SPEC-CLIP-ViT-B-32

SPEC-CLIP-ViT-B-32

Model Sources

Code | Paper | arXiv

Model Usage

• download checkpoint

huggingface-cli download wjpoom/SPEC-CLIP-ViT-B-32 --local-dir checkpoints/SPEC-CLIP-ViT-B-32

• load model

# pip install open_clip_torch
import torch
from PIL import Image
import open_clip

model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-32', pretrained='checkpoints/SPEC-CLIP-ViT-B-32', load_weights_only=False)
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("docs/CLIP.png")).unsqueeze(0)
text = tokenizer(["a diagram", "a dog", "a cat"])

with torch.no_grad(), torch.autocast("cuda"):
    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)  # prints: [[1., 0., 0.]]

Contact

Feel free to contact us if you have any questions or suggestions

• Email (Wujian Peng): [email protected]

Citation

@inproceedings{peng2024synthesize,
  title={Synthesize diagnose and optimize: Towards fine-grained vision-language understanding},
  author={Peng, Wujian and Xie, Sicheng and You, Zuyao and Lan, Shiyi and Wu, Zuxuan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={13279--13288},
  year={2024}
}