Update README.md

README.md

@@ -1,3 +1,50 @@
----
-license: cc-by-2.0
----
+---
+license: cc-by-2.0
+---
+## Usage of PathGen-CLIP
+
+```
+pip install open_clip_torch
+```
+
+```python
+import torch
+from PIL import Image
+import open_clip
+
+model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-16', pretrained='path/pathgen-clip.pt')
+model.eval()  # model in train mode by default, impacts some models with BatchNorm or stochastic depth active
+tokenizer = open_clip.get_tokenizer('ViT-B-32')
+
+image = preprocess(Image.open("example.png")).unsqueeze(0)
+text = tokenizer(["An H&E image of tumor patch", "An H&E image of normal patch"])
+
+with torch.no_grad(), torch.cuda.amp.autocast():
+    image_features = model.encode_image(image)
+    text_features = model.encode_text(text)
+    image_features /= image_features.norm(dim=-1, keepdim=True)
+    text_features /= text_features.norm(dim=-1, keepdim=True)
+
+    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
+
+print("Label probs:", text_probs)
+```
+
+
+
+
+
+## Cite
+
+```
+@misc{sun2024pathgen16m16millionpathology,
+      title={PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration}, 
+      author={Yuxuan Sun and Yunlong Zhang and Yixuan Si and Chenglu Zhu and Zhongyi Shui and Kai Zhang and Jingxiong Li and Xingheng Lyu and Tao Lin and Lin Yang},
+      year={2024},
+      eprint={2407.00203},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2407.00203}, 
+}
+```
+