From the model card
Fields below are copied from the tags and counters on the HuggingFace repository google/siglip-base-patch16-224 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)
- Pipeline tag
- zero-shot-image-classification
- Library
- Transformers
- Framework tags
- PyTorch
- Weight formats
- safetensors
- License tag
apache-2.0— read the license file in the repo before relying on it- Papers cited
- arXiv:2303.15343, arXiv:2209.06794
- Downloads (HF counter at last fetch)
- 1,438,127
- Likes (HF counter at last fetch)
- 89
- Model card
- https://huggingface.co/google/siglip-base-patch16-224
Use cases
- Vision encoder backbone for lightweight VLMs
- Zero-shot image classification with text labels
- Image-text retrieval in small-scale deployments
- Ablation baseline comparing SigLIP vs CLIP loss formulations
Pros
- Outperforms CLIP ViT-B/16 on zero-shot classification benchmarks
- Apache-2.0 licensed
- Well-maintained by Google with consistent API in Transformers
- Base scale enables fast inference on modest hardware
Cons
- 224px resolution limits fine-grained visual detail capture
- Base scale lags SigLIP SO/400M on demanding visual tasks
- Less widely adopted than CLIP — fewer community fine-tunes available
- Sigmoid loss training dynamics differ from CLIP — less documented for fine-tuning
Tags
transformerspytorchsafetensorssiglipzero-shot-image-classificationvisionarxiv:2303.15343arxiv:2209.06794license:apache-2.0endpoints_compatibleregion:us