4.1 KiB
Pix2Struct
Overview
The Pix2Struct model was proposed in Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding by Kenton Lee, Mandar Joshi, Iulia Turc, Hexiang Hu, Fangyu Liu, Julian Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, Kristina Toutanova.
The abstract from the paper is the following:
Visually-situated language is ubiquitous -- sources range from textbooks with diagrams to web pages with images and tables, to mobile apps with buttons and forms. Perhaps due to this diversity, previous work has typically relied on domain-specific recipes with limited sharing of the underlying data, model architectures, and objectives. We present Pix2Struct, a pretrained image-to-text model for purely visual language understanding, which can be finetuned on tasks containing visually-situated language. Pix2Struct is pretrained by learning to parse masked screenshots of web pages into simplified HTML. The web, with its richness of visual elements cleanly reflected in the HTML structure, provides a large source of pretraining data well suited to the diversity of downstream tasks. Intuitively, this objective subsumes common pretraining signals such as OCR, language modeling, image captioning. In addition to the novel pretraining strategy, we introduce a variable-resolution input representation and a more flexible integration of language and vision inputs, where language prompts such as questions are rendered directly on top of the input image. For the first time, we show that a single pretrained model can achieve state-of-the-art results in six out of nine tasks across four domains: documents, illustrations, user interfaces, and natural images.
Tips:
Pix2Struct has been fine tuned on a variety of tasks and datasets, ranging from image captioning, visual question answering (VQA) over different inputs (books, charts, science diagrams), captioning UI components etc. The full list can be found in Table 1 of the paper. We therefore advise you to use these models for the tasks they have been fine tuned on. For instance, if you want to use Pix2Struct for UI captioning, you should use the model fine tuned on the UI dataset. If you want to use Pix2Struct for image captioning, you should use the model fine tuned on the natural images captioning dataset and so on.
If you want to use the model to perform conditional text captioning, make sure to use the processor with add_special_tokens=False.
This model was contributed by ybelkada. The original code can be found here.
Resources
Pix2StructConfig
autodoc Pix2StructConfig - from_text_vision_configs
Pix2StructTextConfig
autodoc Pix2StructTextConfig
Pix2StructVisionConfig
autodoc Pix2StructVisionConfig
Pix2StructProcessor
autodoc Pix2StructProcessor
Pix2StructImageProcessor
autodoc Pix2StructImageProcessor - preprocess
Pix2StructTextModel
autodoc Pix2StructTextModel - forward
Pix2StructVisionModel
autodoc Pix2StructVisionModel - forward
Pix2StructForConditionalGeneration
autodoc Pix2StructForConditionalGeneration - forward