From the model card
Fields below are copied from the tags and counters on the HuggingFace repository laion/CLIP-ViT-B-32-laion2B-s34B-b79K 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)
- laion
- Pipeline tag
- zero-shot-image-classification
- Library
- OpenCLIP
- Framework tags
- PyTorch
- Weight formats
- safetensors
- License tag
mit— read the license file in the repo before relying on it- Papers cited
- arXiv:1910.04867
- Downloads (HF counter at last fetch)
- 3,317,085
- Likes (HF counter at last fetch)
- 142
- Model card
- https://huggingface.co/laion/CLIP-ViT-B-32-laion2B-s34B-b79K
Use cases
- Open-source zero-shot image classification
- Image-text retrieval in semantic search systems
- Feature backbone for multimodal downstream fine-tuning
- Comparing LAION vs OpenAI CLIP training data effects
Pros
- Fully open training data (LAION-2B) enables reproducibility research
- MIT licensed
- Interchangeable with OpenAI CLIP ViT-B/32 for most applications
- Part of OpenCLIP suite with many architecture variants
Cons
- ViT-B/32 resolution is low — ViT-L/14@336 provides significantly better features
- LAION-2B contains noisy web-crawled data affecting alignment quality
- Underperforms OpenAI's ViT-L/14 on fine-grained classification tasks
- No built-in safety filters on the training data
Tags
open_clippytorchsafetensorsclipzero-shot-image-classificationarxiv:1910.04867license:mitregion:us