BLIP
Source: transformers

# BLIP## OverviewThe BLIP model was proposed in [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://arxiv.org/abs/2201.12086) by Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi.BLIP is a model that is able to perform various multi-modal tasks including:- Visual Question Answering - Image-Text retrieval (Image-text matching)- Image CaptioningThe abstract from the paper is the following:*Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. Code, models, and datasets are released.*![BLIP.gif](https://cdn-uploads.huggingface.co/production/uploads/1670928184033-62441d1d9fdefb55a0b7d12c.gif)This model was contributed by [ybelkada](https://huggingface.co/ybelkada).The original code can be found [here](https://github.com/salesforce/BLIP).## Resources- [Jupyter notebook](https://github.com/huggingface/notebooks/blob/main/examples/image_captioning_blip.ipynb) on how to fine-tune BLIP for image captioning on a custom dataset## BlipConfig[[autodoc]] BlipConfig    - from_text_vision_configs## BlipTextConfig[[autodoc]] BlipTextConfig## BlipVisionConfig[[autodoc]] BlipVisionConfig## BlipProcessor[[autodoc]] BlipProcessor## BlipImageProcessor[[autodoc]] BlipImageProcessor    - preprocess<frameworkcontent><pt>## BlipModel`BlipModel` is going to be deprecated in future versions, please use `BlipForConditionalGeneration`, `BlipForImageTextRetrieval` or `BlipForQuestionAnswering` depending on your usecase.[[autodoc]] BlipModel    - forward    - get_text_features    - get_image_features## BlipTextModel[[autodoc]] BlipTextModel    - forward## BlipVisionModel[[autodoc]] BlipVisionModel    - forward## BlipForConditionalGeneration[[autodoc]] BlipForConditionalGeneration    - forward## BlipForImageTextRetrieval[[autodoc]] BlipForImageTextRetrieval    - forward## BlipForQuestionAnswering[[autodoc]] BlipForQuestionAnswering    - forward</pt><tf>## TFBlipModel[[autodoc]] TFBlipModel    - call    - get_text_features    - get_image_features## TFBlipTextModel[[autodoc]] TFBlipTextModel    - call## TFBlipVisionModel[[autodoc]] TFBlipVisionModel    - call## TFBlipForConditionalGeneration[[autodoc]] TFBlipForConditionalGeneration    - call## TFBlipForImageTextRetrieval[[autodoc]] TFBlipForImageTextRetrieval    - call## TFBlipForQuestionAnswering[[autodoc]] TFBlipForQuestionAnswering    - call</tf></frameworkcontent>