Zero-Shot Image Classification
OpenCLIP
clip
vision-language-model
image-text-retrieval
research
long-tail
datacomp
Instructions to use MingliangLiang3/DynamiCS-ViT-B-16-DataComp-DFN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- OpenCLIP
How to use MingliangLiang3/DynamiCS-ViT-B-16-DataComp-DFN with OpenCLIP:
import open_clip model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:MingliangLiang3/DynamiCS-ViT-B-16-DataComp-DFN') tokenizer = open_clip.get_tokenizer('hf-hub:MingliangLiang3/DynamiCS-ViT-B-16-DataComp-DFN') - Notebooks
- Google Colab
- Kaggle
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## Citation
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## Citation
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```bibtex
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@article{liang2026dynamics,
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title={Dynamic Cluster Data Sampling for Efficient and Long-Tail-Aware Vision-Language Pre-training},
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author={Mingliang Liang and Zhuoran Liu and Arjen P. de Vries and Martha Larson},
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journal={arXiv preprint arXiv:2604.27932},
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year={2026}
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}
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```
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