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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**](https://arxiv.org/abs/2407.00203)
## 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)
```
## **Citation**
```
@article{sun2024pathgen,
title={Pathgen-1.6 m: 1.6 million pathology image-text pairs generation through multi-agent collaboration},
author={Sun, Yuxuan and Zhang, Yunlong and Si, Yixuan and Zhu, Chenglu and Shui, Zhongyi and Zhang, Kai and Li, Jingxiong and Lyu, Xingheng and Lin, Tao and Yang, Lin},
journal={arXiv preprint arXiv:2407.00203},
year={2024}
}
```
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