Update README.md

README.md

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 ---
+tags:
+- image-classification
+- pytorch
+library_tag: pytorch
 license: bsd-3-clause
+datasets:
+- imagenet-1k
+pipeline_tag: feature-extraction
 ---
+
+# Model card for resnet50-truncated.tv_in1k
+
+A truncated ResNet-50 feature extraction model, as used in [CLAM](https://www.nature.com/articles/s41551-020-00682-w).
+
+This model features:
+ * ReLU activations
+ * single layer 7x7 convolution with pooling
+ * 1x1 convolution shortcut downsample
+
+Trained on ImageNet-1k, original torchvision model weight, truncated to exclude layer 4
+and the fully connected layer.
+
+This model card was adapted from https://huggingface.co/timm/resnet50.tv_in1k.
+
+## Model Details
+- **Model Type:** Feature backbone
+- **Model Stats:**
+  - Params (M): 8.5
+  - Image size: 224 x 224
+- **Papers:**
+  - Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385
+- **Original:** https://github.com/pytorch/vision
+
+## Model Creation
+
+```python
+import types
+import torch
+from torchvision.models import ResNet
+from torchvision.models import resnet50
+
+def _forward_impl(self, x: torch.Tensor) -> torch.Tensor:
+    x = self.conv1(x)
+    x = self.bn1(x)
+    x = self.relu(x)
+    x = self.maxpool(x)
+
+    x = self.layer1(x)
+    x = self.layer2(x)
+    x = self.layer3(x)
+
+    x = self.avgpool(x)
+    x = x.view(x.size(0), -1)
+
+    return x
+
+model = resnet50(weights=None)
+del model.layer4, model.fc
+
+model._forward_impl = types.MethodType(_forward_impl, model)
+
+state_dict = torch.hub.load_state_dict_from_url(
+    "https://download.pytorch.org/models/resnet50-19c8e357.pth"
+)
+# Remove truncated keys.
+state_dict = {k: v for k, v in state_dict.items() if not k.startswith("layer4.") and not k.startswith("fc.")}
+
+model.load_state_dict(state_dict, strict=True)
+model.eval()
+```
+
+## Model Usage
+
+### Image Embeddings
+```python
+from urllib.request import urlopen
+from PIL import Image
+import torch
+
+img = Image.open(urlopen(
+    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
+))
+
+# See above for how to load the model. Or load a TorchScript version of the model,
+# which can be loaded automatically with
+# model = torch.jit.load("torchscript_model.bin")
+model = model.eval()
+
+transform = transforms.Compose([
+    # Depending on the pipeline, this may be 256x256 or a different value.
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(
+        mean=(0.485, 0.456, 0.406),
+        std=(0.229, 0.224, 0.225)),
+])
+
+with torch.no_grad():
+    output = model(transform(img).unsqueeze(0))  # unsqueeze single image into batch of 1
+output.shape  # 1x1024
+```
+
+## Citation
+```bibtex
+@article{He2015,
+  author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
+  title = {Deep Residual Learning for Image Recognition},
+  journal = {arXiv preprint arXiv:1512.03385},
+  year = {2015}
+}
+```