Computer vision

PaddleOCR 3.5: Running OCR and Document Parsing Tasks with a Transformers Backend

Gradient-based Planning for World Models at Longer Horizons

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Introducing Mistral OCR 3

Supercharge your OCR Pipelines with Open Models

SOTA OCR with Core ML and dots.ocr

Finetuning olmOCR to be a faithful OCR-Engine

Mistral OCR

ScreenAI: A visual language model for UI and visually-situated language understanding

Posted by Srinivas Sunkara and Gilles Baechler, Software Engineers, Google Research Screen user interfaces (UIs) and infographics, such as charts, diagrams and tables, play important roles in human communication and human-machine interaction as they facilitate rich and interactive user experiences. UIs and infographics share similar design principles and visual language (e.

MELON: Reconstructing 3D objects from images with unknown poses

Posted by Mark Matthews, Senior Software Engineer, and Dmitry Lagun, Research Scientist, Google Research A person's prior experience and understanding of the world generally enables them to easily infer what an object looks like in whole, even if only looking at a few 2D pictures of it. Yet the capacity for a computer to reconstruct the shape of an object in 3D given only a few images has remained a difficult algorithmic problem for years.

Health-specific embedding tools for dermatology and pathology

Posted by Dave Steiner, Clinical Research Scientist, Google Health, and Rory Pilgrim, Product Manager, Google Research There’s a worldwide shortage of access to medical imaging expert interpretation across specialties including radiology , dermatology and pathology . Machine learning (ML) technology can help ease this burden by powering tools that enable doctors to interpret these images more accurately and efficiently.

VideoPrism: A foundational visual encoder for video understanding

Posted by Long Zhao, Senior Research Scientist, and Ting Liu, Senior Staff Software Engineer, Google Research An astounding number of videos are available on the Web, covering a variety of content from everyday moments people share to historical moments to scientific observations, each of which contains a unique record of the world. The right tools could help researchers analyze these videos, transforming how we understand the world around us.

Object Detection Leaderboard

Introducing Segment Anything: Working toward the first foundation model for image segmentation

The State of Computer Vision at Hugging Face 🤗

Universal Image Segmentation with Mask2Former and OneFormer

Zero-shot image segmentation with CLIPSeg

Image Classification with AutoTrain

Fine-Tune a Semantic Segmentation Model with a Custom Dataset

Fine-Tune ViT for Image Classification with 🤗 Transformers

Image GPT

We find that, just as a large transformer model trained on language can generate coherent text, the same exact model trained on pixel sequences can generate coherent image completions and samples. By establishing a correlation between sample quality and image classification accuracy, we show that our best generative model also contains features competitive with top convolutional nets in the unsupervised setting.