5 models · ranked by HuggingFace downloads
by openai
OpenAI's CLIP model using a ViT-B/32 image encoder, the smaller of the two widely deployed CLIP variants. Trained contrastively on 400 million image-text pairs, it aligns image and text representations in a shared embedding space for zero-shot classification and retrieval. The B/32 variant sacrifices accuracy versus ViT-L/14 for faster inference.
by openai
OpenAI's CLIP model using a ViT-L/14 image encoder, trained contrastively on 400 million image-text pairs from the internet. It aligns image and text in a shared embedding space, enabling zero-shot image classification by comparing image embeddings against text label embeddings. The ViT-L/14 variant offers higher accuracy than the smaller ViT-B/32 at greater compute cost.
by laion
OpenCLIP ViT-B/32 trained by LAION on 2 billion image-text pairs from the LAION-2B dataset. It provides open-source CLIP features comparable to OpenAI's original ViT-B/32 while being trained on a fully public dataset.
by google
SigLIP base/patch16 at 224px resolution is the lightweight tier of Google's sigmoid-loss vision-language pretraining model. It serves as a vision encoder for multimodal pipelines and as a standalone zero-shot classifier.
by google
SigLIP2-Base with NaFlex (Native Resolution Flexible) encoding, which processes images at their native resolution by dynamically adjusting patch sequences rather than resizing to a fixed size. This improves accuracy on images where spatial details matter. The base variant offers a smaller memory footprint than the 400M so400m variant.