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--- |
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license: cc-by-nc-4.0 |
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tags: |
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- vision |
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- metaclip |
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widget: |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png |
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candidate_labels: playing music, playing sports |
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example_title: Cat & Dog |
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--- |
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# MetaCLIP model, base-sized version, patch resolution 32 |
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MetaCLIP model applied to 2.5 billion data points of CommonCrawl (CC). It was introduced in the paper [Demystifying CLIP Data](https://arxiv.org/abs/2309.16671) by Xu et al. and first released in [this repository](https://github.com/facebookresearch/MetaCLIP). |
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Disclaimer: The team releasing MetaCLIP did not write a model card for this model so this model card has been written by the Hugging Face team. |
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## Model description |
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The [Demystifying CLIP Data](https://arxiv.org/abs/2309.16671) paper aims to reveal CLIP’s method around training data curation. OpenAI never open-sourced code regarding their data preparation pipeline. |
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/clip_overview.jpg" |
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alt="drawing" width="600"/> |
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<small> CLIP high-level overview. Taken from the <a href="https://arxiv.org/abs/2103.00020">CLIP paper</a>. </small> |
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## Intended uses & limitations |
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You can use the raw model for linking images with text in a shared embedding space. This enables things like zero-shot image classification, text-based image retrieval, image-based text retrieval, etc. |
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### How to use |
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We refer to the [docs](https://huggingface.co/docs/transformers/main/en/model_doc/clip#usage). Just replace the names of the models on the hub. |
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### BibTeX entry and citation info |
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```bibtex |
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@misc{xu2023demystifying, |
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title={Demystifying CLIP Data}, |
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author={Hu Xu and Saining Xie and Xiaoqing Ellen Tan and Po-Yao Huang and Russell Howes and Vasu Sharma and Shang-Wen Li and Gargi Ghosh and Luke Zettlemoyer and Christoph Feichtenhofer}, |
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year={2023}, |
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eprint={2309.16671}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
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