transformers/docs/source/en/model_doc/blip.mdx

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# BLIP

## Overview

The 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 Captioning

The 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://s3.amazonaws.com/moonup/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

## BlipModel

[[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

## 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