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* Reverse dependency map shouldn't be created when test_all is set * [test_all] Remove dummies * Modular fixes * Update utils/check_repo.py Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com> * [test_all] Better docs * [test_all] Update src/transformers/commands/chat.py Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> * [test_all] Remove deprecated AdaptiveEmbeddings from the tests * [test_all] Doc builder * [test_all] is_dummy * [test_all] Import utils * [test_all] Doc building should not require all deps --------- Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com> Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
157 lines
4.6 KiB
Markdown
157 lines
4.6 KiB
Markdown
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# BLIP
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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<img alt="TensorFlow" src="https://img.shields.io/badge/TensorFlow-FF6F00?style=flat&logo=tensorflow&logoColor=white">
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</div>
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## Overview
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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.
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BLIP is a model that is able to perform various multi-modal tasks including:
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- Visual Question Answering
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- Image-Text retrieval (Image-text matching)
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- Image Captioning
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The abstract from the paper is the following:
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*Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks.
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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.*
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This model was contributed by [ybelkada](https://huggingface.co/ybelkada).
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The original code can be found [here](https://github.com/salesforce/BLIP).
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## Resources
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- [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
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## BlipConfig
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[[autodoc]] BlipConfig
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- from_text_vision_configs
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## BlipTextConfig
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[[autodoc]] BlipTextConfig
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## BlipVisionConfig
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[[autodoc]] BlipVisionConfig
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## BlipProcessor
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[[autodoc]] BlipProcessor
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## BlipImageProcessor
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[[autodoc]] BlipImageProcessor
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- preprocess
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## BlipImageProcessorFast
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[[autodoc]] BlipImageProcessorFast
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- preprocess
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<frameworkcontent>
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<pt>
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## BlipModel
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`BlipModel` is going to be deprecated in future versions, please use `BlipForConditionalGeneration`, `BlipForImageTextRetrieval` or `BlipForQuestionAnswering` depending on your usecase.
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[[autodoc]] BlipModel
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- forward
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- get_text_features
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- get_image_features
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## BlipTextModel
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[[autodoc]] BlipTextModel
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- forward
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## BlipTextLMHeadModel
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[[autodoc]] BlipTextLMHeadModel
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- forward
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## BlipVisionModel
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[[autodoc]] BlipVisionModel
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- forward
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## BlipForConditionalGeneration
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[[autodoc]] BlipForConditionalGeneration
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- forward
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## BlipForImageTextRetrieval
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[[autodoc]] BlipForImageTextRetrieval
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- forward
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## BlipForQuestionAnswering
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[[autodoc]] BlipForQuestionAnswering
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- forward
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</pt>
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<tf>
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## TFBlipModel
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[[autodoc]] TFBlipModel
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- call
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- get_text_features
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- get_image_features
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## TFBlipTextModel
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[[autodoc]] TFBlipTextModel
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- call
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## TFBlipTextLMHeadModel
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[[autodoc]] TFBlipTextLMHeadModel
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- forward
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## TFBlipVisionModel
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[[autodoc]] TFBlipVisionModel
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- call
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## TFBlipForConditionalGeneration
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[[autodoc]] TFBlipForConditionalGeneration
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- call
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## TFBlipForImageTextRetrieval
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[[autodoc]] TFBlipForImageTextRetrieval
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- call
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## TFBlipForQuestionAnswering
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[[autodoc]] TFBlipForQuestionAnswering
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- call
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</tf>
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</frameworkcontent>
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