init__()
+        self.conv1 = md.Conv2dReLU(
+            in_channels + skip_channels,
+            out_channels,
+            kernel_size=3,
+            padding=1,
+            use_batchnorm=use_batchnorm,
+        )
+        self.attention1 = md.Attention(attention_type, in_channels=in_channels + skip_channels)
+        self.conv2 = md.Conv2dReLU(
+            out_channels,
+            out_channels,
+            kernel_size=3,
+            padding=1,
+            use_batchnorm=use_batchnorm,
+        )
+        self.attention2 = md.Attention(attention_type, in_channels=out_channels)
+
+    def forward(self, x, skip=None):
+        x = F.interpolate(x, scale_factor=2, mode="nearest")
+        if skip is not None:
+            x = torch.cat([x, skip], dim=1)
+            x = self.attention1(x)
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.attention2(x)
+        return x
+
+
+class CenterBlock(nn.Sequential):
+    def __init__(self, in_channels, out_channels, use_batchnorm=True):
+        conv1 = md.Conv2dReLU(
+            in_channels,
+            out_channels,
+            kernel_size=3,
+            padding=1,
+            use_batchnorm=use_batchnorm,
+        )
+        conv2 = md.Conv2dReLU(
+            out_channels,
+            out_channels,
+            kernel_size=3,
+            padding=1,
+            use_batchnorm=use_batchnorm,
+        )
+        super().__init__(conv1, conv2)
+
+
+class UnetPlusPlusDecoder(nn.Module):
+    def __init__(
+            self,
+            encoder_channels,
+            decoder_channels,
+            n_blocks=5,
+            use_batchnorm=True,
+            attention_type=None,
+            center=False,
+            weight_standardization=False,
+    ):
+        super().__init__()
+        if n_blocks != len(decoder_channels):
+            raise ValueError(
+                "Model depth is {}, but you provide `decoder_channels` for {} blocks.".format(
+                    n_blocks, len(decoder_channels)
+                )
+            )
+
+        encoder_channels = encoder_channels[1:]  # remove first skip with same spatial resolution
+        encoder_channels = encoder_channels[::-1]  # reverse channels to start from head of encoder
+        # computing blocks input and output channels
+        head_channels = encoder_channels[0]
+        self.in_channels = [head_channels] + list(decoder_channels[:-1])
+        self.skip_channels = list(encoder_channels[1:]) + [0]
+        self.out_channels = decoder_channels
+        if center:
+            self.center = CenterBlock(
+                head_channels, head_channels, use_batchnorm=use_batchnorm
+            )
+        else:
+            self.center = nn.Identity()
+
+        # combine decoder keyword arguments
+        kwargs = dict(use_batchnorm=use_batchnorm, attention_type=attention_type, weight_standardization=weight_standardization)
+
+        blocks = {}
+        for layer_idx in range(len(self.in_channels) - 1):
+            for depth_idx in range(layer_idx+1):
+                if depth_idx == 0:
+                    in_ch = self.in_channels[layer_idx]
+                    skip_ch = self.skip_channels[layer_idx] * (layer_idx+1)
+                    out_ch = self.out_channels[layer_idx]
+                else:
+                    out_ch = self.skip_channels[layer_idx]
+                    skip_ch = self.skip_channels[layer_idx] * (layer_idx+1-depth_idx)
+                    in_ch = self.skip_channels[layer_idx - 1]
+                blocks[f'x_{depth_idx}_{layer_idx}'] = DecoderBlock(in_ch, skip_ch, out_ch, **kwargs)
+        blocks[f'x_{0}_{len(self.in_channels)-1}'] =\
+            DecoderBlock(self.in_channels[-1], 0, self.out_channels[-1], **kwargs)
+        self.blocks = nn.ModuleDict(blocks)
+        self.depth = len(self.in_channels) - 1
+
+    def forward(self, *features):
+
+        features = features[1:]    # remove first skip with same spatial resolution
+        features = features[::-1]  # reverse channels to start from head of encoder
+        # start building dense connections
+        dense_x = {}
+        for layer_idx in range(len(self.in_channels)-1):
+            for depth_idx in range(self.depth-layer_idx):
+                if layer_idx == 0:
+                    output = self.blocks[f'x_{depth_idx}_{depth_idx}'](features[depth_idx], features[depth_idx+1])
+                    dense_x[f'x_{depth_idx}_{depth_idx}'] = output
+                else:
+                    dense_l_i = depth_idx + layer_idx
+                    cat_features = [dense_x[f'x_{idx}_{dense_l_i}'] for idx in range(depth_idx+1, dense_l_i+1)]
+                    cat_features = torch.cat(cat_features + [features[dense_l_i+1]], dim=1)
+                    dense_x[f'x_{depth_idx}_{dense_l_i}'] =\
+                        self.blocks[f'x_{depth_idx}_{dense_l_i}'](dense_x[f'x_{depth_idx}_{dense_l_i-1}'], cat_features)
+        dense_x[f'x_{0}_{self.depth}'] = self.blocks[f'x_{0}_{self.depth}'](dense_x[f'x_{0}_{self.depth-1}'])
+        return dense_x[f'x_{0}_{self.depth}']
\ No newline at end of file
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/unetplusplus/model.py b/segmentation_models_pytorch/segmentation_models_pytorch/unetplusplus/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5ff4652a4987f68f02c68cf75ac87906b2ae7cb
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/unetplusplus/model.py
@@ -0,0 +1,131 @@
+from typing import Optional, Union, List
+from .decoder import UnetPlusPlusDecoder
+from ..encoders import get_encoder
+from ..base import SegmentationModel
+from ..base import SegmentationHead, ClassificationHead
+from torchvision import transforms
+
+
+class UnetPlusPlus(SegmentationModel):
+    """Unet++ is a fully convolution neural network for image semantic segmentation. Consist of *encoder* 
+    and *decoder* parts connected with *skip connections*. Encoder extract features of different spatial 
+    resolution (skip connections) which are used by decoder to define accurate segmentation mask. Decoder of
+    Unet++ is more complex than in usual Unet.
+    Args:
+        encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone)
+            to extract features of different spatial resolution
+        encoder_depth: A number of stages used in encoder in range [3, 5]. Each stage generate features 
+            two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features
+            with shapes [(N, C, H, W),], for depth 1 - [(N, C, H, W), (N, C, H // 2, W // 2)] and so on).
+            Default is 5
+        encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and 
+            other pretrained weights (see table with available weights for each encoder_name)
+        decoder_channels: List of integers which specify **in_channels** parameter for convolutions used in decoder.
+            Length of the list should be the same as **encoder_depth**
+        decoder_use_batchnorm: If **True**, BatchNorm2d layer between Conv2D and Activation layers
+            is used. If **"inplace"** InplaceABN will be used, allows to decrease memory consumption.
+            Available options are **True, False, "inplace"**
+        decoder_attention_type: Attention module used in decoder of the model. Available options are **None**, **se** and **scse**.
+            SE paper - https://arxiv.org/abs/1709.01507
+            SCSE paper - https://arxiv.org/abs/1808.08127
+        in_channels: A number of input channels for the model, default is 3 (RGB images)
+        classes: A number of classes for output mask (or you can think as a number of channels of output mask)
+        activation: An activation function to apply after the final convolution layer.
+            Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, **callable** and **None**.
+            Default is **None**
+        aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build 
+            on top of encoder if **aux_params** is not **None** (default). Supported params:
+                - classes (int): A number of classes
+                - pooling (str): One of "max", "avg". Default is "avg"
+                - dropout (float): Dropout factor in [0, 1)
+                - activation (str): An activation function to apply "sigmoid"/"softmax" (could be **None** to return logits)
+    Returns:
+        ``torch.nn.Module``: **Unet++**
+
+    Reference:
+        https://arxiv.org/abs/1807.10165,
+        https://arxiv.org/abs/1912.05074
+    """
+
+    def __init__(
+        self,
+        encoder_name: str = "resnet34",
+        encoder_depth: int = 5,
+        encoder_weights: Optional[str] = "imagenet",
+        decoder_use_batchnorm: bool = True,
+        decoder_channels: List[int] = (256, 128, 64, 32, 16),
+        decoder_attention_type: Optional[str] = None,
+        in_channels: int = 3,
+        classes: int = 1,
+        activation: Optional[Union[str, callable]] = None,
+        aux_params: Optional[dict] = None,
+        weight_standardization: bool = False,
+    ):
+        super().__init__()
+        self.classes = classes
+        self.encoder = get_encoder(
+            encoder_name,
+            in_channels=in_channels,
+            depth=encoder_depth,
+            weights=encoder_weights,
+        )
+
+        self.decoder = UnetPlusPlusDecoder(
+            encoder_channels=self.encoder.out_channels,
+            decoder_channels=decoder_channels,
+            n_blocks=encoder_depth,
+            use_batchnorm=decoder_use_batchnorm,
+            center=True if encoder_name.startswith("vgg") else False,
+            attention_type=decoder_attention_type,
+            weight_standardization=weight_standardization,
+        )
+
+        self.segmentation_head = SegmentationHead(
+            in_channels=decoder_channels[-1],
+            out_channels=classes,
+            activation=activation,
+            kernel_size=3,
+        )
+
+        if aux_params is not None:
+            self.classification_head = ClassificationHead(
+                in_channels=self.encoder.out_channels[-1], **aux_params
+            )
+        else:
+            self.classification_head = None
+
+        self.name = "unetplusplus-{}".format(encoder_name)
+        self.initialize()
+
+    def predict(self, x):
+        """Inference method. Switch model to `eval` mode, call `.forward(x)` with `torch.no_grad()`
+
+        Args:
+            x: 4D torch tensor with shape (batch_size, channels, height, width)
+
+        Return:
+            prediction: 4D torch tensor with shape (batch_size, classes, height, width)
+
+        """
+        if self.training:
+            self.eval()
+
+        with torch.no_grad():
+            x = self.forward(x)
+
+        if self.classes > 1:
+            probs = torch.softmax(output, dim=1)
+        else:
+            probs = torch.sigmoid(output)
+
+        probs = probs.squeeze(0)
+        tf = transforms.Compose(
+            [
+                transforms.ToPILImage(),
+                transforms.Resize(full_img.size[1]),
+                transforms.ToTensor()
+            ]
+        )
+        full_mask = tf(probs.cpu())   
+
+        return full_mask
\ No newline at end of file
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/utils/__init__.py b/segmentation_models_pytorch/segmentation_models_pytorch/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d7481dba7951e5572abccc2e7e1266118adf932
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/utils/__init__.py
@@ -0,0 +1,3 @@
+from . import train
+from . import losses
+from . import metrics
\ No newline at end of file
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/utils/base.py b/segmentation_models_pytorch/segmentation_models_pytorch/utils/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e1f3772adecd488cd0045a0002c230d6c1be71b
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/utils/base.py
@@ -0,0 +1,71 @@
+import re
+import torch.nn as nn
+
+class BaseObject(nn.Module):
+
+    def __init__(self, name=None):
+        super().__init__()
+        self._name = name
+
+    @property
+    def __name__(self):
+        if self._name is None:
+            name = self.__class__.__name__
+            s1 = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', name)
+            return re.sub('([a-z0-9])([A-Z])', r'\1_\2', s1).lower()
+        else:
+            return self._name
+
+
+class Metric(BaseObject):
+    pass
+
+
+class Loss(BaseObject):
+
+    def __add__(self, other):
+        if isinstance(other, Loss):
+            return SumOfLosses(self, other)
+        else:
+            raise ValueError('Loss should be inherited from `Loss` class')
+
+    def __radd__(self, other):
+        return self.__add__(other)
+
+    def __mul__(self, value):
+        if isinstance(value, (int, float)):
+            return MultipliedLoss(self, value)
+        else:
+            raise ValueError('Loss should be inherited from `BaseLoss` class')
+
+    def __rmul__(self, other):
+        return self.__mul__(other)
+
+
+class SumOfLosses(Loss):
+
+    def __init__(self, l1, l2):
+        name = '{} + {}'.format(l1.__name__, l2.__name__)
+        super().__init__(name=name)
+        self.l1 = l1
+        self.l2 = l2
+
+    def __call__(self, *inputs):
+        return self.l1.forward(*inputs) + self.l2.forward(*inputs)
+
+
+class MultipliedLoss(Loss):
+
+    def __init__(self, loss, multiplier):
+
+        # resolve name
+        if len(loss.__name__.split('+')) > 1:
+            name = '{} * ({})'.format(multiplier, loss.__name__)
+        else:
+            name = '{} * {}'.format(multiplier, loss.__name__)
+        super().__init__(name=name)
+        self.loss = loss
+        self.multiplier = multiplier
+
+    def __call__(self, *inputs):
+        return self.multiplier * self.loss.forward(*inputs)
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/utils/functional.py b/segmentation_models_pytorch/segmentation_models_pytorch/utils/functional.py
new file mode 100644
index 0000000000000000000000000000000000000000..a06e2c12cf054191a52246abcc090ca5e1d99d31
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/utils/functional.py
@@ -0,0 +1,126 @@
+import torch
+
+
+def _take_channels(*xs, ignore_channels=None):
+    if ignore_channels is None:
+        return xs
+    else:
+        channels = [channel for channel in range(xs[0].shape[1]) if channel not in ignore_channels]
+        xs = [torch.index_select(x, dim=1, index=torch.tensor(channels).to(x.device)) for x in xs]
+        return xs
+
+
+def _threshold(x, threshold=None):
+    if threshold is not None:
+        return (x > threshold).type(x.dtype)
+    else:
+        return x
+
+
+def iou(pr, gt, eps=1e-7, threshold=None, ignore_channels=None):
+    """Calculate Intersection over Union between ground truth and prediction
+    Args:
+        pr (torch.Tensor): predicted tensor
+        gt (torch.Tensor):  ground truth tensor
+        eps (float): epsilon to avoid zero division
+        threshold: threshold for outputs binarization
+    Returns:
+        float: IoU (Jaccard) score
+    """
+
+    pr = _threshold(pr, threshold=threshold)
+    pr, gt = _take_channels(pr, gt, ignore_channels=ignore_channels)
+
+    intersection = torch.sum(gt * pr)
+    union = torch.sum(gt) + torch.sum(pr) - intersection + eps
+    return (intersection + eps) / union
+
+
+jaccard = iou
+
+
+def f_score(pr, gt, beta=1, eps=1e-7, threshold=None, ignore_channels=None):
+    """Calculate F-score between ground truth and prediction
+    Args:
+        pr (torch.Tensor): predicted tensor
+        gt (torch.Tensor):  ground truth tensor
+        beta (float): positive constant
+        eps (float): epsilon to avoid zero division
+        threshold: threshold for outputs binarization
+    Returns:
+        float: F score
+    """
+
+    pr = _threshold(pr, threshold=threshold)
+    pr, gt = _take_channels(pr, gt, ignore_channels=ignore_channels)
+
+    tp = torch.sum(gt * pr)
+    fp = torch.sum(pr) - tp
+    fn = torch.sum(gt) - tp
+
+    score = ((1 + beta ** 2) * tp + eps) \
+            / ((1 + beta ** 2) * tp + beta ** 2 * fn + fp + eps)
+
+    return score
+
+
+def accuracy(pr, gt, threshold=0.5, ignore_channels=None):
+    """Calculate accuracy score between ground truth and prediction
+    Args:
+        pr (torch.Tensor): predicted tensor
+        gt (torch.Tensor):  ground truth tensor
+        eps (float): epsilon to avoid zero division
+        threshold: threshold for outputs binarization
+    Returns:
+        float: precision score
+    """
+    pr = _threshold(pr, threshold=threshold)
+    pr, gt = _take_channels(pr, gt, ignore_channels=ignore_channels)
+
+    tp = torch.sum(gt == pr, dtype=pr.dtype)
+    score = tp / gt.view(-1).shape[0]
+    return score
+
+
+def precision(pr, gt, eps=1e-7, threshold=None, ignore_channels=None):
+    """Calculate precision score between ground truth and prediction
+    Args:
+        pr (torch.Tensor): predicted tensor
+        gt (torch.Tensor):  ground truth tensor
+        eps (float): epsilon to avoid zero division
+        threshold: threshold for outputs binarization
+    Returns:
+        float: precision score
+    """
+
+    pr = _threshold(pr, threshold=threshold)
+    pr, gt = _take_channels(pr, gt, ignore_channels=ignore_channels)
+
+    tp = torch.sum(gt * pr)
+    fp = torch.sum(pr) - tp
+
+    score = (tp + eps) / (tp + fp + eps)
+
+    return score
+
+
+def recall(pr, gt, eps=1e-7, threshold=None, ignore_channels=None):
+    """Calculate Recall between ground truth and prediction
+    Args:
+        pr (torch.Tensor): A list of predicted elements
+        gt (torch.Tensor):  A list of elements that are to be predicted
+        eps (float): epsilon to avoid zero division
+        threshold: threshold for outputs binarization
+    Returns:
+        float: recall score
+    """
+
+    pr = _threshold(pr, threshold=threshold)
+    pr, gt = _take_channels(pr, gt, ignore_channels=ignore_channels)
+
+    tp = torch.sum(gt * pr)
+    fn = torch.sum(gt) - tp
+
+    score = (tp + eps) / (tp + fn + eps)
+
+    return score
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/utils/losses.py b/segmentation_models_pytorch/segmentation_models_pytorch/utils/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a90cee9f56f06efc47c5c9bd48002c1d298cbaa
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/utils/losses.py
@@ -0,0 +1,67 @@
+import torch.nn as nn
+
+from . import base
+from . import functional as F
+from ..base.modules import Activation
+
+
+class JaccardLoss(base.Loss):
+
+    def __init__(self, eps=1., activation=None, ignore_channels=None, **kwargs):
+        super().__init__(**kwargs)
+        self.eps = eps
+        self.activation = Activation(activation)
+        self.ignore_channels = ignore_channels
+
+    def forward(self, y_pr, y_gt):
+        y_pr = self.activation(y_pr)
+        return 1 - F.jaccard(
+            y_pr, y_gt,
+            eps=self.eps,
+            threshold=None,
+            ignore_channels=self.ignore_channels,
+        )
+
+
+class DiceLoss(base.Loss):
+
+    def __init__(self, eps=1., beta=1., activation=None, ignore_channels=None, **kwargs):
+        super().__init__(**kwargs)
+        self.eps = eps
+        self.beta = beta
+        self.activation = Activation(activation)
+        self.ignore_channels = ignore_channels
+
+    def forward(self, y_pr, y_gt):
+        y_pr = self.activation(y_pr)
+        return 1 - F.f_score(
+            y_pr, y_gt,
+            beta=self.beta,
+            eps=self.eps,
+            threshold=None,
+            ignore_channels=self.ignore_channels,
+        )
+
+
+class L1Loss(nn.L1Loss, base.Loss):
+    pass
+
+
+class MSELoss(nn.MSELoss, base.Loss):
+    pass
+
+
+class CrossEntropyLoss(nn.CrossEntropyLoss, base.Loss):
+    pass
+
+
+class NLLLoss(nn.NLLLoss, base.Loss):
+    pass
+
+
+class BCELoss(nn.BCELoss, base.Loss):
+    pass
+
+
+class BCEWithLogitsLoss(nn.BCEWithLogitsLoss, base.Loss):
+    pass
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/utils/meter.py b/segmentation_models_pytorch/segmentation_models_pytorch/utils/meter.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6e7fd3078189160dfcbbbcc188d8a0bb0c293e0
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/utils/meter.py
@@ -0,0 +1,61 @@
+import numpy as np
+
+
+class Meter(object):
+    '''Meters provide a way to keep track of important statistics in an online manner.
+    This class is abstract, but provides a standard interface for all meters to follow.
+    '''
+
+    def reset(self):
+        '''Resets the meter to default settings.'''
+        pass
+
+    def add(self, value):
+        '''Log a new value to the meter
+        Args:
+            value: Next result to include.
+        '''
+        pass
+
+    def value(self):
+        '''Get the value of the meter in the current state.'''
+        pass
+
+
+class AverageValueMeter(Meter):
+    def __init__(self):
+        super(AverageValueMeter, self).__init__()
+        self.reset()
+        self.val = 0
+
+    def add(self, value, n=1):
+        self.val = value
+        self.sum += value
+        self.var += value * value
+        self.n += n
+
+        if self.n == 0:
+            self.mean, self.std = np.nan, np.nan
+        elif self.n == 1:
+            self.mean = 0.0 + self.sum  # This is to force a copy in torch/numpy
+            self.std = np.inf
+            self.mean_old = self.mean
+            self.m_s = 0.0
+        else:
+            self.mean = self.mean_old + (value - n * self.mean_old) / float(self.n)
+            self.m_s += (value - self.mean_old) * (value - self.mean)
+            self.mean_old = self.mean
+            self.std = np.sqrt(self.m_s / (self.n - 1.0))
+
+    def value(self):
+        return self.mean, self.std
+
+    def reset(self):
+        self.n = 0
+        self.sum = 0.0
+        self.var = 0.0
+        self.val = 0.0
+        self.mean = np.nan
+        self.mean_old = 0.0
+        self.m_s = 0.0
+        self.std = np.nan
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/utils/metrics.py b/segmentation_models_pytorch/segmentation_models_pytorch/utils/metrics.py
new file mode 100644
index 0000000000000000000000000000000000000000..256f21f7c69bb94db484940fad9899189358639a
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/utils/metrics.py
@@ -0,0 +1,99 @@
+from . import base
+from . import functional as F
+from ..base.modules import Activation
+
+
+class IoU(base.Metric):
+    __name__ = 'iou_score'
+
+    def __init__(self, eps=1e-7, threshold=0.5, activation=None, ignore_channels=None, **kwargs):
+        super().__init__(**kwargs)
+        self.eps = eps
+        self.threshold = threshold
+        self.activation = Activation(activation)
+        self.ignore_channels = ignore_channels
+
+    def forward(self, y_pr, y_gt):
+        y_pr = self.activation(y_pr)
+        return F.iou(
+            y_pr, y_gt,
+            eps=self.eps,
+            threshold=self.threshold,
+            ignore_channels=self.ignore_channels,
+        )
+
+
+class Fscore(base.Metric):
+
+    def __init__(self, beta=1, eps=1e-7, threshold=0.5, activation=None, ignore_channels=None, **kwargs):
+        super().__init__(**kwargs)
+        self.eps = eps
+        self.beta = beta
+        self.threshold = threshold
+        self.activation = Activation(activation)
+        self.ignore_channels = ignore_channels
+
+    def forward(self, y_pr, y_gt):
+        y_pr = self.activation(y_pr)
+        return F.f_score(
+            y_pr, y_gt,
+            eps=self.eps,
+            beta=self.beta,
+            threshold=self.threshold,
+            ignore_channels=self.ignore_channels,
+        )
+
+
+class Accuracy(base.Metric):
+
+    def __init__(self, threshold=0.5, activation=None, ignore_channels=None, **kwargs):
+        super().__init__(**kwargs)
+        self.threshold = threshold
+        self.activation = Activation(activation)
+        self.ignore_channels = ignore_channels
+
+    def forward(self, y_pr, y_gt):
+        y_pr = self.activation(y_pr)
+        return F.accuracy(
+            y_pr, y_gt,
+            threshold=self.threshold,
+            ignore_channels=self.ignore_channels,
+        )
+
+
+class Recall(base.Metric):
+
+    def __init__(self, eps=1e-7, threshold=0.5, activation=None, ignore_channels=None, **kwargs):
+        super().__init__(**kwargs)
+        self.eps = eps
+        self.threshold = threshold
+        self.activation = Activation(activation)
+        self.ignore_channels = ignore_channels
+
+    def forward(self, y_pr, y_gt):
+        y_pr = self.activation(y_pr)
+        return F.recall(
+            y_pr, y_gt,
+            eps=self.eps,
+            threshold=self.threshold,
+            ignore_channels=self.ignore_channels,
+        )
+
+
+class Precision(base.Metric):
+
+    def __init__(self, eps=1e-7, threshold=0.5, activation=None, ignore_channels=None, **kwargs):
+        super().__init__(**kwargs)
+        self.eps = eps
+        self.threshold = threshold
+        self.activation = Activation(activation)
+        self.ignore_channels = ignore_channels
+
+    def forward(self, y_pr, y_gt):
+        y_pr = self.activation(y_pr)
+        return F.precision(
+            y_pr, y_gt,
+            eps=self.eps,
+            threshold=self.threshold,
+            ignore_channels=self.ignore_channels,
+        )
diff --git a/segmentation_models_pytorch/segmentation_models_pytorch/utils/train.py b/segmentation_models_pytorch/segmentation_models_pytorch/utils/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..f60b195e0a118b5c934b57f90bc7c44fa7741b83
--- /dev/null
+++ b/segmentation_models_pytorch/segmentation_models_pytorch/utils/train.py
@@ -0,0 +1,113 @@
+import sys
+import torch
+from tqdm import tqdm as tqdm
+from .meter import AverageValueMeter
+
+
+class Epoch:
+
+    def __init__(self, model, loss, metrics, stage_name, device='cpu', verbose=True):
+        self.model = model
+        self.loss = loss
+        self.metrics = metrics
+        self.stage_name = stage_name
+        self.verbose = verbose
+        self.device = device
+
+        self._to_device()
+
+    def _to_device(self):
+        self.model.to(self.device)
+        self.loss.to(self.device)
+        for metric in self.metrics:
+            metric.to(self.device)
+
+    def _format_logs(self, logs):
+        str_logs = ['{} - {:.4}'.format(k, v) for k, v in logs.items()]
+        s = ', '.join(str_logs)
+        return s
+
+    def batch_update(self, x, y):
+        raise NotImplementedError
+
+    def on_epoch_start(self):
+        pass
+
+    def run(self, dataloader):
+
+        self.on_epoch_start()
+
+        logs = {}
+        loss_meter = AverageValueMeter()
+        metrics_meters = {metric.__name__: AverageValueMeter() for metric in self.metrics}
+
+        with tqdm(dataloader, desc=self.stage_name, file=sys.stdout, disable=not (self.verbose)) as iterator:
+            for x, y in iterator:
+                x, y = x.to(self.device), y.to(self.device)
+                loss, y_pred = self.batch_update(x, y)
+
+                # update loss logs
+                loss_value = loss.cpu().detach().numpy()
+                loss_meter.add(loss_value)
+                loss_logs = {self.loss.__name__: loss_meter.mean}
+                logs.update(loss_logs)
+
+                # update metrics logs
+                for metric_fn in self.metrics:
+                    metric_value = metric_fn(y_pred, y).cpu().detach().numpy()
+                    metrics_meters[metric_fn.__name__].add(metric_value)
+                metrics_logs = {k: v.mean for k, v in metrics_meters.items()}
+                logs.update(metrics_logs)
+
+                if self.verbose:
+                    s = self._format_logs(logs)
+                    iterator.set_postfix_str(s)
+
+        return logs
+
+
+class TrainEpoch(Epoch):
+
+    def __init__(self, model, loss, metrics, optimizer, device='cpu', verbose=True):
+        super().__init__(
+            model=model,
+            loss=loss,
+            metrics=metrics,
+            stage_name='train',
+            device=device,
+            verbose=verbose,
+        )
+        self.optimizer = optimizer
+
+    def on_epoch_start(self):
+        self.model.train()
+
+    def batch_update(self, x, y):
+        self.optimizer.zero_grad()
+        prediction = self.model.forward(x)
+        loss = self.loss(prediction, y)
+        loss.backward()
+        self.optimizer.step()
+        return loss, prediction
+
+
+class ValidEpoch(Epoch):
+
+    def __init__(self, model, loss, metrics, device='cpu', verbose=True):
+        super().__init__(
+            model=model,
+            loss=loss,
+            metrics=metrics,
+            stage_name='valid',
+            device=device,
+            verbose=verbose,
+        )
+
+    def on_epoch_start(self):
+        self.model.eval()
+
+    def batch_update(self, x, y):
+        with torch.no_grad():
+            prediction = self.model.forward(x)
+            loss = self.loss(prediction, y)
+        return loss, prediction
diff --git a/segmentation_models_pytorch/setup.py b/segmentation_models_pytorch/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..df148eea77c7bc74d31c75b768fbce8c930e23c5
--- /dev/null
+++ b/segmentation_models_pytorch/setup.py
@@ -0,0 +1,131 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# Note: To use the 'upload' functionality of this file, you must:
+#   $ pip install twine
+
+import io
+import os
+import sys
+from shutil import rmtree
+
+from setuptools import find_packages, setup, Command
+
+# Package meta-data.
+NAME = 'segmentation_models_pytorch'
+DESCRIPTION = 'Image segmentation models with pre-trained backbones. PyTorch.'
+URL = 'https://github.com/qubvel/segmentation_models.pytorch'
+EMAIL = 'qubvel@gmail.com'
+AUTHOR = 'Pavel Yakubovskiy'
+REQUIRES_PYTHON = '>=3.0.0'
+VERSION = None
+
+# The rest you shouldn't have to touch too much :)
+# ------------------------------------------------
+# Except, perhaps the License and Trove Classifiers!
+# If you do change the License, remember to change the Trove Classifier for that!
+
+here = os.path.abspath(os.path.dirname(__file__))
+
+# What packages are required for this module to be executed?
+try:
+    with open(os.path.join(here, 'requirements.txt'), encoding='utf-8') as f:
+        REQUIRED = f.read().split('\n')
+except:
+    REQUIRED = []
+
+# What packages are optional?
+EXTRAS = {
+    'test': ['pytest']
+}
+
+# Import the README and use it as the long-description.
+# Note: this will only work if 'README.md' is present in your MANIFEST.in file!
+try:
+    with io.open(os.path.join(here, 'README.md'), encoding='utf-8') as f:
+        long_description = '\n' + f.read()
+except FileNotFoundError:
+    long_description = DESCRIPTION
+
+# Load the package's __version__.py module as a dictionary.
+about = {}
+if not VERSION:
+    with open(os.path.join(here, NAME, '__version__.py')) as f:
+        exec(f.read(), about)
+else:
+    about['__version__'] = VERSION
+
+
+class UploadCommand(Command):
+    """Support setup.py upload."""
+
+    description = 'Build and publish the package.'
+    user_options = []
+
+    @staticmethod
+    def status(s):
+        """Prints things in bold."""
+        print(s)
+
+    def initialize_options(self):
+        pass
+
+    def finalize_options(self):
+        pass
+
+    def run(self):
+        try:
+            self.status('Removing previous builds...')
+            rmtree(os.path.join(here, 'dist'))
+        except OSError:
+            pass
+
+        self.status('Building Source and Wheel (universal) distribution...')
+        os.system('{0} setup.py sdist bdist_wheel --universal'.format(sys.executable))
+
+        self.status('Uploading the package to PyPI via Twine...')
+        os.system('twine upload dist/*')
+
+        self.status('Pushing git tags...')
+        os.system('git tag v{0}'.format(about['__version__']))
+        os.system('git push --tags')
+
+        sys.exit()
+
+
+# Where the magic happens:
+setup(
+    name=NAME,
+    version=about['__version__'],
+    description=DESCRIPTION,
+    long_description=long_description,
+    long_description_content_type='text/markdown',
+    author=AUTHOR,
+    author_email=EMAIL,
+    python_requires=REQUIRES_PYTHON,
+    url=URL,
+    packages=find_packages(exclude=('tests', 'docs', 'images')),
+    # If your package is a single module, use this instead of 'packages':
+    # py_modules=['mypackage'],
+
+    # entry_points={
+    #     'console_scripts': ['mycli=mymodule:cli'],
+    # },
+    install_requires=REQUIRED,
+    extras_require=EXTRAS,
+    include_package_data=True,
+    license='MIT',
+    classifiers=[
+        # Trove classifiers
+        # Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers
+        'License :: OSI Approved :: MIT License',
+        'Programming Language :: Python',
+        'Programming Language :: Python :: 3',
+        'Programming Language :: Python :: Implementation :: CPython',
+        'Programming Language :: Python :: Implementation :: PyPy'
+    ],
+    # $ setup.py publish support.
+    cmdclass={
+        'upload': UploadCommand,
+    },
+)
diff --git a/segmentation_models_pytorch/tests/test_losses.py b/segmentation_models_pytorch/tests/test_losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..220208374ca9e73d6bcb06e2db6aa41f01d86f48
--- /dev/null
+++ b/segmentation_models_pytorch/tests/test_losses.py
@@ -0,0 +1,224 @@
+import pytest
+import torch
+import segmentation_models_pytorch as smp
+import segmentation_models_pytorch.losses._functional as F
+from segmentation_models_pytorch.losses import DiceLoss, JaccardLoss, SoftBCEWithLogitsLoss, SoftCrossEntropyLoss
+
+
+def test_focal_loss_with_logits():
+    input_good = torch.tensor([10, -10, 10]).float()
+    input_bad = torch.tensor([-1, 2, 0]).float()
+    target = torch.tensor([1, 0, 1])
+
+    loss_good = F.focal_loss_with_logits(input_good, target)
+    loss_bad = F.focal_loss_with_logits(input_bad, target)
+    assert loss_good < loss_bad
+
+
+def test_softmax_focal_loss_with_logits():
+    input_good = torch.tensor([[0, 10, 0], [10, 0, 0], [0, 0, 10]]).float()
+    input_bad = torch.tensor([[0, -10, 0], [0, 10, 0], [0, 0, 10]]).float()
+    target = torch.tensor([1, 0, 2]).long()
+
+    loss_good = F.softmax_focal_loss_with_logits(input_good, target)
+    loss_bad = F.softmax_focal_loss_with_logits(input_bad, target)
+    assert loss_good < loss_bad
+
+
+@pytest.mark.parametrize(
+    ["y_true", "y_pred", "expected", "eps"],
+    [
+        [[1, 1, 1, 1], [1, 1, 1, 1], 1.0, 1e-5],
+        [[0, 1, 1, 0], [0, 1, 1, 0], 1.0, 1e-5],
+        [[1, 1, 1, 1], [1, 1, 0, 0], 0.5, 1e-5],
+    ],
+)
+def test_soft_jaccard_score(y_true, y_pred, expected, eps):
+    y_true = torch.tensor(y_true, dtype=torch.float32)
+    y_pred = torch.tensor(y_pred, dtype=torch.float32)
+    actual = F.soft_jaccard_score(y_pred, y_true, eps=eps)
+    assert float(actual) == pytest.approx(expected, eps)
+
+
+@pytest.mark.parametrize(
+    ["y_true", "y_pred", "expected", "eps"],
+    [
+        [[[1, 1, 0, 0], [0, 0, 1, 1]], [[1, 1, 0, 0], [0, 0, 1, 1]], 1.0, 1e-5],
+        [[[1, 1, 0, 0], [0, 0, 1, 1]], [[0, 0, 1, 0], [0, 1, 0, 0]], 0.0, 1e-5],
+        [[[1, 1, 0, 0], [0, 0, 0, 1]], [[1, 1, 0, 0], [0, 0, 0, 0]], 0.5, 1e-5],
+    ],
+)
+def test_soft_jaccard_score_2(y_true, y_pred, expected, eps):
+    y_true = torch.tensor(y_true, dtype=torch.float32)
+    y_pred = torch.tensor(y_pred, dtype=torch.float32)
+    actual = F.soft_jaccard_score(y_pred, y_true, dims=[1], eps=eps)
+    actual = actual.mean()
+    assert float(actual) == pytest.approx(expected, eps)
+
+
+@pytest.mark.parametrize(
+    ["y_true", "y_pred", "expected", "eps"],
+    [
+        [[1, 1, 1, 1], [1, 1, 1, 1], 1.0, 1e-5],
+        [[0, 1, 1, 0], [0, 1, 1, 0], 1.0, 1e-5],
+        [[1, 1, 1, 1], [1, 1, 0, 0], 2.0 / 3.0, 1e-5],
+    ],
+)
+def test_soft_dice_score(y_true, y_pred, expected, eps):
+    y_true = torch.tensor(y_true, dtype=torch.float32)
+    y_pred = torch.tensor(y_pred, dtype=torch.float32)
+    actual = F.soft_dice_score(y_pred, y_true, eps=eps)
+    assert float(actual) == pytest.approx(expected, eps)
+
+
+@torch.no_grad()
+def test_dice_loss_binary():
+    eps = 1e-5
+    criterion = DiceLoss(mode=smp.losses.BINARY_MODE, from_logits=False)
+
+    # Ideal case
+    y_pred = torch.tensor([1.0, 1.0, 1.0]).view(1, 1, 1, -1)
+    y_true = torch.tensor(([1, 1, 1])).view(1, 1, 1, -1)
+    loss = criterion(y_pred, y_true)
+    assert float(loss) == pytest.approx(0.0, abs=eps)
+
+    y_pred = torch.tensor([1.0, 0.0, 1.0]).view(1, 1, 1, -1)
+    y_true = torch.tensor(([1, 0, 1])).view(1, 1, 1, -1)
+    loss = criterion(y_pred, y_true)
+    assert float(loss) == pytest.approx(0.0, abs=eps)
+
+    y_pred = torch.tensor([0.0, 0.0, 0.0]).view(1, 1, 1, -1)
+    y_true = torch.tensor(([0, 0, 0])).view(1, 1, 1, -1)
+    loss = criterion(y_pred, y_true)
+    assert float(loss) == pytest.approx(0.0, abs=eps)
+
+    # Worst case
+    y_pred = torch.tensor([1.0, 1.0, 1.0]).view(1, 1, -1)
+    y_true = torch.tensor([0, 0, 0]).view(1, 1, 1, -1)
+    loss = criterion(y_pred, y_true)
+    assert float(loss) == pytest.approx(0.0, abs=eps)
+
+    y_pred = torch.tensor([1.0, 0.0, 1.0]).view(1, 1, -1)
+    y_true = torch.tensor([0, 1, 0]).view(1, 1, 1, -1)
+    loss = criterion(y_pred, y_true)
+    assert float(loss) == pytest.approx(1.0, abs=eps)
+
+    y_pred = torch.tensor([0.0, 0.0, 0.0]).view(1, 1, -1)
+    y_true = torch.tensor([1, 1, 1]).view(1, 1, 1, -1)
+    loss = criterion(y_pred, y_true)
+    assert float(loss) == pytest.approx(1.0, abs=eps)
+
+
+@torch.no_grad()
+def test_binary_jaccard_loss():
+    eps = 1e-5
+    criterion = JaccardLoss(mode=smp.losses.BINARY_MODE, from_logits=False)
+
+    # Ideal case
+    y_pred = torch.tensor([1.0]).view(1, 1, 1, 1)
+    y_true = torch.tensor(([1])).view(1, 1, 1, 1)
+    loss = criterion(y_pred, y_true)
+    assert float(loss) == pytest.approx(0.0, abs=eps)
+
+    y_pred = torch.tensor([1.0, 0.0, 1.0]).view(1, 1, 1, -1)
+    y_true = torch.tensor(([1, 0, 1])).view(1, 1, 1, -1)
+    loss = criterion(y_pred, y_true)
+    assert float(loss) == pytest.approx(0.0, abs=eps)
+
+    y_pred = torch.tensor([0.0, 0.0, 0.0]).view(1, 1, 1, -1)
+    y_true = torch.tensor(([0, 0, 0])).view(1, 1, 1, -1)
+    loss = criterion(y_pred, y_true)
+    assert float(loss) == pytest.approx(0.0, abs=eps)
+
+    # Worst case
+    y_pred = torch.tensor([1.0, 1.0, 1.0]).view(1, 1, -1)
+    y_true = torch.tensor([0, 0, 0]).view(1, 1, 1, -1)
+    loss = criterion(y_pred, y_true)
+    assert float(loss) == pytest.approx(0.0, abs=eps)
+
+    y_pred = torch.tensor([1.0, 0.0, 1.0]).view(1, 1, -1)
+    y_true = torch.tensor([0, 1, 0]).view(1, 1, 1, -1)
+    loss = criterion(y_pred, y_true)
+    assert float(loss) == pytest.approx(1.0, eps)
+
+    y_pred = torch.tensor([0.0, 0.0, 0.0]).view(1, 1, -1)
+    y_true = torch.tensor([1, 1, 1]).view(1, 1, 1, -1)
+    loss = criterion(y_pred, y_true)
+    assert float(loss) == pytest.approx(1.0, eps)
+
+
+@torch.no_grad()
+def test_multiclass_jaccard_loss():
+    eps = 1e-5
+    criterion = JaccardLoss(mode=smp.losses.MULTICLASS_MODE, from_logits=False)
+
+    # Ideal case
+    y_pred = torch.tensor([[[1.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 1.0]]])
+    y_true = torch.tensor([[0, 0, 1, 1]])
+
+    loss = criterion(y_pred, y_true)
+    assert float(loss) == pytest.approx(0.0, abs=eps)
+
+    # Worst case
+    y_pred = torch.tensor([[[1.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 1.0]]])
+    y_true = torch.tensor([[1, 1, 0, 0]])
+
+    loss = criterion(y_pred, y_true)
+    assert float(loss) == pytest.approx(1.0, abs=eps)
+
+    # 1 - 1/3 case
+    y_pred = torch.tensor([[[1.0, 0.0, 1.0, 0.0], [0.0, 1.0, 0.0, 1.0]]])
+    y_true = torch.tensor([[1, 1, 0, 0]])
+
+    loss = criterion(y_pred, y_true)
+    assert float(loss) == pytest.approx(1.0 - 1.0 / 3.0, abs=eps)
+
+
+@torch.no_grad()
+def test_multilabel_jaccard_loss():
+    eps = 1e-5
+    criterion = JaccardLoss(mode=smp.losses.MULTILABEL_MODE, from_logits=False)
+
+    # Ideal case
+    y_pred = torch.tensor([[[1.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 1.0]]])
+    y_true = torch.tensor([[[1.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 1.0]]])
+
+    loss = criterion(y_pred, y_true)
+    assert float(loss) == pytest.approx(0.0, abs=eps)
+
+    # Worst case
+    y_pred = torch.tensor([[[1.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 1.0]]])
+    y_true = 1 - y_pred
+    loss = criterion(y_pred, y_true)
+    assert float(loss) == pytest.approx(1.0, abs=eps)
+
+    # 1 - 1/3 case
+    y_pred = torch.tensor([[[0.0, 1.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0]]])
+    y_true = torch.tensor([[[1.0, 1.0, 0.0, 0.0], [1.0, 1.0, 0.0, 0.0]]])
+
+    loss = criterion(y_pred, y_true)
+    assert float(loss) == pytest.approx(1.0 - 1.0 / 3.0, abs=eps)
+
+
+@torch.no_grad()
+def test_soft_ce_loss():
+    criterion = SoftCrossEntropyLoss(smooth_factor=0.1, ignore_index=-100)
+
+    # Ideal case
+    y_pred = torch.tensor([[+9, -9, -9, -9], [-9, +9, -9, -9], [-9, -9, +9, -9], [-9, -9, -9, +9]]).float()
+    y_true = torch.tensor([0, 1, -100, 3]).long()
+
+    loss = criterion(y_pred, y_true)
+    print(loss)
+
+
+@torch.no_grad()
+def test_soft_bce_loss():
+    criterion = SoftBCEWithLogitsLoss(smooth_factor=0.1, ignore_index=-100)
+
+    # Ideal case
+    y_pred = torch.tensor([-9, 9, 1, 9, -9]).float()
+    y_true = torch.tensor([0, 1, -100, 1, 0]).long()
+
+    loss = criterion(y_pred, y_true)
+    print(loss)
diff --git a/segmentation_models_pytorch/tests/test_models.py b/segmentation_models_pytorch/tests/test_models.py
new file mode 100644
index 0000000000000000000000000000000000000000..29f60f116f40658bdf2dd6f7ef3c68b865d4fa8b
--- /dev/null
+++ b/segmentation_models_pytorch/tests/test_models.py
@@ -0,0 +1,143 @@
+import os
+import sys
+import mock
+import pytest
+import torch
+
+# mock detection module
+sys.modules["torchvision._C"] = mock.Mock()
+import segmentation_models_pytorch as smp
+
+IS_TRAVIS = os.environ.get("TRAVIS", False)
+
+
+def get_encoders():
+    travis_exclude_encoders = [
+        "senet154",
+        "resnext101_32x16d",
+        "resnext101_32x32d",
+        "resnext101_32x48d",
+    ]
+    encoders = smp.encoders.get_encoder_names()
+    if IS_TRAVIS:
+        encoders = [e for e in encoders if e not in travis_exclude_encoders]
+    return encoders
+
+
+ENCODERS = get_encoders()
+DEFAULT_ENCODER = "resnet18"
+
+
+def get_sample(model_class):
+    if model_class in [smp.Unet, smp.Linknet, smp.FPN, smp.PSPNet, smp.UnetPlusPlus, smp.MAnet]:
+        sample = torch.ones([1, 3, 64, 64])
+    elif model_class == smp.PAN:
+        sample = torch.ones([2, 3, 256, 256])
+    elif model_class == smp.DeepLabV3:
+        sample = torch.ones([2, 3, 128, 128])
+    else:
+        raise ValueError("Not supported model class {}".format(model_class))
+    return sample
+
+
+def _test_forward(model, sample, test_shape=False):
+    with torch.no_grad():
+        out = model(sample)
+    if test_shape:
+        assert out.shape[2:] == sample.shape[2:]
+
+
+def _test_forward_backward(model, sample, test_shape=False):
+    out = model(sample)
+    out.mean().backward()
+    if test_shape:
+        assert out.shape[2:] == sample.shape[2:]
+
+
+@pytest.mark.parametrize("encoder_name", ENCODERS)
+@pytest.mark.parametrize("encoder_depth", [3, 5])
+@pytest.mark.parametrize("model_class", [smp.FPN, smp.PSPNet, smp.Linknet, smp.Unet, smp.UnetPlusPlus])
+def test_forward(model_class, encoder_name, encoder_depth, **kwargs):
+    if model_class is smp.Unet or model_class is smp.UnetPlusPlus or model_class is smp.MAnet:
+        kwargs["decoder_channels"] = (16, 16, 16, 16, 16)[-encoder_depth:]
+    model = model_class(
+        encoder_name, encoder_depth=encoder_depth, encoder_weights=None, **kwargs
+    )
+    sample = get_sample(model_class)
+    model.eval()
+    if encoder_depth == 5 and model_class != smp.PSPNet:
+        test_shape = True
+    else:
+        test_shape = False
+
+    _test_forward(model, sample, test_shape)
+
+
+@pytest.mark.parametrize(
+    "model_class",
+    [smp.PAN, smp.FPN, smp.PSPNet, smp.Linknet, smp.Unet, smp.UnetPlusPlus, smp.MAnet, smp.DeepLabV3]
+)
+def test_forward_backward(model_class):
+    sample = get_sample(model_class)
+    model = model_class(DEFAULT_ENCODER, encoder_weights=None)
+    _test_forward_backward(model, sample)
+
+
+@pytest.mark.parametrize("model_class", [smp.PAN, smp.FPN, smp.PSPNet, smp.Linknet, smp.Unet, smp.UnetPlusPlus, smp.MAnet])
+def test_aux_output(model_class):
+    model = model_class(
+        DEFAULT_ENCODER, encoder_weights=None, aux_params=dict(classes=2)
+    )
+    sample = get_sample(model_class)
+    label_size = (sample.shape[0], 2)
+    mask, label = model(sample)
+    assert label.size() == label_size
+
+
+@pytest.mark.parametrize("upsampling", [2, 4, 8])
+@pytest.mark.parametrize("model_class", [smp.FPN, smp.PSPNet])
+def test_upsample(model_class, upsampling):
+    default_upsampling = 4 if model_class is smp.FPN else 8
+    model = model_class(DEFAULT_ENCODER, encoder_weights=None, upsampling=upsampling)
+    sample = get_sample(model_class)
+    mask = model(sample)
+    assert mask.size()[-1] / 64 == upsampling / default_upsampling
+
+
+@pytest.mark.parametrize("model_class", [smp.FPN])
+@pytest.mark.parametrize("encoder_name", ENCODERS)
+@pytest.mark.parametrize("in_channels", [1, 2, 4])
+def test_in_channels(model_class, encoder_name, in_channels):
+    sample = torch.ones([1, in_channels, 64, 64])
+    model = model_class(DEFAULT_ENCODER, encoder_weights=None, in_channels=in_channels)
+    model.eval()
+    with torch.no_grad():
+        model(sample)
+
+    assert model.encoder._in_channels == in_channels
+
+
+@pytest.mark.parametrize("encoder_name", ENCODERS)
+def test_dilation(encoder_name):
+    if (encoder_name in ['inceptionresnetv2', 'xception', 'inceptionv4'] or
+            encoder_name.startswith('vgg') or encoder_name.startswith('densenet') or
+            encoder_name.startswith('timm-res')):
+        return
+
+    encoder = smp.encoders.get_encoder(encoder_name)
+    encoder.make_dilated(
+        stage_list=[5],
+        dilation_list=[2],
+    )
+
+    encoder.eval()
+    with torch.no_grad():
+        sample = torch.ones([1, 3, 64, 64])
+        output = encoder(sample)
+
+    shapes = [out.shape[-1] for out in output]
+    assert shapes == [64, 32, 16, 8, 4, 4]  # last downsampling replaced with dilation
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])
diff --git a/segmentation_models_pytorch/tests/test_preprocessing.py b/segmentation_models_pytorch/tests/test_preprocessing.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7dd63e98d08c76f8866f9a3d894858d0e69f694
--- /dev/null
+++ b/segmentation_models_pytorch/tests/test_preprocessing.py
@@ -0,0 +1,51 @@
+import os
+import sys
+import mock
+import pytest
+import numpy as np
+
+# mock detection module 
+sys.modules['torchvision._C'] = mock.Mock()
+
+import segmentation_models_pytorch as smp
+
+
+def _test_preprocessing(inp, out, **params):
+    preprocessed_output = smp.encoders.preprocess_input(inp, **params)
+    assert np.allclose(preprocessed_output, out)
+
+
+def test_mean():
+    inp = np.ones((32, 32, 3))
+    out = np.zeros((32, 32, 3))
+    mean = (1, 1, 1)
+    _test_preprocessing(inp, out, mean=mean)
+
+
+def test_std():
+    inp = np.ones((32, 32, 3)) * 255
+    out = np.ones((32, 32, 3))
+    std = (255, 255, 255)
+    _test_preprocessing(inp, out, std=std)
+
+
+def test_input_range():
+    inp = np.ones((32, 32, 3))
+    out = np.ones((32, 32, 3))
+    _test_preprocessing(inp, out, input_range=(0, 1))
+    _test_preprocessing(inp * 255, out, input_range=(0, 1))
+    _test_preprocessing(inp * 255, out * 255, input_range=(0, 255))
+
+
+def test_input_space():
+    inp = np.stack(
+        [np.ones((32, 32)),
+        np.zeros((32, 32))],
+        axis=-1
+    )
+    out = np.stack(
+        [np.zeros((32, 32)),
+         np.ones((32, 32))],
+        axis=-1
+    )
+    _test_preprocessing(inp, out, input_space='BGR')
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diff --git a/utils/augment.py b/utils/augment.py
new file mode 100644
index 0000000000000000000000000000000000000000..5591b2144ba44a7d46cee8dc4a1472096093202e
--- /dev/null
+++ b/utils/augment.py
@@ -0,0 +1,159 @@
+from PIL import Image, ImageEnhance, ImageOps
+import numpy as np
+import random
+from torchvision import transforms
+
+class TNetPolicy(object):
+    """ 
+        Applies the augmentation policy used in Jun et al's coronary artery segmentation T-Net
+        https://arxiv.org/abs/1905.04197. As described by the authors, first they zoom-in or zoom-out at a 
+        random ratio within +/- 20%. Then, the image is shifted, horizontally and vertically, at a random 
+        ratio within +/- 20% of the image size (512 x 512). Then, the angiography is rotated between 
+        +/- 30 degrees. The rotation angle is not larger because actual angiographies do not deviate much
+        from this range. Finally, because the brightness of the angiography image can vary, the brightness is
+        also changed within +/- 40% at random rates.
+        Referred in the results as Aug1.
+        Example:
+        >>> policy = TNetPolicy()
+        >>> transformed_img, transformed_mask = policy(image, mask)
+    """
+    def __init__(self, scale_ranges = [0.8, 1.2], img_size = [512, 512], translate = [0.2, 0.2], rotation = [-30, 30], brightness = 0.4):
+        self.scale_ranges = scale_ranges 
+        self.img_size = img_size
+        self.translate = translate
+        self.rotation = rotation
+        self.brightness = brightness
+
+    def __call__(self, image, mask = None):
+            tf_mask = None
+            tf_list = list() # List of transformation
+
+            # Random zoom-in or zoom-out of -20% to 20%
+            params = transforms.RandomAffine.get_params(degrees = [0, 0], translate = [0, 0], \
+            scale_ranges = self.scale_ranges, img_size = self.img_size, shears = [0, 0])
+            tf_image = transforms.functional.affine(image, params[0], params[1], params[2], params[3])
+            if mask is not None:
+                tf_mask = transforms.functional.affine(mask, params[0], params[1], params[2], params[3])
+
+            # Random horizontal and vertical shift of -20% to 20%
+            params = transforms.RandomAffine.get_params(degrees = [0, 0], translate = self.translate, \
+            scale_ranges = [1, 1], img_size = self.img_size, shears = [0, 0])
+            tf_image = transforms.functional.affine(tf_image, params[0], params[1], params[2], params[3])
+            if mask is not None:
+                tf_mask = transforms.functional.affine(tf_mask, params[0], params[1], params[2], params[3])
+
+            # Random rotation of -30 to 30 degress
+            angle = transforms.RandomRotation.get_params(self.rotation)
+            tf_image = transforms.functional.rotate(tf_image, angle)
+            if mask is not None:
+                tf_mask = transforms.functional.rotate(tf_mask, angle)
+
+            # Random brightness change of -40% to 40%
+            tf = transforms.ColorJitter(brightness = self.brightness)
+            tf_image = tf(tf_image)
+
+            if mask is not None:
+                return (tf_image, tf_mask)
+            else:
+                return tf_image
+    
+    def __repr__(self):
+        return "TNet Coronary Artery Segmentation Augmentation Policy"
+
+class RetinaPolicy(object):
+    def __init__(self, scale_ranges = [1, 1.1], img_size = [512, 512], translate = [0.1, 0.1], rotation = [-20, 20], crop_dims = [480, 480], brightness = None):
+        self.scale_ranges = scale_ranges 
+        self.img_size = img_size
+        self.translate = translate
+        self.rotation = rotation
+        self.brightness = brightness
+        self.crop_dims = crop_dims
+
+    def __call__(self, image, mask = None):
+            tf_mask = None
+
+            # Random crop
+            i, j, h, w = transforms.RandomCrop.get_params(image, self.crop_dims)
+            tf_image = transforms.functional.crop(image, i, j, h, w)
+            if mask is not None:
+                tf_mask = transforms.functional.crop(mask, i, j, h, w) 
+
+            # Random rotation of -20 to 20 degress
+            angle = transforms.RandomRotation.get_params(self.rotation)
+            tf_image = transforms.functional.rotate(tf_image, angle)
+            if mask is not None:
+                tf_mask = transforms.functional.rotate(tf_mask, angle)   
+
+            # Random horizontal and vertical shift of -10% to 10%
+            params = transforms.RandomAffine.get_params(degrees = [0, 0], translate = self.translate, \
+            scale_ranges = [1, 1], img_size = self.img_size, shears = [0, 0])
+            tf_image = transforms.functional.affine(tf_image, params[0], params[1], params[2], params[3])
+            if mask is not None:
+                tf_mask = transforms.functional.affine(tf_mask, params[0], params[1], params[2], params[3])
+
+            # TODO: -10% to 10% may make more sense, due to the existance of images with black padding borders
+            # Random zoom-in of 0% to 10%
+            params = transforms.RandomAffine.get_params(degrees = [0, 0], translate = [0, 0], \
+            scale_ranges = self.scale_ranges, img_size = self.img_size, shears = [0, 0])
+            tf_image = transforms.functional.affine(tf_image, params[0], params[1], params[2], params[3])
+            if mask is not None:
+                tf_mask = transforms.functional.affine(tf_mask, params[0], params[1], params[2], params[3])
+
+            # TODO: change brightness too
+            # Random brightness change
+            if self.brightness is not None:
+                tf = transforms.ColorJitter(brightness = self.brightness)
+                tf_image = tf(tf_image)
+
+            if mask is not None:
+                return (tf_image, tf_mask)
+            else:
+                return tf_image
+
+    def __repr__(self):
+        return "Retinal Vessel Segmentation Augmentation Policy"
+
+class CoronaryPolicy(object):
+    def __init__(self, scale_ranges = [1, 1.1], img_size = [512, 512], translate = [0.1, 0.1], rotation = [-20, 20], brightness = None):
+        self.scale_ranges = scale_ranges 
+        self.img_size = img_size
+        self.translate = translate
+        self.rotation = rotation
+        self.brightness = brightness
+
+    def __call__(self, image, mask = None):
+            tf_mask = None
+            # Random rotation of -20 to 20 degress
+            angle = transforms.RandomRotation.get_params(self.rotation)
+            tf_image = transforms.functional.rotate(image, angle)
+            if mask is not None:
+                tf_mask = transforms.functional.rotate(mask, angle)   
+
+            # Random horizontal and vertical shift of -10% to 10%
+            params = transforms.RandomAffine.get_params(degrees = [0, 0], translate = self.translate, \
+            scale_ranges = [1, 1], img_size = self.img_size, shears = [0, 0])
+            tf_image = transforms.functional.affine(tf_image, params[0], params[1], params[2], params[3])
+            if mask is not None:
+                tf_mask = transforms.functional.affine(tf_mask, params[0], params[1], params[2], params[3])
+
+            # TODO: -10% to 10% may make more sense, due to the existance of images with black padding borders
+            # Random zoom-in of 0% to 10%
+            params = transforms.RandomAffine.get_params(degrees = [0, 0], translate = [0, 0], \
+            scale_ranges = self.scale_ranges, img_size = self.img_size, shears = [0, 0])
+            tf_image = transforms.functional.affine(tf_image, params[0], params[1], params[2], params[3])
+            if mask is not None:
+                tf_mask = transforms.functional.affine(tf_mask, params[0], params[1], params[2], params[3])
+
+            # TODO: change brightness too
+            # Random brightness change
+            if self.brightness is not None:
+                tf = transforms.ColorJitter(brightness = self.brightness)
+                tf_image = tf(tf_image)
+
+            if mask is not None:
+                return (tf_image, tf_mask)
+            else:
+                return tf_image
+
+    def __repr__(self):
+        return "Coronary Artery Segmentation Augmentation Policy"
\ No newline at end of file
diff --git a/utils/dataset.py b/utils/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..8232caba46af8958c1f3db2d3762b4864304330b
--- /dev/null
+++ b/utils/dataset.py
@@ -0,0 +1,315 @@
+from os.path import splitext
+from os import listdir
+from typing import Dict, List
+import numpy
+from glob import glob
+import torch
+from torch.utils.data import Dataset
+import logging
+from PIL import Image
+import torch.nn.functional as F
+from utils.augment import *
+import cv2
+import utils.utils 
+
+
+
+r"""
+Defines the `BasicSegmentationDataset` and `CoronaryArterySegmentationDatasets`, which extend the `Dataset` and `BasicSegmentationDataset` \ 
+classes, respectively. Each class defines the specific methods needed for data processing and a method :func:`__getitem__` to return samples.
+"""
+
+class BasicSegmentationDataset(Dataset):
+    r"""
+    Implements a basic dataset for segmentation tasks, with methods for image and mask scaling and normalization. \
+    The filenames of the segmentation ground truths must be equal to the filenames of the images to be segmented, \
+    except for a possible suffix.
+
+    Args:
+        imgs_dir (str): path to the directory containing the images to be segmented.
+        masks_dir (str): path to the directory containing the segmentation ground truths.
+        scale (float, optional): image scale, between 0 and 1, to be used in the segmentation.
+        mask_suffix (str, optional): suffix to be added to an image's filename to obtain its 
+            ground truth filename.
+    """
+
+    def __init__(self, imgs_dir: str, masks_dir: str, scale: float = 1, mask_suffix: str = ''):
+        self.imgs_dir = imgs_dir
+        self.masks_dir = masks_dir
+        self.scale = scale
+        self.mask_suffix = mask_suffix
+        assert 0 < scale <= 1, 'Scale must be between 0 and 1'
+
+        self.ids = [splitext(file)[0] for file in listdir(imgs_dir)
+                    if not file.startswith('.')]
+        logging.info(f'Creating dataset with {len(self.ids)} examples')
+
+    def __len__(self) -> int:
+        r"""
+        Returns the size of the dataset.
+        """
+        return len(self.ids)
+
+    @classmethod
+    def preprocess(cls, pil_img: Image, scale: float) -> Image:
+        r"""
+        Preprocesses an `Image`, rescaling it and returning it as a NumPy array in 
+        the CHW format.
+
+        Args:
+            pil_imgs (Image): object of class `Image` to be preprocessed.
+            scale (float): image scale, between 0 and 1.
+        """
+        w, h = pil_img.size
+        newW, newH = int(scale * w), int(scale * h)
+        assert newW > 0 and newH > 0, 'Scale is too small'
+        pil_img = pil_img.resize((newW, newH))
+
+        img_nd = numpy.array(pil_img)
+
+        if len(img_nd.shape) == 2:
+            img_nd = numpy.expand_dims(img_nd, axis=2)
+
+        # HWC to CHW
+        img_trans = img_nd.transpose((2, 0, 1))
+        if img_trans.max() > 1:
+            img_trans = img_trans / 255
+
+        return img_trans
+
+    def __getitem__(self, i) -> Dict[List[torch.FloatTensor], List[torch.FloatTensor]]:
+        r"""
+        Returns two tensors: an image and the corresponding mask. 
+        """
+        idx = self.ids[i]
+        mask_file = glob(self.masks_dir + idx + self.mask_suffix + '.*')
+        img_file = glob(self.imgs_dir + idx + '.*')
+
+        assert len(mask_file) == 1, \
+            f'Either no mask or multiple masks found for the ID {idx}: {mask_file}'
+        assert len(img_file) == 1, \
+            f'Either no image or multiple images found for the ID {idx}: {img_file}'
+        mask = Image.open(mask_file[0])
+        img = Image.open(img_file[0])
+        
+        assert img.size == mask.size, \
+            f'Image and mask {idx} should be the same size, but are {img.size} and {mask.size}'
+
+        img = self.preprocess(img, self.scale)
+        mask = self.preprocess(mask, self.scale)
+
+        return {
+            'image': [torch.from_numpy(img).type(torch.FloatTensor)],
+            'mask': [torch.from_numpy(mask).type(torch.FloatTensor)]
+        }
+
+class CoronaryDataset(BasicSegmentationDataset):
+    r"""
+    Implements a dataset for the Retinal Vessel Segmentation task
+
+    Args:
+        imgs_dir (str): path to the directory containing the images to be segmented.
+        masks_dir (str): path to the directory containing the segmentation ground truths.
+        scale (float, optional): image scale, between 0 and 1, to be used in the segmentation.
+        augmentation_ratio (int, optional): number of augmentations to generate per image.
+        crop_size (int, optional): size of the square image to be fed to the model.
+        aug_policy (str, optional): data augmentation policy.
+    """
+    # Number of classes, including the background class
+    n_classes = 2
+
+    # Maps maks grayscale value to mask class index
+    gray2class_mapping = {
+        0: 0,
+        255: 1
+    }
+
+    # Maps mask grayscale value to mask RGB value
+    gray2rgb_mapping = {
+        0: (0, 0, 0),
+        255: (255, 255, 255)
+    }
+    
+    rgb2class_mapping = {
+        (0, 0, 0): 0,
+        (255, 255, 255): 1
+    }
+
+    def __init__(self, imgs_dir: str, masks_dir: str, scale: float = 1, augmentation_ratio: int = 0, crop_size: int = 512, aug_policy: str = 'retina'):
+        super().__init__(imgs_dir, masks_dir, scale)
+        self.augmentation_ratio = augmentation_ratio
+        self.policy = aug_policy
+        self.crop_size = crop_size
+
+    @classmethod
+    def mask_img2class_mask(cls, pil_mask: Image, scale: float) -> numpy.array:
+        r"""
+        Preprocesses a grayscale `Image` containing a segmentation mask, rescaling it, converting its grayscale values \
+        to class indices and returning it as a NumPy array in the CHW format.
+
+        Args:
+            pil_imgs (Image): object of class `Image` to be preprocessed.
+            scale (float): image scale, between 0 and 1.
+        """
+        w, h = pil_mask.size
+        newW, newH = int(scale * w), int(scale * h)
+        assert newW > 0 and newH > 0, 'Scale is too small'
+        pil_mask = pil_mask.resize((newW, newH))
+
+        if pil_mask.mode != "L":
+            pil_mask = pil_mask.convert(mode="L")
+        mask_nd = numpy.array(pil_mask)
+
+        if len(mask_nd.shape) == 2:
+            mask_nd = numpy.expand_dims(mask_nd, axis=2)
+
+        # HWC to CHW
+        mask = mask_nd.transpose((2, 0, 1))
+        mask = mask / 255
+
+        return mask
+
+    @classmethod
+    def one_hot2mask(cls, one_hot_mask: torch.FloatTensor, shape: str = 'CHW') -> numpy.array:
+        r"""
+        Returns the one-channel mask (1HW) corresponding to the CHW one-hot encoded one.
+        """
+        # Assuming tensor in CHW shape
+        if shape == 'CHW':
+            return numpy.argmax(one_hot_mask.detach().numpy(), axis=0)
+        elif shape == 'NCHW':
+            return numpy.argmax(one_hot_mask.detach().numpy(), axis=1)
+        return numpy.argmax(one_hot_mask.detach().numpy(), axis=0)
+
+    @classmethod
+    def mask2one_hot(cls, mask_tensor: torch.FloatTensor, output_shape: str = 'NHWC') -> torch.Tensor:
+        r"""
+        Returns the received `FloatTensor` in the N1HW shape to a one hot encoded `LongTensor` in the NHWC shape.\
+            Can return in NCHW shape is specified.
+
+        Args:
+            mask_tensor (FloatTensor): N1HW FloatTensor to be one-hot encoded.
+            output_shape (str): NHWC or NCHW.
+        """
+        assert output_shape == 'NHWC' or output_shape == 'NCHW', 'Invalid output shape specified'
+
+        # Assuming tensor in NCHW = N1HW shape
+        if output_shape == 'NHWC':
+            return F.one_hot(mask_tensor, cls.n_classes).squeeze(1)
+        # Assuming tensor in N1HW shape
+        elif output_shape == 'NCHW':
+            return torch.transpose(torch.transpose(F.one_hot(mask_tensor, cls.n_classes), 2, 3), 1, 2)
+
+    @classmethod
+    def class2gray(cls, mask: numpy.array) -> numpy.array:
+        r"""
+        Replaces the class labels in a numpy array represented mask by their grayscale values, according to `gray2class_mapping`.
+        """
+        assert len(cls.gray2class_mapping) == cls.n_classes, \
+            f'Number of class mappings - {len(cls.gray2class_mapping)} - should be the same as the number of classes - {cls.n_classes}'
+        for color, label in cls.gray2class_mapping.items():
+            mask[mask == label] = color
+        return mask
+    
+    @classmethod 
+    def gray2rgb(cls, img: Image) -> Image:
+        r"""
+        Converts a grayscale image into an RGB one, according to gray2rgb_mapping.
+        """
+        rgb_img = Image.new("RGB", img.size)
+        for x in range(img.size[0]):
+            for y in range(img.size[1]):
+                rgb_img.putpixel((x, y), cls.gray2rgb_mapping[img.getpixel((x, y))])
+        return rgb_img
+
+    @classmethod
+    def mask2image(cls, mask: numpy.array) -> Image:
+        r"""
+        Converts a one-channel mask (1HW) with class indices into an RGB image, according to gray2class_mapping and gray2rgb_mapping.
+        """
+        return cls.gray2rgb(Image.fromarray(cls.class2gray(mask).astype(numpy.uint8)))
+
+    def augment(self, image, mask, policy = 'retina', augmentation_ratio = 0):
+        """
+        Returns a list with the original image and mask and augmented versions of them. 
+        The number of augmented images and masks is equal to the specified augmentation_ratio.
+        The policy is chosen by the policy argument
+        """
+        tf_imgs = []
+        tf_masks = []
+        # Data Augmentation
+        for i in range(augmentation_ratio): 
+            # Select the policy 
+            if policy == 'retina':
+                aug_policy = RetinaPolicy(crop_dims=[self.crop_size, self.crop_size], brightness=[0.9, 1.1])
+       
+            # Apply the transformation
+            tf_image, tf_mask = aug_policy(image, mask) 
+              
+            # Further process the images and masks
+            tf_image = self.preprocess(tf_image, self.scale)
+            tf_mask = self.mask_img2class_mask(tf_mask, self.scale)
+            tf_image = torch.from_numpy(tf_image).type(torch.FloatTensor)
+            tf_mask = torch.from_numpy(tf_mask).type(torch.FloatTensor)
+            tf_imgs.append(tf_image)
+            tf_masks.append(tf_mask)
+
+        i, j, h, w = transforms.RandomCrop.get_params(image, [self.crop_size, self.crop_size])
+        image = transforms.functional.crop(image, i, j, h, w)
+        mask = transforms.functional.crop(mask, i, j, h, w)
+        image = self.preprocess(image, self.scale)
+        mask = self.mask_img2class_mask(mask, self.scale)
+        image = torch.from_numpy(image).type(torch.FloatTensor)
+        mask = torch.from_numpy(mask).type(torch.FloatTensor)
+
+        tf_imgs.insert(0, image)
+        tf_masks.insert(0, mask)
+
+        return (tf_imgs, tf_masks)
+
+    def __getitem__(self, i) -> Dict[List[torch.FloatTensor], List[torch.FloatTensor]]:
+        r"""
+        Returns two tensors: an image, of shape 1HW, and the corresponding mask, of shape CHW. 
+        """
+        idx = self.ids[i]
+        # mask_file = glob(self.masks_dir + idx.replace('training', 'manual1') + '.*')
+        # img_file = glob(self.imgs_dir + idx + '.*')
+        mask_file = glob(f"{self.masks_dir}{idx}.*")
+        img_file = glob(self.imgs_dir + idx + '.*')
+
+        # print(img_file, mask_file)
+
+        assert len(mask_file) == 1, \
+            f'Either no mask or multiple masks found for the ID {idx}: {mask_file}'
+        assert len(img_file) == 1, \
+            f'Either no image or multiple images found for the ID {idx}: {img_file}'
+        
+        mask = Image.open(mask_file[0])
+        image = Image.open(img_file[0])
+
+        # Here we apply any changes to the image that we want for our specfici prediction task
+        maskArray = numpy.array(mask).astype('uint8')
+        imageArray = numpy.array(image).astype('uint8')
+
+        # ## Get endpoints of skeleton
+        # endPoints = utils.utils.skelEndpoints(maskArray)
+
+        # ## change a channel to show the start and end of centreline 
+        # imageArray[:, :, -1] = endPoints.astype(numpy.uint8)*255
+
+        crudeMask = utils.utils.crudeMaskGenerator(maskArray)
+        imageArray[:, :, -1] = crudeMask.astype(numpy.uint8)
+
+        # print(imageArray.max(), imageArray.min())
+
+        ## Reconvert to PIL image object 
+        image = Image.fromarray(imageArray.astype(numpy.uint8))
+
+        assert image.size == mask.size, \
+            f'Image and mask {idx} should be the same size, but are {image.size} and {mask.size}'        
+
+        images, masks = self.augment(image, mask, policy = self.policy, augmentation_ratio = self.augmentation_ratio)
+        return {
+            'image': images,
+            'mask': masks
+        }
diff --git a/utils/utils.py b/utils/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ffa0d3d6ad5da9e0510db99d8103cb9d49caea5c
--- /dev/null
+++ b/utils/utils.py
@@ -0,0 +1,110 @@
+import os
+import time
+import random
+import numpy
+import cv2
+import torch
+from sklearn.metrics import accuracy_score, f1_score, jaccard_score, precision_score, recall_score, roc_auc_score #recall = sensitivity, precision = PPV
+import skimage
+
+
+""" Seeding the randomness. """
+def seeding(seed):
+    random.seed(seed)
+    os.environ["PYTHONHASHSEED"] = str(seed)
+    numpy.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.backends.cudnn.deterministic = True
+
+""" Create a directory. """
+def create_dir(path):
+    if not os.path.exists(path):
+        os.makedirs(path)
+    else:
+        pass
+
+""" Calculate the time taken """
+def epoch_time(start_time, end_time):
+    elapsed_time = end_time - start_time
+    elapsed_mins = int(elapsed_time / 60)
+    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))
+    return elapsed_mins, elapsed_secs
+
+
+"""METRICS"""
+def metricsCalculator(y_true, y_pred):
+    score_jaccard = jaccard_score(y_true, y_pred, pos_label=255)
+    score_f1 = f1_score(y_true, y_pred, pos_label=255)
+    score_recall = recall_score(y_true, y_pred, pos_label=255)
+    score_precision = precision_score(y_true, y_pred, pos_label=255)
+    score_acc = accuracy_score(y_true, y_pred)
+    score_auc = roc_auc_score(y_true, y_pred)
+
+    scores = [score_jaccard, score_f1, score_recall, score_precision, score_acc, score_auc]
+
+    print(f"\t=>Jaccard: {score_jaccard:1.4f} - F1: {score_f1:1.4f} - Recall: {score_recall:1.4f} - Precision: {score_precision:1.4f} - Acc: {score_acc:1.4f} - AUC: {score_auc:1.4f}\n")
+
+
+    return scores
+
+def skelEndpoints(maskArray):
+    skel = skimage.morphology.skeletonize(maskArray.astype('bool'))
+    skel = numpy.uint8(skel>0)
+
+    # Apply the convolution.
+    kernel = numpy.uint8([[1,  1, 1],
+    [1, 10, 1],
+    [1,  1, 1]])
+    src_depth = -1
+    filtered = cv2.filter2D(skel,src_depth,kernel)
+
+    # Look through to find the value of 11.
+    # This returns a mask of the endpoints, but if you
+    # just want the coordinates, you could simply
+    # return numpy.where(filtered==11)
+    out = numpy.zeros_like(skel)
+    out[numpy.where(filtered==11)] = 1
+    # endCoords = numpy.where(filtered==11)
+    # endCoords = list(zip(*endCoords))
+    # startPoint = endCoords[0]
+    # endPoint = endCoords[1]
+
+    # print(f"Skel starts at {startPoint} and finishes at {endPoint}")
+
+    # print(sum(out))
+
+    out = out.astype('uint8')*255
+
+    return out
+
+
+def crudeMaskGenerator(maskArray):
+    skel = skimage.morphology.skeletonize(maskArray.astype('bool'))
+    skel = numpy.uint8(skel>0)
+    radius = 15
+
+
+    crudeMask = numpy.zeros_like(skel)
+    skelPoints = numpy.argwhere(skel>0)
+
+    # Create a circular mask to dilate the skel
+    y, x = numpy.ogrid[-radius:radius+1,
+                   -radius:radius+1]
+
+    circleMask = x**2 + y**2 <= radius**2
+
+    for i, point in enumerate(skelPoints[:-1]):
+        yPos = point[0]
+        xPos = point[1]
+
+        if (yPos < skel.shape[0]-radius and xPos < skel.shape[1]-radius):
+            if (yPos > radius and xPos > radius):
+
+                crudeMask[int(yPos-radius):int(yPos+radius+1),
+                        int(xPos-radius):int(xPos+radius+1)] += circleMask
+
+
+    crudeMask = crudeMask>0
+
+    return crudeMask.astype('uint8')*255