Popular GitHub Repositories for Computer Vision Algorithms

AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.

《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。

100 Days of ML Coding

12 Weeks, 24 Lessons, AI for All!

GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.

Jan is an open source alternative to ChatGPT that runs 100% offline on your computer

The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

Caffe: a fast open framework for deep learning.

OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation

We write your reusable computer vision tools. ?

Collection of various algorithms in mathematics, machine learning, computer science and physics implemented in C++ for educational purposes.

Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.

OpenMMLab Detection Toolbox and Benchmark

Cross-platform, customizable ML solutions for live and streaming media.

Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.

Enhanced ChatGPT Clone: Features Agents, DeepSeek, Anthropic, AWS, OpenAI, Assistants API, Azure, Groq, o1, GPT-4o, Mistral, OpenRouter, Vertex AI, Gemini, Artifacts, AI model switching, message search, Code Interpreter, langchain, DALL-E-3, OpenAPI Actions, Functions, Secure Multi-User Auth, Presets, open-source for self-hosting. Active project.

A complete daily plan for studying to become a machine learning engineer.

? Papers & tech blogs by companies sharing their work on data science & machine learning in production.

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.