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--- |
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license: apache-2.0 |
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model-index: |
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- name: SPEC-CLIP-ViT-B-32 |
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results: |
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- task: |
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type: image-text-matching |
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dataset: |
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name: SPEC |
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type: compositional-reasoning |
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metrics: |
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- name: Absolute Size I2T |
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type: Image to Text Matching |
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value: 68.9 |
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- name: Absolute Size T2I |
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type: Image to Text Matching |
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value: 60.7 |
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- name: Relative Size I2T |
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type: Image to Text Matching |
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value: 40.3 |
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- name: Relative Size T2I |
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type: Image to Text Matching |
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value: 44.1 |
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- name: Absolute Position I2T |
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type: Image to Text Matching |
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value: 30.6 |
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- name: Absolute Position T2I |
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type: Image to Text Matching |
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value: 34.2 |
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- name: Relative Position I2T |
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type: Image to Text Matching |
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value: 46.6 |
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- name: Relative Position T2I |
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type: Image to Text Matching |
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value: 46.9 |
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- name: Existence I2T |
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type: Image to Text Matching |
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value: 83.4 |
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- name: Existence T2I |
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type: Image to Text Matching |
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value: 53.1 |
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- name: Count I2T |
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type: Image to Text Matching |
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value: 55.6 |
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- name: Count T2I |
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type: Image to Text Matching |
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value: 57.8 |
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source: |
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name: SPEC paper |
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url: https://arxiv.org/pdf/2312.00081 |
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--- |
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# SPEC-CLIP-ViT-B-32 |
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### Model Sources |
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[**Code**](https://github.com/wjpoom/SPEC) | [**Paper**](https://huggingface.co/papers/2312.00081) | [**arXiv**](https://arxiv.org/abs/2312.00081) |
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### Model Usage |
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* download checkpoint |
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```shell |
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huggingface-cli download wjpoom/SPEC-CLIP-ViT-B-32 --local-dir checkpoints/SPEC-CLIP-ViT-B-32 |
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``` |
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* load model |
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```python |
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# pip install open_clip_torch |
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import torch |
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from PIL import Image |
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import open_clip |
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model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-32', pretrained='checkpoints/SPEC-CLIP-ViT-B-32', load_weights_only=False) |
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model.eval() # model in train mode by default, impacts some models with BatchNorm or stochastic depth active |
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tokenizer = open_clip.get_tokenizer('ViT-B-32') |
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image = preprocess(Image.open("docs/CLIP.png")).unsqueeze(0) |
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text = tokenizer(["a diagram", "a dog", "a cat"]) |
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with torch.no_grad(), torch.autocast("cuda"): |
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image_features = model.encode_image(image) |
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text_features = model.encode_text(text) |
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image_features /= image_features.norm(dim=-1, keepdim=True) |
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text_features /= text_features.norm(dim=-1, keepdim=True) |
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text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1) |
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print("Label probs:", text_probs) # prints: [[1., 0., 0.]] |
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``` |
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## Contact |
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Feel free to contact us if you have any questions or suggestions |
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- Email (Wujian Peng): wjpeng24@m.fudan.edu.cn |
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## Citation |
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``` bibtex |
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@inproceedings{peng2024synthesize, |
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title={Synthesize diagnose and optimize: Towards fine-grained vision-language understanding}, |
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author={Peng, Wujian and Xie, Sicheng and You, Zuyao and Lan, Shiyi and Wu, Zuxuan}, |
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booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, |
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pages={13279--13288}, |
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year={2024} |
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} |
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``` |
