BLIP Image Captioning Large
Salesforce/blip-image-captioning-large
published Dec 2022 · updated Feb 2025
BLIP Image Captioning Large is an image-to-text model that generates descriptive captions for images using a Vision-Language Pre-training framework.
specs
| Task | Image-to-text (image captioning) |
| Architecture | Vision Transformer Large (ViT-L) backbone with multimodal encoder-decoder |
| Dataset | COCO (pretrained on 129M image-text pairs) |
| License | BSD-3-Clause |
about this model
Salesforce/blip-image-captioning-large is an image-to-text model that generates descriptive captions for images, built on the BLIP (Bootstrapping Language-Image Pre-training) framework with a Vision Transformer large (ViT-L) backbone. It can produce both unconditional captions and conditional captions guided by a text prompt.
Model Description
BLIP unifies vision-language understanding and generation in a single framework, bootstrapping noisy web data by generating synthetic captions and filtering them. This approach achieves state-of-the-art results across multiple tasks without requiring task-specific architectures.
Key Strengths
- Unified architecture – transfers to both understanding tasks (e.g., retrieval) and generation tasks (e.g., captioning, VQA).
- Bootstrapping method – a captioner creates synthetic captions and a filter removes noise, effectively leveraging large-scale web data.
- Strong generalization – demonstrates zero-shot performance on video-language tasks.
Performance Benchmarks
On the COCO dataset, BLIP with ViT-L achieves the following improvements over prior state-of-the-art models:
- Image-text retrieval: +2.7% in average recall@1
- Image captioning: +2.8% in CIDEr score
- Visual Question Answering: +1.6% in VQA score
These results are reported in the BLIP paper (arXiv:2201.12086). The model is pre-trained on 129 million images and fine-tuned for captioning on COCO.
Usage via Gigarouter
Gigarouter hosts this model as a managed, OpenAI-compatible API. Users send image data and receive generated captions without managing infrastructure. The model supports both conditional and unconditional captioning through a single API endpoint.
best for
- ·Automatic alt-text generation for images in web content
- ·Describing scenes for accessibility (screen readers)
- ·Captioning product images for e-commerce
FAQ
It performs image captioning (both conditional and unconditional), image-text retrieval, and visual question answering when finetuned, but this model is specialized for captioning.
BLIP Large uses a ViT-L backbone with more parameters, resulting in higher accuracy on captioning benchmarks like CIDEr (+2.8% over prior work). It is slower but more accurate.
The model accepts an image (processed to RGB tensor) and optionally a text prompt for conditional captioning. The output is a natural language caption string.
Call the gigarouter OpenAI-compatible endpoint with an API key, send the image as a base64-encoded string or URL, and receive the generated caption in the response.
Yes, it is released under the BSD-3-Clause license, allowing commercial use with attribution. However, the GitHub repo is deprecated; the recommended library is LAVIS.
We're benchmarking and onboarding BLIP Image Captioning Large as a hosted, OpenAI-compatible API. Sign in for free credit and be ready when it lands, or tell us you want it and we'll prioritize it.