Update README.md

README.md

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-license: cc-by-nc-4.0
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+---
+license: cc-by-nc-4.0
+---
+# PathGen-CLIP
+
+This is the official PathGen-CLIP trained based on  **PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration**
+
+## Usage of Trained PathGen-CLIP series model
+
+The trained PathGen-CLIP can be downloaded via this [**PathGen-CLIP**](https://pub-7a38cc906afa44a4a01533c288d0b1af.r2.dev/pathgenclip.pt) and the PathGen-CLIP-L via this  [**PathGen-CLIP-L**](https://huggingface.co/jamessyx/PathGen-CLIP-L) (We also transform PathGen-CLIP-L to HF version [**PathGenCLIP-vit-large-patch14-hf**](https://huggingface.co/jamessyx/pathgenclip-vit-large-patch14-hf)  to facilitate the integration into LLM).
+
+```
+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') // PathGen-CLIP
+# model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-16', pretrained='path/pathgen-clip-l.pt') // PathGen-CLIP-L
+model.eval()  # model in train mode by default, impacts some models with BatchNorm or stochastic depth active
+tokenizer = open_clip.get_tokenizer('ViT-B-16')
+
+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)
+```
+
+