All-in-one training for vision models (YOLO, ViTs, RT-DETR, DINOv3): pretraining, fine-tuning, distillation.

CUDA Python: Performance meets Productivity

深度学习经典、新论文逐段精读

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

This repository contains demos I made with the Transformers library by HuggingFace.

🚀🚀 「大模型」2小时完全从0训练26M的小参数GPT！🌏 Train a 26M-parameter GPT from scratch in just 2h!

Development repository for the Triton language and compiler

YOLOv10: Real-Time End-to-End Object Detection [NeurIPS 2024]

Build high-performance AI models with modular building blocks

YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

Ultralytics YOLO 🚀

Effortless AI-assisted data labeling with AI support from YOLO, Segment Anything (SAM+SAM2), MobileSAM!!

A playbook for systematically maximizing the performance of deep learning models.

Framework agnostic sliced/tiled inference + interactive ui + error analysis plots

Example of using ultralytics YOLO V5 with OpenCV 4.5.4, C++ and Python

Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image …

🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

Carbon Language's main repository: documents, design, implementation, and related tools. (NOTE: Carbon Language is experimental; see README)

🔥🔥🔥🔥 (Earlier YOLOv7 not official one) YOLO with Transformers and Instance Segmentation, with TensorRT acceleration! 🔥🔥🔥

YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )

Official repository for RADTorch - The Medical Imaging Machine Learning Framework

Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.

Concise deep learning for JAX

A paper list of object detection using deep learning.

单阶段通用目标检测器

Datasets, Transforms and Models specific to Computer Vision

A collection of various deep learning architectures, models, and tips

A course project for UCSD ECE285, from group Sageband

《机器学习》（西瓜书）公式详解