From the model card
Fields below are copied from the tags and counters on the HuggingFace repository openai/clip-vit-large-patch14 at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.
- Publisher (HF namespace)
- openai
- Pipeline tag
- zero-shot-image-classification
- Library
- Transformers
- Framework tags
- PyTorch, TensorFlow, JAX
- Weight formats
- safetensors
- Papers cited
- arXiv:2103.00020, arXiv:1908.04913
- Downloads (HF counter at last fetch)
- 6,723,984
- Likes (HF counter at last fetch)
- 2,073
- Model card
- https://huggingface.co/openai/clip-vit-large-patch14
Use cases
- Zero-shot image classification without task-specific training data
- Image-text retrieval in multimodal search systems
- Visual similarity search using image embeddings
- Content moderation prototyping based on natural language descriptions
- Feature extraction backbone for downstream vision-language fine-tuning
Pros
- Zero-shot classification eliminates need for labeled image training data
- Flexible natural language label specification — categories can be arbitrary text
- ViT-L/14 outperforms smaller CLIP variants on standard classification benchmarks
- Broad framework support (PyTorch, TF, JAX, safetensors)
Cons
- No explicit commercial license specified — requires review before production use
- Results are highly sensitive to prompt phrasing; prompt engineering required
- Outperformed by fine-tuned classifiers on narrow domain-specific tasks
- ViT-L/14 scale requires GPU for practical throughput
- Struggles with fine-grained visual distinctions between similar subcategories
Tags
transformerspytorchtfjaxsafetensorsclipzero-shot-image-classificationvisionarxiv:2103.00020arxiv:1908.04913endpoints_compatibleregion:us