From the model card
Fields below are copied from the tags and counters on the HuggingFace repository Salesforce/blip-image-captioning-base 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)
- Salesforce
- Pipeline tag
- image-to-text
- Library
- Transformers
- Framework tags
- PyTorch, TensorFlow
- License tag
bsd-3-clause— read the license file in the repo before relying on it- Papers cited
- arXiv:2201.12086
- Downloads (HF counter at last fetch)
- 1,823,300
- Likes (HF counter at last fetch)
- 886
- Model card
- https://huggingface.co/Salesforce/blip-image-captioning-base
Use cases
- Automated image alt-text generation
- Image captioning for dataset annotation workflows
- Visual content description in accessibility tools
- Feature comparison against BLIP-2 and newer captioning models
Pros
- Simple to use via HuggingFace pipeline API
- BSD-3 licensed — permissive for commercial use
- Bootstrapped training on filtered noisy data improves caption quality
- Strong baseline for image captioning research
Cons
- BLIP-2 and InstructBLIP substantially outperform it on detailed captioning
- Base variant lags BLIP-large on standard caption benchmarks
- Requires ~1.5GB memory — not edge-friendly
- Captions can be generic without visual detail for complex images
Tags
transformerspytorchtfblipimage-text-to-textimage-captioningimage-to-textarxiv:2201.12086license:bsd-3-clauseendpoints_compatibleregion:us