Create README.md

README.md

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+---
+license: apache-2.0
+tags:
+- medical
+---
+
+<!-- markdownlint-disable first-line-h1 -->
+<!-- markdownlint-disable html -->
+
+<div align="center">
+<h1>
+  EndoViT
+</h1>
+</div>
+
+<p align="center">
+<a href="https://link.springer.com/article/10.1007/s11548-024-03091-5" target="_blank">Paper</a>    <a href="https://github.com/DominikBatic/EndoViT" target="_blank">Github</a></a>
+</p>
+
+<div align="center">
+</div>
+
+
+##Get Started
+
+This section provides a quick start example for using the EndoViT model.
+
+Installation:
+
+```python
+pip install torch==2.0.1 timm==0.9.16 huggingface-hub==0.22.2
+```
+
+Extracting features from a list of images. (Can also be a good starting point for using EndoViT as backbone)
+
+```python
+import torch
+import torchvision.transforms as T
+from PIL import Image
+from pathlib import Path
+from timm.models.vision_transformer import VisionTransformer
+from functools import partial
+from torch import nn
+from huggingface_hub import snapshot_download
+
+
+def process_single_image(image_path, input_size=224, dataset_mean=[0.3464, 0.2280, 0.2228], dataset_std=[0.2520, 0.2128, 0.2093]):
+    # Define the transformations
+    transform = T.Compose([
+        T.Resize((input_size, input_size)),
+        T.ToTensor(),
+        T.Normalize(mean=dataset_mean, std=dataset_std)
+    ])
+
+    # Open the image
+    image = Image.open(image_path).convert('RGB')
+
+    # Apply the transformations
+    processed_image = transform(image)
+
+    return processed_image
+def load_model_from_huggingface(repo_id, model_filename):
+    # Download model files
+    model_path = snapshot_download(repo_id=repo_id, revision="main")
+    model_weights_path = Path(model_path) / model_filename
+
+    # Load model weights
+    model_weights = torch.load(model_weights_path)['model']
+
+    # Define the model (ensure this matches your model's architecture)
+    model = VisionTransformer(patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6)).eval()
+
+    # Load the weights into the model
+    loading = model.load_state_dict(model_weights, strict=False)
+
+    return model, loading
+
+
+image_paths = sorted(Path('demo_images').glob('*.png')) # TODO replace with image pass
+images = torch.stack([process_single_image(image_path) for image_path in image_paths])
+
+device = "cuda"
+dtype = torch.float16
+model, loading_info = load_model_from_huggingface("egeozsoy/EndoViT", "endovit.pth")
+model = model.to(device, dtype)
+print(loading_info)
+output = model.forward_features(images.to(device, dtype))
+print(output.shape)
+```
+
+
+## ✏️ Citation
+
+```
+@article{batic2024endovit,
+  title={EndoViT: pretraining vision transformers on a large collection of endoscopic images},
+  author={Bati{\'c}, Dominik and Holm, Felix and {\"O}zsoy, Ege and Czempiel, Tobias and Navab, Nassir},
+  journal={International Journal of Computer Assisted Radiology and Surgery},
+  pages={1--7},
+  year={2024},
+  publisher={Springer}
+}
+```