Tags: #computer-vision

A comprehensive library providing state-of-the-art pre-trained models for various machine learning tasks across text, vision, audio, and multimodal domains, facilitating both inference and training.

An AI-powered, vision-driven UI automation framework that enables natural language control and scripting across web, mobile, and custom interfaces.

Automates complex browser-based workflows using LLMs and computer vision, providing a resilient and adaptive solution for web interaction.

An advanced multimodal AI chatbot framework that enables conversational interaction and deep understanding of video and image content, integrating various large language models.

A comprehensive repository offering C++ and Python code examples for computer vision, deep learning, and AI research articles from LearnOpenCV.com.

A lightweight library providing one-line dataloaders and efficient pre-processing tools for a vast hub of AI datasets, supporting various ML frameworks.

An ultra-compact 7B parameter AI agent designed by Microsoft to automate multi-step computer tasks through visual perception and direct interface interaction.

Enables fast and efficient multi-concept customization of text-to-image diffusion models like Stable Diffusion using a few images.

A PyTorch-based framework to adapt Meta AI's Segment Anything Model (SAM) for improved performance on challenging downstream computer vision tasks using adapters and prompts.

An all-in-one open-source platform for multimodal data labeling and annotation, supporting 3D LiDAR, image, and LLM training data with AI-fueled tools.

A prompt rewriting tool that refines user prompts into clearer, structured versions to enhance the quality of text-to-image generation and image-to-image editing.

A series of video foundation models and large-scale datasets designed for comprehensive multimodal video understanding and generation.

A high-performance, comprehensive face recognition library built on PaddlePaddle and PyTorch.

A comprehensive curated list of resources, papers, datasets, and projects related to text-to-image generation and manipulation.

A comprehensive research hub for large-scale self-supervised pre-training of foundation models across diverse tasks, languages, and modalities.

Autodistill automates the process of training small, fast supervised models from unlabeled images by leveraging large foundation models, eliminating the need for manual data labeling.

A family of powerful multi-modal large language models (MLLMs) designed to advance AI's understanding and generation capabilities across various data types.

A lightweight C++ toolkit for deploying over 100 AI models across various inference engines.

A meticulously categorized collection of research papers on diffusion models, spanning various subareas from visual illusions to image restoration and text-guided editing.

SUPIR is an AI-driven project focused on developing practical algorithms for photo-realistic image restoration and upscaling in real-world scenarios.

A pioneering open-source multimodal large language model family aiming to match or exceed commercial models like GPT-4o/GPT-5 in performance.

MMPreTrain is an OpenMMLab project providing a comprehensive, open-source PyTorch-based toolbox for pre-training and benchmarking various computer vision and multi-modal models.