Update README.md

README.md

@@ -1,47 +1,54 @@
-## How to use UniCAS to extract features.
-
-The code below can be used to run inference; `UniCAS` expects images of size 224x224 that were extracted at 20× magnification.
-
-```python
-import functools
-import timm
-import torch
-from torchvision import transforms
-
-params = {
-    'patch_size': 16, 
-    'embed_dim': 1024, 
-    'depth': 24, 
-    'num_heads': 16, 
-    'init_values': 1e-05, 
-    'mlp_ratio': 2.671875 * 2, 
-    'mlp_layer': functools.partial(
-        timm.layers.mlp.GluMlp, gate_last=False
-    ), 
-    'act_layer': torch.nn.modules.activation.SiLU, 
-    'no_embed_class': False, 
-    'img_size': 224, 
-    'num_classes': 0, 
-    'in_chans': 3
-}
-
-model = timm.models.VisionTransformer(**params)
-print(model.load_state_dict(torch.load("UniCAS.pth"), strict=False))
-model = model.eval().to("cuda")
-
-
-transform = transforms.Compose([
-    transforms.ToTensor(),
-    transforms.Normalize(
-        mean=(0.485, 0.456, 0.406),  
-       std=(0.229, 0.224, 0.225),
-    ),
-])
-
-input = torch.rand(3, 224, 224)
-input = transforms.ToPILImage()(input)
-input = transform(input).unsqueeze(0)
-with torch.no_grad():
-    features = model(input.to("cuda"))
-print(features.shape)  # torch.Size([1, 1024])
+---
+tags:
+- cytology foundation model
+- vision transformer
+- cervical screening
+license: apache-2.0
+---
+## How to use UniCAS to extract features.
+
+The code below can be used to run inference; `UniCAS` expects images of size 224x224 that were extracted at 20× magnification.
+
+```python
+import functools
+import timm
+import torch
+from torchvision import transforms
+
+params = {
+    'patch_size': 16, 
+    'embed_dim': 1024, 
+    'depth': 24, 
+    'num_heads': 16, 
+    'init_values': 1e-05, 
+    'mlp_ratio': 2.671875 * 2, 
+    'mlp_layer': functools.partial(
+        timm.layers.mlp.GluMlp, gate_last=False
+    ), 
+    'act_layer': torch.nn.modules.activation.SiLU, 
+    'no_embed_class': False, 
+    'img_size': 224, 
+    'num_classes': 0, 
+    'in_chans': 3
+}
+
+model = timm.models.VisionTransformer(**params)
+print(model.load_state_dict(torch.load("UniCAS.pth"), strict=False))
+model = model.eval().to("cuda")
+
+
+transform = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Normalize(
+        mean=(0.485, 0.456, 0.406),  
+       std=(0.229, 0.224, 0.225),
+    ),
+])
+
+input = torch.rand(3, 224, 224)
+input = transforms.ToPILImage()(input)
+input = transform(input).unsqueeze(0)
+with torch.no_grad():
+    features = model(input.to("cuda"))
+print(features.shape)  # torch.Size([1, 1024])
 ```