YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/

BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models

PyTorch ,ONNX and TensorRT implementation of YOLOv4

YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite

An easy to use PyTorch to TensorRT converter

OpenMMLab Model Deployment Framework

TensorRT MODNet, YOLOv4, YOLOv3, SSD, MTCNN, and GoogLeNet

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

YOLO5Face: Why Reinventing a Face Detector (https://arxiv.org/abs/2105.12931) ECCV Workshops 2022)

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、reg…

DAMO-YOLO: a fast and accurate object detection method with some new techs, including NAS backbones, efficient RepGFPN, ZeroHead, AlignedOTA, and distillation enhancement.

A Wide Range of Custom Functions for YOLOv4, YOLOv4-tiny, YOLOv3, and YOLOv3-tiny Implemented in TensorFlow, TFLite, and TensorRT.

PaddleSlim is an open-source library for deep model compression and architecture search.

PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT

Add bisenetv2. My implementation of BiSeNet

Real-time pose estimation accelerated with NVIDIA TensorRT

Fast and accurate object detection with end-to-end GPU optimization

Library for Fast and Flexible Human Pose Estimation

Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture.

High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀