🛠 A lite C++ toolkit of awesome AI models with ONNXRuntime, NCNN, MNN and TNN. YOLOX, YOLOP, MODNet, YOLOR, NanoDet, YOLOX, SCRFD, YOLOX . MNN, NCNN, TNN, ONNXRuntime, CPU/GPU.

Security Orchestration, Automation and Response (SOAR) Platform. 安全编排与自动化响应平台，无需编写代码的安全自动化，使用 SOAR 可以让团队工作更加高效

Awesome Artificial Intelligence Projects

OpenMMLab Detection Toolbox and Benchmark

The pytorch re-implement of the official efficientdet with SOTA performance in real time and pretrained weights.

Training examples and results for ImageNet/CIFAR/COCO/VOC training.

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

A quickstart and benchmark for pytorch distributed training.

Codes for "TENER: Adapting Transformer Encoder for Named Entity Recognition"

Synchronized Batch Normalization implementation in PyTorch.

Unifying Public Datasets for Insulator Detectionand Fault Classification in Electrical Power Lines

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。

GRE Review, do the summary by myself

Pytorch implementation of RetinaNet object detection.

中文自然语言处理数据集，平时做做实验的材料。欢迎补充提交合并。

Keras implementation of RetinaNet object detection.

Minimal PyTorch implementation of YOLOv3

A PyTorch implementation of the YOLO v3 object detection algorithm

A collection of machine learning examples and tutorials.

A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.

PyTorch implementations of deep reinforcement learning algorithms and environments

pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行

A faster pytorch implementation of faster r-cnn

Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.

🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

PyTorch Tutorial for Deep Learning Researchers

💬 Machine Learning Course with Python. Refer to the course page for step-by-step explanations.