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--- |
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license: apache-2.0 |
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tags: |
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- medical |
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- Image Feature Extraction |
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datasets: |
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- Geometryyy/Cholec80 |
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- minwoosun/CholecSeg8k |
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library_name: timm |
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--- |
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<!-- markdownlint-disable first-line-h1 --> |
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<!-- markdownlint-disable html --> |
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<div align="center"> |
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<h1> |
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EndoViT |
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</h1> |
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</div> |
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<p align="center"> |
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<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> |
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</p> |
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<div align="center"> |
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</div> |
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##Get Started |
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This section provides a quick start example for using the EndoViT model. |
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Installation: |
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```python |
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pip install torch==2.0.1 timm==0.9.16 huggingface-hub==0.22.2 |
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``` |
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Extracting features from a list of images. (Can also be a good starting point for using EndoViT as backbone) |
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```python |
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import torch |
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import torchvision.transforms as T |
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from PIL import Image |
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from pathlib import Path |
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from timm.models.vision_transformer import VisionTransformer |
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from functools import partial |
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from torch import nn |
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from huggingface_hub import snapshot_download |
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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]): |
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# Define the transformations |
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transform = T.Compose([ |
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T.Resize((input_size, input_size)), |
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T.ToTensor(), |
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T.Normalize(mean=dataset_mean, std=dataset_std) |
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]) |
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# Open the image |
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image = Image.open(image_path).convert('RGB') |
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# Apply the transformations |
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processed_image = transform(image) |
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return processed_image |
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def load_model_from_huggingface(repo_id, model_filename): |
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# Download model files |
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model_path = snapshot_download(repo_id=repo_id, revision="main") |
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model_weights_path = Path(model_path) / model_filename |
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# Load model weights |
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model_weights = torch.load(model_weights_path)['model'] |
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# Define the model (ensure this matches your model's architecture) |
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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() |
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# Load the weights into the model |
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loading = model.load_state_dict(model_weights, strict=False) |
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return model, loading |
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image_paths = sorted(Path('demo_images').glob('*.png')) # TODO replace with image path |
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images = torch.stack([process_single_image(image_path) for image_path in image_paths]) |
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device = "cuda" |
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dtype = torch.float16 |
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model, loading_info = load_model_from_huggingface("egeozsoy/EndoViT", "endovit.pth") |
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model = model.to(device, dtype) |
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print(loading_info) |
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output = model.forward_features(images.to(device, dtype)) |
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print(output.shape) |
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``` |
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## ✏️ Citation |
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``` |
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@article{batic2024endovit, |
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title={EndoViT: pretraining vision transformers on a large collection of endoscopic images}, |
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author={Bati{\'c}, Dominik and Holm, Felix and {\"O}zsoy, Ege and Czempiel, Tobias and Navab, Nassir}, |
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journal={International Journal of Computer Assisted Radiology and Surgery}, |
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pages={1--7}, |
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year={2024}, |
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publisher={Springer} |
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} |
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``` |
