Create README.md

README.md

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+---
+tags:
+- image-classification
+- timm
+- owkin
+- biology
+- cancer
+- colon
+library_name: timm
+datasets:
+- 1aurent/LC25000
+metrics:
+- accuracy
+pipeline_tag: image-classification
+widget:
+  - src: >-
+      https://datasets-server.huggingface.co/cached-assets/1aurent/LC25000/--/56a7c495692c27afd294a88b7aadaa7b79d8e270/--/default/train/24999/image/image.jpg
+    example_title: benign
+  - src: >-
+      https://datasets-server.huggingface.co/cached-assets/1aurent/LC25000/--/56a7c495692c27afd294a88b7aadaa7b79d8e270/--/default/train/17501/image/image.jpg
+    example_title: adenocarcinomas
+---
+
+# Model card for vit_base_patch16_224.owkin_pancancer_ft_lc25000_colon
+
+A Vision Transformer (ViT) image classification model. \
+Trained by Owkin on 40M pan-cancer histology tiles from TCGA. \
+Fine-tuned on LC25000's colon subset.
+
+## Model Details
+
+- **Model Type:** Image classification / feature backbone
+- **Model Stats:**
+  - Params (M): 85.8
+  - Image size: 224 x 224 x 3
+- **Papers:**
+  - Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling: https://www.medrxiv.org/content/10.1101/2023.07.21.23292757v2
+- **Pretrain Dataset:** TGCA: https://portal.gdc.cancer.gov/
+- **Dataset:** LC25000: https://huggingface.co/datasets/1aurent/LC25000
+- **Original:** https://github.com/owkin/HistoSSLscaling/
+- **License:** https://github.com/owkin/HistoSSLscaling/blob/main/LICENSE.txt
+
+## Model Usage
+
+### Image Classification
+```python
+from urllib.request import urlopen
+from PIL import Image
+import timm
+
+# get example histology image
+img = Image.open(
+  urlopen(
+    "https://datasets-server.huggingface.co/cached-assets/1aurent/LC25000/--/56a7c495692c27afd294a88b7aadaa7b79d8e270/--/default/train/24999/image/image.jpg"
+  )
+)
+
+# load model from the hub
+model = timm.create_model(
+  model_name="hf-hub:1aurent/vit_base_patch16_224.owkin_pancancer_ft_lc25000_colon",
+  pretrained=True,
+).eval()
+
+# get model specific transforms (normalization, resize)
+data_config = timm.data.resolve_model_data_config(model)
+transforms = timm.data.create_transform(**data_config, is_training=False)
+
+output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1
+```
+
+### Image Embeddings
+```python
+from urllib.request import urlopen
+from PIL import Image
+import timm
+
+# get example histology image
+img = Image.open(
+  urlopen(
+    "https://datasets-server.huggingface.co/cached-assets/1aurent/LC25000/--/56a7c495692c27afd294a88b7aadaa7b79d8e270/--/default/train/24999/image/image.jpg"
+  )
+)
+
+# load model from the hub
+model = timm.create_model(
+  model_name="hf-hub:1aurent/vit_base_patch16_224.owkin_pancancer_ft_lc25000_colon",
+  pretrained=True,
+  num_classes=0,
+).eval()
+
+# get model specific transforms (normalization, resize)
+data_config = timm.data.resolve_model_data_config(model)
+transforms = timm.data.create_transform(**data_config, is_training=False)
+
+output = model(transforms(img).unsqueeze(0))  # output is (batch_size, num_features) shaped tensor
+```
+
+## Citation
+```bibtex
+@article {Filiot2023.07.21.23292757,
+  author = {Alexandre Filiot and Ridouane Ghermi and Antoine Olivier and Paul Jacob and Lucas Fidon and Alice Mac Kain and Charlie Saillard and Jean-Baptiste Schiratti},
+  title = {Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling},
+  elocation-id = {2023.07.21.23292757},
+  year = {2023},
+  doi = {10.1101/2023.07.21.23292757},
+  publisher = {Cold Spring Harbor Laboratory Press},
+  URL = {https://www.medrxiv.org/content/early/2023/09/14/2023.07.21.23292757},
+  eprint = {https://www.medrxiv.org/content/early/2023/09/14/2023.07.21.23292757.full.pdf},
+  journal = {medRxiv}
+}
+```