Update Readme.md

README.md

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-# Building Footprint Segmentation
-A U-Net model for segmenting buildings from satellite imagery
-
-A binary segmentation mask (of the same height and width with the input image) should be created The segmentation mask should have a value of 1 at pixels where there is a building and 0 at other pixels.
-
-The figure below showcases the input and output image expected. In the mask pixels that correspond to pixels in the input image are white and background is black.
-
-![Expected input and output](https://raw.githubusercontent.com/gunaykrgl/buildingSegmentation/main/Notebook/task_definition.png)
-
-The training and Machine-learning related code is found at [the Notebook](https://github.com/gunaykrgl/buildingSegmentation/blob/main/Notebook/Building_Segmentation.ipynb).
-
-# Data
-The data used in this project is sourced from [Road and Building Detection Datasets](https://www.cs.toronto.edu/~vmnih/data/) with the following citation:
-
-```
-@phdthesis{MnihThesis,
-    author = {Volodymyr Mnih},
-    title = {Machine Learning for Aerial Image Labeling},
-    school = {University of Toronto},
-    year = {2013}
-}
-
-```
-
-For the ease of use, relevant parts of this dataset was sourced from [kaggle.com](https://www.kaggle.com/datasets/balraj98/massachusetts-buildings-dataset)
-
-# Remote Running
-You can try the application without installation by navigating to the following link:
-[Building Footprint Segmentation](https://huggingface.co/spaces/gunayk3/building_footprint_segmentation)
-
-## Screenshots
-<img src="https://raw.githubusercontent.com/gunaykrgl/buildingSegmentation/main/screenshots/scr1.png" width="600">
-<img src="https://raw.githubusercontent.com/gunaykrgl/buildingSegmentation/main/screenshots/scr2.png" width="600">
-<img src="https://raw.githubusercontent.com/gunaykrgl/buildingSegmentation/main/screenshots/scr3.png" width="600">
-
-# Local Installation
-```
-gh repo clone gunaykrgl/buildingSegmentation
-cd buildingSegmentation
-pip install -r requirements.txt
-```
-
-## Running Locally
-After local installation, the application can be run by the following command:
-```
-gradio run demo.py
-```
-
-After this command is run, there will be a localhost url be generated at the end of the outputs. Later, the application can be used by navigating to the url in a web browser.
+---
+title: Building Footprint Segmentation
+emoji: 🏢
+colorFrom: indigo
+colorTo: red
+sdk: gradio
+sdk_version: 4.24.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference