Zero-Shot Image Classification
Transformers
Safetensors
English
clip
vision-language
compositional-reasoning
contrastive-learning
text-encoder
sugarcrepe
whatsup
crepe
valse
Instructions to use Mayfull/READ-CLIP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mayfull/READ-CLIP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Mayfull/READ-CLIP") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("Mayfull/READ-CLIP") model = AutoModelForZeroShotImageClassification.from_pretrained("Mayfull/READ-CLIP", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update model card: searchable tags + NeurIPS citation
Browse files
README.md
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- vision-language
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---
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# READ-CLIP (ViT-B/32)
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## Citation
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```bibtex
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@inproceedings{
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- vision-language
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- text-encoder
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- sugarcrepe
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---
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# READ-CLIP (ViT-B/32)
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## Citation
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```bibtex
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@inproceedings{kwon2026enhancing,
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title={Enhancing Compositional Reasoning in {CLIP} via Reconstruction and Alignment of Text Descriptions},
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author={Jihoon Kwon and Kyle Min and Jy-yong Sohn},
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booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
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year={2026},
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url={https://openreview.net/forum?id=6uKIm4bfEe}
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}
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```
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