From the model card
Fields below are copied from the tags and counters on the HuggingFace repository openai/clip-vit-base-patch32 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
- Papers cited
- arXiv:2103.00020, arXiv:1908.04913
- Downloads (HF counter at last fetch)
- 19,936,700
- Likes (HF counter at last fetch)
- 1,137
- Model card
- https://huggingface.co/openai/clip-vit-base-patch32
Use cases
- Zero-shot image classification prototyping without labeled training data
- Image-to-text retrieval in research and experimental pipelines
- Content tagging using arbitrary natural language categories
- Lightweight image embedding extraction for visual similarity search
- Rapid iteration on visual classification tasks before committing to fine-tuning
Pros
- Faster inference than the larger ViT-L/14 CLIP variant
- Zero-shot setup avoids collecting and labeling training images
- Natural-language category specification supports flexible, updatable classification
- Broad framework support (PyTorch, TF, JAX)
Cons
- Lower classification accuracy than ViT-L/14 CLIP on most benchmarks
- Results sensitive to prompt phrasing variations requiring experimentation
- Substantially outperformed by fine-tuned classifiers on domain-specific tasks
- No commercial license specified — review terms before production use
- Requires GPU for real-time throughput at production scale
Tags
transformerspytorchtfjaxclipzero-shot-image-classificationvisionarxiv:2103.00020arxiv:1908.04913endpoints_compatibleregion:us