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https://github.com/codingonion/awesome-yolo-object-detection
🚀🚀🚀 A collection of some awesome public YOLO object detection series projects.
https://github.com/codingonion/awesome-yolo-object-detection
attention autonomous-driving awesome-list cuda gpt gui llama3 llm object-detection onnx qt snn sora spiking-neural-network tensorrt ultralytics yolo yolov5 yolov8 yolov9
Last synced: 3 months ago
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🚀🚀🚀 A collection of some awesome public YOLO object detection series projects.
- Host: GitHub
- URL: https://github.com/codingonion/awesome-yolo-object-detection
- Owner: codingonion
- Created: 2022-02-19T17:57:06.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-04-27T14:50:44.000Z (8 months ago)
- Last Synced: 2024-05-23T07:03:03.784Z (7 months ago)
- Topics: attention, autonomous-driving, awesome-list, cuda, gpt, gui, llama3, llm, object-detection, onnx, qt, snn, sora, spiking-neural-network, tensorrt, ultralytics, yolo, yolov5, yolov8, yolov9
- Homepage:
- Size: 251 KB
- Stars: 1,076
- Watchers: 28
- Forks: 171
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- ultimate-awesome - awesome-yolo-object-detection - 🚀🚀🚀 A collection of some awesome public YOLO object detection series projects. (Programming Language Lists / Python Lists)
- awesome-yolo-object-detection - awesome-yolo-object-detection - yolo-object-detection?style=social"/> : 🚀🚀🚀 A collection of some awesome YOLO object detection series projects. (Summary)
- awesome-yolo-object-detection - awesome-yolo-object-detection - yolo-object-detection?style=social"/> : 🚀🚀🚀 A collection of some awesome YOLO object detection series projects. (Summary)
README
# Awesome-YOLO-Object-Detection
[](https://github.com/sindresorhus/awesome)
🚀🚀🚀 YOLO is a great real-time one-stage object detection framework. This repository lists some awesome public YOLO object detection series projects.
## Contents
- [Awesome-YOLO-Object-Detection](#awesome-yolo-object-detection)
- [Summary](#summary)
- [Official YOLO](#official-yolo)
- [Awesome List](#awesome-list)
- [Paper and Code Overview](#paper-and-code-overview)
- [Paper Review](#paper-review)
- [Code Review](#code-review)
- [Learning Resources](#learning-resources)
- [Extensional Frameworks](#extensional-frameworks)
- [Other Versions of YOLO](#other-versions-of-yolo)
- [PyTorch Implementation](#pytorch-implementation)
- [C Implementation](#c-implementation)
- [CPP Implementation](#cpp-implementation)
- [ROS Implementation](#ros-implementation)
- [Mojo Implementation](#mojo-implementation)
- [Rust Implementation](#rust-implementation)
- [Go Implementation](#go-implementation)
- [CSharp Implementation](#csharp-implementation)
- [Tensorflow and Keras Implementation](#tensorflow-and-keras-implementation)
- [PaddlePaddle Implementation](#paddlepaddle-implementation)
- [Caffe Implementation](#caffe-implementation)
- [MXNet Implementation](#mxnet-implementation)
- [Web Implementation](#web-implementation)
- [Others](#others)
- [Lighter and Deployment Frameworks](#lighter-and-deployment-frameworks)
- [Lightweight Backbones and FPN](#lightweight-backbones-and-fpn)
- [Pruning Knoweldge-Distillation Quantization](#pruning-knoweldge-distillation-quantization)
- [Pruning](#pruning)
- [Quantization](#quantization)
- [Knoweldge-Distillation](#knoweldge-distillation)
- [High-performance Inference Engine](#high-performance-inference-engine)
- [ONNX](#onnx)
- [TensorRT](#tensorrt)
- [DeepStream](#deepstream)
- [OpenVINO](#openvino)
- [NCNN](#ncnn)
- [MNN](#mnn)
- [Other Engine](#other-engine)
- [FPGA TPU NPU Hardware Deployment](#fpga-tpu-npu-hardware-deployment)
- [FPGA](#fpga)
- [RK3588](#rk3588)
- [Other Hardware](#other-hardware)
- [Applications](#applications)
- [Video Object Detection](#video-object-detection)
- [Object Tracking](#object-tracking)
- [Multi-Object Tracking](#multi-object-tracking)
- [Dynamic Object Tracking](#Dynamic-object-tracking)
- [Deep Reinforcement Learning](#deep-reinforcement-learning)
- [Motion Control Field](#motion-control-field)
- [Super-Resolution Field](#super-resolution-field)
- [Spiking Neural Network](#spiking-neural-network)
- [Attention and Transformer](#attention-and-transformer)
- [Small Object Detection](#small-object-detection)
- [Few-shot Object Detection](#few-shot-object-detection)
- [Open World Object Detection](#open-world-object-detection)
- [Oriented Object Detection](#oriented-object-detection)
- [Face Detection and Recognition](#face-detection-and-recognition)
- [Face Detection](#face-detection)
- [Face Recognition](#face-recognition)
- [Face Mask Detection](#face-mask-detection)
- [Social Distance Detection](#social-distance-detection)
- [Autonomous Driving Field Detection](#autonomous-driving-field-detection)
- [Vehicle Detection](#vehicle-detection)
- [License Plate Detection and Recognition](#license-plate-detection-and-recognition)
- [Lane Detection](#lane-detection)
- [Driving Behavior Detection](#driving-behavior-detection)
- [Parking Slot Detection](#parking-slot-detection)
- [Traffic Light Detection](#traffic-light-detection)
- [Traffic Sign Detection](#traffic-sign-detection)
- [Crosswalk Detection](#crosswalk-detection)
- [Traffic Accidents Detection](#traffic-accidents-detection)
- [Road Damage Detection](#road-damage-detection)
- [Animal Detection](#animal-detection)
- [Helmet Detection](#helmet-detection)
- [Hand Detection](#hand-detection)
- [Gesture Recognition](#gesture-recognition)
- [Action Detection](#action-detection)
- [Emotion Recognition](#emotion-recognition)
- [Human Pose Estimation](#human-pose-estimation)
- [Distance Measurement](#distance-measurement)
- [Instance and Semantic Segmentation](#instance-and-semantic-segmentation)
- [3D Object Detection](#3d-object-detection)
- [SLAM Field Detection](#slam-field-detection)
- [Industrial Defect Detection](#industrial-defect-detection)
- [SAR Image Detection](#sar-image-detection)
- [Multispectral Image Fusion Detection](#multispectral-image-fusion-detection)
- [Safety Monitoring Field Detection](#safety-monitoring-field-detection)
- [Anti-UAV Field Detection](#anti-uav-field-detection)
- [Medical Field Detection](#medical-field-detection)
- [Chemistry Field Detection](#chemistry-field-detection)
- [Agricultural Field Detection](#agricultural-field-detection)
- [Sports Field Detection](#sports-field-detection)
- [Adverse Weather Conditions](#adverse-weather-conditions)
- [Adversarial Attack and Defense](#adversarial-attack-and-defense)
- [Camouflaged Detection](#camouflaged-detection)
- [Game Field Detection](#game-field-detection)
- [Automatic Annotation Tools](#automatic-annotation-tools)
- [Feature Map Visualization](#feature-map-visualization)
- [Object Detection Evaluation Metrics](#object-detection-evaluation-metrics)
- [GUI](#gui)
- [Swift-Related](#swift-related)
- [Flutter-Related](#flutter-related)
- [Streamlit-Related](#streamlit-related)
- [Gradio-Related](#gradio-related)
- [QT-Related](#qt-related)
- [PySide-Related](#pyside-related)
- [Other Applications](#other-applications)
- [Blogs](#blogs)
- [Videos](#videos)
## Summary
- ### Official YOLO
- [YOLOv1](https://pjreddie.com/darknet/yolov1) ([Darknet](https://github.com/pjreddie/darknet) 
) : "You Only Look Once: Unified, Real-Time Object Detection". (**[CVPR 2016](https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Redmon_You_Only_Look_CVPR_2016_paper.html)**)
- [YOLOv2](https://pjreddie.com/darknet/yolov2) ([Darknet](https://github.com/pjreddie/darknet) ) : "YOLO9000: Better, Faster, Stronger". (**[CVPR 2017](https://openaccess.thecvf.com/content_cvpr_2017/html/Redmon_YOLO9000_Better_Faster_CVPR_2017_paper.html)**)
- [YOLOv3](https://pjreddie.com/darknet/yolo) ([Darknet](https://github.com/pjreddie/darknet) ) : "YOLOv3: An Incremental Improvement". (**[arXiv 2018](https://arxiv.org/abs/1804.02767)**)
- [YOLOv4](https://github.com/AlexeyAB/darknet) 
([WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4) 
) : "YOLOv4: Optimal Speed and Accuracy of Object Detection". (**[arXiv 2020](https://arxiv.org/abs/2004.10934)**)
- [Scaled-YOLOv4](https://github.com/AlexeyAB/darknet) ([WongKinYiu/ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4) 
) : "Scaled-YOLOv4: Scaling Cross Stage Partial Network". (**[CVPR 2021](https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scaled-YOLOv4_Scaling_Cross_Stage_Partial_Network_CVPR_2021_paper.html)**)
- [YOLOv5](https://github.com/ultralytics/yolov5) 
: YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite. [docs.ultralytics.com](https://docs.ultralytics.com/). YOLOv5 🚀 is the world's most loved vision AI, representing [Ultralytics](https://ultralytics.com/) open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
- [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) 
: "YOLOX: Exceeding YOLO Series in 2021". (**[arXiv 2021](https://arxiv.org/abs/2107.08430)**)
- [YOLOR](https://github.com/WongKinYiu/yolor) 
: "You Only Learn One Representation: Unified Network for Multiple Tasks". (**[arXiv 2021](https://arxiv.org/abs/2105.04206)**)
- [YOLOF](https://github.com/megvii-model/YOLOF) 
: "You Only Look One-level Feature". (**[CVPR 2021](https://openaccess.thecvf.com/content/CVPR2021/html/Chen_You_Only_Look_One-Level_Feature_CVPR_2021_paper.html)**). "微信公众号「计算机视觉研究院」《[CVPR目标检测新框架：不再是YOLO，而是只需要一层特征（干货满满，建议收藏）](https://mp.weixin.qq.com/s/5sTxdjhKIPpQ-rCsWfe80A)》"。
- [YOLOS](https://github.com/hustvl/YOLOS) 
: "You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection". (**[NeurIPS 2021](https://proceedings.neurips.cc//paper/2021/hash/dc912a253d1e9ba40e2c597ed2376640-Abstract.html)**)
- [YOLOv6](https://github.com/meituan/YOLOv6) 
: "YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications". (**[arXiv 2022](https://arxiv.org/abs/2209.02976)**). "微信公众号「美团技术团队」《[YOLOv6：又快又准的目标检测框架开源啦](https://mp.weixin.qq.com/s/RrQCP4pTSwpTmSgvly9evg)》"。"微信公众号「美团技术团队」《[目标检测开源框架YOLOv6全面升级，更快更准的2.0版本来啦](https://mp.weixin.qq.com/s/9FyvWrHErfgJrVXIC_PKqg)》"。"微信公众号「美团技术团队」《[通用目标检测开源框架YOLOv6在美团的量化部署实战 ](https://mp.weixin.qq.com/s/J-3saNkCCAHLjkZQ3VCaeQ)》"。 "微信公众号「集智书童」《[超越YOLOv7 | YOLOv6论文放出，重参+自蒸馏+感知量化+...各种Tricks大放异彩](https://mp.weixin.qq.com/s/DPHC7bO1Q-IKDUqPU7DSJA)》"。"微信公众号「极市平台」《[Repvgg-style ConvNets，硬件友好！详解YOLOv6的高效backbone：EfficientRep](https://mp.weixin.qq.com/s/2Md30QdqgWnWwVR7d4sx1Q)》"。
- [YOLOv7](https://github.com/WongKinYiu/yolov7) 
: "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors". (**[CVPR 2023](https://arxiv.org/abs/2207.02696)**). "微信公众号「CVer」《[CVPR 2023 | YOLOv7强势收录！时隔6年，YOLOv系列再登CVPR！](https://mp.weixin.qq.com/s/HjaszZYPLoV03Z4Rw9KiCg)》"。
- [DAMO-YOLO](https://github.com/tinyvision/DAMO-YOLO) 
: DAMO-YOLO: a fast and accurate object detection method with some new techs, including NAS backbones, efficient RepGFPN, ZeroHead, AlignedOTA, and distillation enhancement. "DAMO-YOLO : A Report on Real-Time Object Detection Design". (**[arXiv 2022](https://arxiv.org/abs/2211.15444)**)
- [DynamicDet](https://github.com/VDIGPKU/DynamicDet) 
: "DynamicDet: A Unified Dynamic Architecture for Object Detection". (**[CVPR 2023](https://arxiv.org/abs/2304.05552)**)
- [EdgeYOLO](https://github.com/LSH9832/edgeyolo) 
: EdgeYOLO: anchor-free, edge-friendly. an edge-real-time anchor-free object detector with decent performance. "Edge YOLO: Real-time intelligent object detection system based on edge-cloud cooperation in autonomous vehicles". (**[IEEE Transactions on Intelligent Transportation Systems, 2022](https://ieeexplore.ieee.org/abstract/document/9740044)**). "EdgeYOLO: An Edge-Real-Time Object Detector". (**[arXiv 2023](https://arxiv.org/abs/2302.07483)**)
- [RT-DETR](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rtdetr) 
: "DETRs Beat YOLOs on Real-time Object Detection". (**[arXiv 2023](https://arxiv.org/abs/2304.08069)**). "微信公众号「集智书童」《[YOLO超快时代终结了 | RT-DETR用114FPS实现54.8AP，远超YOLOv8](https://mp.weixin.qq.com/s/V3MUXinJhpq8J4UWTUL17w)》"。
- [YOLO-NAS](https://github.com/Deci-AI/super-gradients) 
: Easily train or fine-tune SOTA computer vision models with one open source training library. The home of [Yolo-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md). [www.supergradients.com](https://www.supergradients.com/). YOLO-NAS: A Next-Generation, Object Detection Foundational Model generated by Deci’s Neural Architecture Search Technology. Deci is thrilled to announce the release of a new object detection model, YOLO-NAS - a game-changer in the world of object detection, providing superior real-time object detection capabilities and production-ready performance. Deci's mission is to provide AI teams with tools to remove development barriers and attain efficient inference performance more quickly. The new YOLO-NAS delivers state-of-the-art (SOTA) performance with the unparalleled accuracy-speed performance, outperforming other models such as YOLOv5, YOLOv6, YOLOv7 and YOLOv8.
- [YOLO-World](https://github.com/AILab-CVC/YOLO-World) 
: "YOLO-World: Real-Time Open-Vocabulary Object Detection". (**[CVPR 2024](https://arxiv.org/abs/2401.17270)**). [www.yoloworld.cc](https://www.yoloworld.cc/)
- [YOLOv8](https://github.com/ultralytics/ultralytics) 
: NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite. [docs.ultralytics.com](https://docs.ultralytics.com/)
- [YOLOv9](https://github.com/WongKinYiu/yolov9) 
: "YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information". (**[arXiv 2024](https://arxiv.org/abs/2402.13616)**)
- [LeYOLO](https://github.com/LilianHollard/LeYOLO) 
: "LeYOLO, New Scalable and Efficient CNN Architecture for Object Detection". (**[arXiv 2024](https://arxiv.org/abs/2406.14239)**)
- [YOLOv10](https://github.com/THU-MIG/yolov10) 
: "YOLOv10: Real-Time End-to-End Object Detection". (**[arXiv 2024](https://arxiv.org/abs/2405.14458v1)**)
- [YOLOv11](https://github.com/ultralytics/ultralytics) : NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite. [Ultralytics](https://www.ultralytics.com/) [YOLOv11](https://github.com/ultralytics/ultralytics) s a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLO11 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks. [docs.ultralytics.com](https://docs.ultralytics.com/)
- ### Awesome List
- [awesome-yolo-object-detection](https://github.com/codingonion/awesome-yolo-object-detection) 
: 🚀🚀🚀 A collection of some awesome YOLO object detection series projects.
- [srebroa/awesome-yolo](https://github.com/srebroa/awesome-yolo) 
: 🚀 ⭐ The list of the most popular YOLO algorithms - awesome YOLO.
- [Bubble-water/YOLO-Summary](https://github.com/Bubble-water/YOLO-Summary) 
: YOLO-Summary.
- [WZMIAOMIAO/deep-learning-for-image-processing](https://github.com/WZMIAOMIAO/deep-learning-for-image-processing) 
: deep learning for image processing including classification and object-detection etc.
- [hoya012/deep_learning_object_detection](https://github.com/hoya012/deep_learning_object_detection) 
: A paper list of object detection using deep learning.
- [amusi/awesome-object-detection](https://github.com/amusi/awesome-object-detection) 
: Awesome Object Detection.
- ### Paper and Code Overview
- #### Paper Review
- [52CV/CV-Surveys](https://github.com/52CV/CV-Surveys) 
: 计算机视觉相关综述。包括目标检测、跟踪........
- [GreenTeaHua/YOLO-Review](https://github.com/GreenTeaHua/YOLO-Review) 
: "A Review of YOLO Object Detection Based on Deep Learning". "基于深度学习的YOLO目标检测综述". (**[Journal of Electronics & Information Technology 2022](https://jeit.ac.cn/cn/article/doi/10.11999/JEIT210790)**)
- "A Review of Yolo Algorithm Developments". (**[Procedia Computer Science 2022](https://www.sciencedirect.com/science/article/pii/S1877050922001363)**)
- #### Code Review
- [MMDetection](https://github.com/open-mmlab/mmdetection) 
: OpenMMLab Detection Toolbox and Benchmark. [mmdetection.readthedocs.io](https://mmdetection.readthedocs.io/en/latest/). (**[arXiv 2019](https://arxiv.org/abs/1906.07155)**)
- [MMYOLO](https://github.com/open-mmlab/mmyolo) 
: OpenMMLab YOLO series toolbox and benchmark. Implemented RTMDet, RTMDet-Rotated,YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOX, PPYOLOE, etc. [mmyolo.readthedocs.io/zh_CN/dev/](https://mmyolo.readthedocs.io/zh_CN/dev/)
- [iscyy/ultralyticsPro](https://github.com/iscyy/ultralyticsPro) 
: 🔥🔥🔥专注于改进YOLOv8模型，NEW - YOLOv8 🚀 RT-DETR 🥇 in PyTorch >, Support to improve backbone, neck, head, loss, IoU, NMS and other modules🚀
- [iscyy/yoloair](https://github.com/iscyy/yoloair) 
: YOLO Air : Makes improvements easy again. 🔥🔥🔥YOLOv5, YOLOv6, YOLOv7, YOLOv8, PPYOLOE, YOLOX, YOLOR, YOLOv4, YOLOv3, Transformer, Attention, TOOD and Improved-YOLOv5-YOLOv7... Support to improve backbone, neck, head, loss, IoU, NMS and other modules🚀. YOLOAir是一个基于PyTorch的YOLO算法库。统一模型代码框架、统一应用、统一改进、易于模块组合、构建更强大的网络模型。 "微信公众号「FightingCV」《[YOLOAir | 面向小白的目标检测库，更快更方便更完整的YOLO库](https://mp.weixin.qq.com/s/smwx-Ievs3rWMw_D4lSwqg)》"。 "微信公众号「我爱计算机视觉」《[集成多种YOLO改进点，面向小白科研的YOLO检测代码库YOLOAir](https://mp.weixin.qq.com/s/EEJrnfnTn7wAcEpVPx06BQ)》"
- [iscyy/yoloair2](https://github.com/iscyy/yoloair2) 
: YOLOAir2☁️💡🎈 : Makes improvements easy again. ☁️💡🎈YOLOAir2 is the second version of the YOLOAir series, The framework is based on YOLOv7, including YOLOv7, YOLOv8, YOLOv6, YOLOv5, YOLOX, YOLOR, YOLOv4, YOLOv3, Transformer, Attention and Improved-YOLOv7... Support to improve Backbone, Neck, Head, Loss, IoU, NMS and other modules.
- [jizhishutong/YOLOU](https://github.com/jizhishutong/YOLOU) 
: YOLOU：United, Study and easier to Deploy. The purpose of our creation of YOLOU is to better learn the algorithms of the YOLO series and pay tribute to our predecessors. YOLOv3、YOLOv4、YOLOv5、YOLOv5-Lite、YOLOv6-v1、YOLOv6-v2、YOLOv7、YOLOX、YOLOX-Lite、PP-YOLOE、PP-PicoDet-Plus、YOLO-Fastest v2、FastestDet、YOLOv5-SPD、TensorRT、NCNN、Tengine、OpenVINO. "微信公众号「集智书童」《[YOLOU开源 | 汇集YOLO系列所有算法，集算法学习、科研改进、落地于一身！](https://mp.weixin.qq.com/s/clupheQ8iHnhR4FJcTtB8A)》"
- [WangQvQ/Yolov5_Magic](https://github.com/WangQvQ/Yolov5_Magic) 
: YOLO Magic🪄 is an extension based on Ultralytics' YOLOv5, designed to provide more powerful functionality and simpler operations for visual tasks.
- [positive666/yolo_research](https://github.com/positive666/yolo_research) 
: 🚀 yolo_reserach PLUS High-level. based on yolo-high-level project (detect\pose\classify\segment\):include yolov5\yolov7\yolov8\ core ,improvement research ,SwintransformV2 and Attention Series. training skills, business customization, engineering deployment.
- [augmentedstartups/AS-One](https://github.com/augmentedstartups/AS-One) 
: Easy & Modular Computer Vision Detectors and Trackers - Run YOLO-NAS,v8,v7,v6,v5,R,X in under 20 lines of code. [www.augmentedstartups.com](https://www.augmentedstartups.com/)
- [Oneflow-Inc/one-yolov5](https://github.com/Oneflow-Inc/one-yolov5) 
: A more efficient yolov5 with oneflow backend 🎉🎉🎉. "微信公众号「GiantPandaCV」《[One-YOLOv5 发布，一个训得更快的YOLOv5](https://mp.weixin.qq.com/s/tZ7swUd0biz7G3CiRkHHfw)》"
- [PaddlePaddle/PaddleYOLO](https://github.com/PaddlePaddle/PaddleYOLO) 
: 🚀🚀🚀 YOLO series of PaddlePaddle implementation, PP-YOLOE+, YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOX, YOLOv5u, YOLOv7u, RTMDet and so on. 🚀🚀🚀
- [WangRongsheng/BestYOLO](https://github.com/WangRongsheng/BestYOLO) 
: 🌟Change the world, it will become a better place. | 以科研和竞赛为导向的最好的YOLO实践框架!
- [KangChou/Cver4s](https://github.com/KangChou/Cver4s) 
: Cver4s：Computer vision algorithm code base.
- [chaizwj/yolov8-tricks](https://github.com/chaizwj/yolov8-tricks) 
: 目标检测，采用yolov8作为基准模型，数据集采用VisDrone2019，带有自己的改进策略。
- ### Learning Resources
- [KuiperInfer (自制深度学习推理框架)](https://github.com/zjhellofss/KuiperInfer) 
: 带你从零实现一个高性能的深度学习推理库，支持llama 、Unet、Yolov5、Resnet等模型的推理。Implement a high-performance deep learning inference library step by step.
- [kuiperdatawhale](https://github.com/zjhellofss/kuiperdatawhale) 
: 从零自制深度学习推理框架。
- [roboflow/notebooks](https://github.com/roboflow/notebooks) 
: Examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models like Grounding DINO and SAM. [roboflow.com/models](https://roboflow.com/models)
- [yjh0410/PyTorch_YOLO_Tutorial](https://github.com/yjh0410/PyTorch_YOLO_Tutorial) 
: YOLO Tutorial.
- [HuKai97/yolov5-5.x-annotations](https://github.com/HuKai97/yolov5-5.x-annotations) 
: 一个基于yolov5-5.0的中文注释版本！
- [crkk-feng/yolov5-annotations](https://github.com/crkk-feng/yolov5-annotations) 
: A Chinese annotated version of yolov5-5.0.
- [XiaoJiNu/yolov5-v6-chinese-comment](https://github.com/XiaoJiNu/yolov5-v6-chinese-comment) 
: yolov5-v6版本注释。
- [1131624548/About-YOLOv5-7-0](https://github.com/1131624548/About-YOLOv5-7-0) : YOLOv5代码注释。
- [zyds/yolov5-code](https://github.com/zyds/yolov5-code) 
: 手把手带你实战 YOLOv5。
## Extensional Frameworks
- [EasyCV](https://github.com/alibaba/EasyCV) 
: An all-in-one toolkit for computer vision. "YOLOX-PAI: An Improved YOLOX, Stronger and Faster than YOLOv6". (**[arXiv 2022](https://arxiv.org/abs/2208.13040)**). "微信公众号「集智书童」《[YOLOX升级 | 阿里巴巴提出YOLOX-PAI，1ms内精度无敌，超越YOLOv6、PP-YOLOE](https://mp.weixin.qq.com/s/bIu3cYyZ-fVb5iB0bTfyug)》"
- [YOLACT & YOLACT++](https://github.com/dbolya/yolact) 
: You Only Look At CoefficienTs. (**[ICCV 2019](https://openaccess.thecvf.com/content_ICCV_2019/html/Bolya_YOLACT_Real-Time_Instance_Segmentation_ICCV_2019_paper.html), [IEEE TPAMI 2020](https://ieeexplore.ieee.org/abstract/document/9159935)**)
- [Alpha-IoU](https://github.com/Jacobi93/Alpha-IoU) 
: "Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression". (**[NeurIPS 2021](https://proceedings.neurips.cc//paper/2021/hash/a8f15eda80c50adb0e71943adc8015cf-Abstract.html)**)
- [CIoU](https://github.com/Zzh-tju/CIoU) 
: Complete-IoU (CIoU) Loss and Cluster-NMS for Object Detection and Instance Segmentation (YOLACT). (**[AAAI 2020](https://ojs.aaai.org/index.php/AAAI/article/view/6999), [IEEE TCYB 2021](https://ieeexplore.ieee.org/abstract/document/9523600)**)
- [Albumentations](https://github.com/albumentations-team/albumentations) 
: Albumentations is a Python library for image augmentation. Image augmentation is used in deep learning and computer vision tasks to increase the quality of trained models. The purpose of image augmentation is to create new training samples from the existing data. "Albumentations: Fast and Flexible Image Augmentations". (**[Information 2020](https://www.mdpi.com/2078-2489/11/2/125)**)
- [doubleZ0108/Data-Augmentation](https://github.com/doubleZ0108/Data-Augmentation) 
: General Data Augmentation Algorithms for Object Detection(esp. Yolo).
## Other Versions of YOLO
- ### PyTorch Implementation
- [ultralytics/yolov3](https://github.com/ultralytics/yolov3) 
: YOLOv3 in PyTorch > ONNX > CoreML > TFLite.
- [eriklindernoren/PyTorch-YOLOv3](https://github.com/eriklindernoren/PyTorch-YOLOv3) 
: Minimal PyTorch implementation of YOLOv3.
- [Tianxiaomo/pytorch-YOLOv4](https://github.com/Tianxiaomo/pytorch-YOLOv4) 
: PyTorch ,ONNX and TensorRT implementation of YOLOv4.
- [ayooshkathuria/pytorch-yolo-v3](https://github.com/ayooshkathuria/pytorch-yolo-v3) 
: A PyTorch implementation of the YOLO v3 object detection algorithm.
- [WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4) : PyTorch implementation of YOLOv4.
- [argusswift/YOLOv4-pytorch](https://github.com/argusswift/YOLOv4-pytorch) 
: This is a pytorch repository of YOLOv4, attentive YOLOv4 and mobilenet YOLOv4 with PASCAL VOC and COCO.
- [longcw/yolo2-pytorch](https://github.com/longcw/yolo2-pytorch) 
: YOLOv2 in PyTorch.
- [bubbliiiing/yolov5-v6.1-pytorch](https://github.com/bubbliiiing/yolov5-v6.1-pytorch) 
: 这是一个yolov5-v6.1-pytorch的源码，可以用于训练自己的模型。
- [bubbliiiing/yolov5-pytorch](https://github.com/bubbliiiing/yolov5-pytorch) 
: 这是一个YoloV5-pytorch的源码，可以用于训练自己的模型。
- [bubbliiiing/yolov4-pytorch](https://github.com/bubbliiiing/yolov4-pytorch) 
: 这是一个YoloV4-pytorch的源码，可以用于训练自己的模型。
- [bubbliiiing/yolov4-tiny-pytorch](https://github.com/bubbliiiing/yolov4-tiny-pytorch) 
: 这是一个YoloV4-tiny-pytorch的源码，可以用于训练自己的模型。
- [bubbliiiing/yolov3-pytorch](https://github.com/bubbliiiing/yolo3-pytorch) 
: 这是一个yolo3-pytorch的源码，可以用于训练自己的模型。
- [bubbliiiing/yolox-pytorch](https://github.com/bubbliiiing/yolox-pytorch) 
: 这是一个yolox-pytorch的源码，可以用于训练自己的模型。
- [bubbliiiing/yolov7-pytorch](https://github.com/bubbliiiing/yolov7-pytorch) 
: 这是一个yolov7的库，可以用于训练自己的数据集。
- [bubbliiiing/yolov8-pytorch](https://github.com/bubbliiiing/yolov8-pytorch) 
: 这是一个yolov8-pytorch的仓库，可以用于训练自己的数据集。
- [BobLiu20/YOLOv3_PyTorch](https://github.com/BobLiu20/YOLOv3_PyTorch) 
: Full implementation of YOLOv3 in PyTorch.
- [ruiminshen/yolo2-pytorch](https://github.com/ruiminshen/yolo2-pytorch) 
: PyTorch implementation of the YOLO (You Only Look Once) v2.
- [DeNA/PyTorch_YOLOv3](https://github.com/DeNA/PyTorch_YOLOv3) 
: Implementation of YOLOv3 in PyTorch.
- [abeardear/pytorch-YOLO-v1](https://github.com/abeardear/pytorch-YOLO-v1) 
: an experiment for yolo-v1, including training and testing.
- [wuzhihao7788/yolodet-pytorch](https://github.com/wuzhihao7788/yolodet-pytorch) 
: reproduce the YOLO series of papers in pytorch, including YOLOv4, PP-YOLO, YOLOv5，YOLOv3, etc.
- [uvipen/Yolo-v2-pytorch](https://github.com/uvipen/Yolo-v2-pytorch) 
: YOLO for object detection tasks.
- [Peterisfar/YOLOV3](https://github.com/Peterisfar/YOLOV3) 
: yolov3 by pytorch.
- [misads/easy_detection](https://github.com/misads/easy_detection) 
: 一个简单方便的目标检测框架(PyTorch环境可直接运行，不需要cuda编译)，支持Faster_RCNN、Yolo系列(v2~v5)、EfficientDet、RetinaNet、Cascade-RCNN等经典网络。
- [miemiedetection](https://github.com/miemie2013/miemiedetection) 
: Pytorch and ncnn implementation of PPYOLOE、YOLOX、PPYOLO、PPYOLOv2、SOLOv2 an so on.
- [pjh5672/YOLOv1](https://github.com/pjh5672/YOLOv1) 
: YOLOv1 implementation using PyTorch.
- [pjh5672/YOLOv2](https://github.com/pjh5672/YOLOv2) 
: YOLOv2 implementation using PyTorch.
- [pjh5672/YOLOv3](https://github.com/pjh5672/YOLOv3) 
: YOLOv3 implementation using PyTorch.
- [Iywie/pl_YOLO](https://github.com/Iywie/pl_YOLO) 
: YOLOv7, YOLOX and YOLOv5 are working right now.
- [DavidLandup0/deepvision](https://github.com/DavidLandup0/deepvision) 
: PyTorch and TensorFlow/Keras image models with automatic weight conversions and equal API/implementations - Vision Transformer (ViT), ResNetV2, EfficientNetV2, (planned...) DeepLabV3+, ConvNeXtV2, YOLO, NeRF, etc.
- [theos-ai/easy-yolov7](https://github.com/theos-ai/easy-yolov7) 
: This a clean and easy-to-use implementation of YOLOv7 in PyTorch, made with ❤️ by Theos AI.
- ### C Implementation
- [ggml](https://github.com/ggerganov/ggml) 
: Tensor library for machine learning. Written in C.
- [rockcarry/ffcnn](https://github.com/rockcarry/ffcnn) 
: ffcnn is a cnn neural network inference framework, written in 600 lines C language.
- [ar7775/Object-Detection-System-Yolo](https://github.com/ar7775/Object-Detection-System-Yolo) 
: Object Detection System.
- [lstuma/YOLO_utils](https://github.com/lstuma/YOLO_utils) 
: A few utilities for the YOLO project implemented in C for extra speed.
- [RajneeshKumar12/yolo-detection-app](https://github.com/RajneeshKumar12/yolo-detection-app) 
: Yolo app for object detection.
- [Deyht/CIANNA](https://github.com/Deyht/CIANNA) 
: CIANNA - Convolutional Interactive Artificial Neural Networks by/for Astrophysicists.
- ### CPP Implementation
- [walktree/libtorch-yolov3](https://github.com/walktree/libtorch-yolov3) 
: A Libtorch implementation of the YOLO v3 object detection algorithm, written with pure C++.
- [yasenh/libtorch-yolov5](https://github.com/yasenh/libtorch-yolov5) 
: A LibTorch inference implementation of the yolov5.
- [Nebula4869/YOLOv5-LibTorch](https://github.com/Nebula4869/YOLOv5-LibTorch) 
: Real time object detection with deployment of YOLOv5 through LibTorch C++ API.
- [ncdhz/YoloV5-LibTorch](https://github.com/ncdhz/YoloV5-LibTorch) 
: 一个 C++ 版本的 YoloV5 封装库.
- [Rane2021/yolov5_train_cpp_inference](https://github.com/Rane2021/yolov5_train_cpp_inference) 
: yolov5训练和c++推理代码，效果出色。
- [stephanecharette/DarkHelp](https://github.com/stephanecharette/DarkHelp) 
: The DarkHelp C++ API is a wrapper to make it easier to use the Darknet neural network framework within a C++ application.
- [UNeedCryDear/yolov5-opencv-dnn-cpp](https://github.com/UNeedCryDear/yolov5-opencv-dnn-cpp) 
: 使用opencv模块部署yolov5-6.0版本。
- [UNeedCryDear/yolov5-seg-opencv-onnxruntime-cpp](https://github.com/UNeedCryDear/yolov5-seg-opencv-onnxruntime-cpp) 
: yolov5 segmentation with onnxruntime and opencv.
- [hpc203/yolov5-dnn-cpp-python](https://github.com/hpc203/yolov5-dnn-cpp-python) 
: 用opencv的dnn模块做yolov5目标检测，包含C++和Python两个版本的程序。
- [hpc203/yolox-opencv-dnn](https://github.com/hpc203/yolox-opencv-dnn) 
: 使用OpenCV部署YOLOX，支持YOLOX-S、YOLOX-M、YOLOX-L、YOLOX-X、YOLOX-Darknet53五种结构，包含C++和Python两种版本的程序。
- [hpc203/yolov7-opencv-onnxrun-cpp-py](https://github.com/hpc203/yolov7-opencv-onnxrun-cpp-py) 
: 分别使用OpenCV、ONNXRuntime部署YOLOV7目标检测，一共包含12个onnx模型，依然是包含C++和Python两个版本的程序。
- [doleron/yolov5-opencv-cpp-python](https://github.com/doleron/yolov5-opencv-cpp-python) 
: Example of using ultralytics YOLO V5 with OpenCV 4.5.4, C++ and Python.
- [UNeedCryDear/yolov8-opencv-onnxruntime-cpp](https://github.com/UNeedCryDear/yolov8-opencv-onnxruntime-cpp) 
: detection and instance segmentation of yolov8,use onnxruntime and opencv.
- ### ROS Implementation
- [mgonzs13/yolov8_ros](https://github.com/mgonzs13/yolov8_ros) 
: Ultralytics YOLOv8, YOLOv9, YOLOv10, YOLOv11 for ROS 2.
- [leggedrobotics/darknet_ros](https://github.com/leggedrobotics/darknet_ros) 
: Real-Time Object Detection for ROS.
- [engcang/ros-yolo-sort](https://github.com/engcang/ros-yolo-sort) 
: YOLO and SORT, and ROS versions of them.
- [chrisgundling/YoloLight](https://github.com/chrisgundling/YoloLight) 
: Tiny-YOLO-v2 ROS Node for Traffic Light Detection.
- [Ar-Ray-code/YOLOX-ROS](https://github.com/Ar-Ray-code/YOLOX-ROS) 
: YOLOX + ROS2 object detection package.
- [Ar-Ray-code/YOLOv5-ROS](https://github.com/Ar-Ray-code/YOLOv5-ROS) 
: YOLOv5 + ROS2 object detection package.
- [Tossy0423/yolov4-for-darknet_ros](https://github.com/Tossy0423/yolov4-for-darknet_ros) 
: This is the environment in which YOLO V4 is ported to darknet_ros.
- [qianmin/yolov5_ROS](https://github.com/qianmin/yolov5_ROS) 
: run YOLOv5 in ROS，ROS使用YOLOv5。
- [ailllist/yolov5_ROS](https://github.com/ailllist/yolov5_ROS) 
: yolov5 for ros, not webcam.
- [Shua-Kang/ros_pytorch_yolov5](https://github.com/Shua-Kang/ros_pytorch_yolov5) 
: A ROS wrapper for yolov5. (master branch is v5.0 of yolov5; for v6.1, see branch v6.1).
- [ziyan0302/Yolov5_DeepSort_Pytorch_ros](https://github.com/ziyan0302/Yolov5_DeepSort_Pytorch_ros) 
: Connect Yolov5 detection module and DeepSort tracking module via ROS.
- [U07157135/ROS2-with-YOLOv5](https://github.com/U07157135/ROS2-with-YOLOv5) 
: 在無人機上以ROS2技術實現YOLOv5物件偵測。
- [lukazso/yolov6-ros](https://github.com/lukazso/yolov6-ros) 
: ROS package for YOLOv6.
- [qq44642754a/Yolov5_ros](https://github.com/qq44642754a/Yolov5_ros) 
: Real-time object detection with ROS, based on YOLOv5 and PyTorch (基于 YOLOv5的ROS实时对象检测).
- [lukazso/yolov7-ros](https://github.com/lukazso/yolov7-ros) 
: ROS package for official YOLOv7.
- [phuoc101/yolov7_ros](https://github.com/phuoc101/yolov7_ros) 
: ROS package for official YOLOv7.
- [ConfusionTechnologies/ros-yolov5-node](https://github.com/ConfusionTechnologies/ros-yolov5-node) 
: For ROS2, uses ONNX GPU Runtime to inference YOLOv5.
- [Ar-Ray-code/darknet_ros_fp16](https://github.com/Ar-Ray-code/darknet_ros_fp16) 
: darknet + ROS2 Humble + OpenCV4 + CUDA 11（cuDNN, Jetson Orin）.
- [wk123467/yolov5s_trt_ros](https://github.com/wk123467/yolov5s_trt_ros) 
: 利用TensorRT对yolov5s进行加速，并将其应用于ROS，实现交通标志、红绿灯(直接输出路灯状态)、行人和车辆等交通场景的检测。
- [PardisTaghavi/yolov7_strongsort_ros](https://github.com/PardisTaghavi/yolov7_strongsort_ros) 
: Integration of "Yolov7 StrongSort" with ROS for real time object tracking.
- [af-doom/yolov8_ros_tensorrt-](https://github.com/af-doom/yolov8_ros_tensorrt-) 
: This is a YOLOv8 project based on ROS implementation, where YOLOv8 uses Tensorrt acceleration.
- [KoKoMier/ros_darknet_yolov4](https://github.com/KoKoMier/ros_darknet_yolov4) 
: 这是机器人小组视觉与雷达的结合程序，首先通过yolo目标检测识别到物体，然后把识别到的数据发送给ros里面程序，用于雷达数据结合。
- [YellowAndGreen/Yolov5-OpenCV-Cpp-Python-ROS](https://github.com/YellowAndGreen/Yolov5-OpenCV-Cpp-Python-ROS) 
: Inference with YOLOv5, OpenCV 4.5.4 DNN, C++, ROS and Python.
- [mgonzs13/yolov8_ros](https://github.com/mgonzs13/yolov8_ros) : ROS 2 wrap for Ultralytics [YOLOv8](https://github.com/ultralytics/ultralytics) to perform object detection.
- [fishros/yolov5_ros2](https://github.com/fishros/yolov5_ros2) 
: 基于YoloV5的ROS2功能包，可以快速完成物体识别与位姿发布。
- [fateshelled/EdgeYOLO-ROS](https://github.com/fateshelled/EdgeYOLO-ROS) 
: EdgeYOLO + ROS2 object detection package.
- [vivaldini/yolov6-uav](https://github.com/vivaldini/yolov6-uav) 
: This repository contains a ROS noetic package for YOLOv6 to recognize objects from UAV and provide their positions.
- [Alpaca-zip/ultralytics_ros](https://github.com/Alpaca-zip/ultralytics_ros) 
: ROS/ROS2 package for Ultralytics YOLOv8 real-time object detection.
- ### Mojo Implementation
- [taalhaataahir0102/Mojo-Yolo](https://github.com/taalhaataahir0102/Mojo-Yolo) 
: Mojo-Yolo.
- ### Rust Implementation
- [Candle](https://github.com/huggingface/candle) 
: Minimalist ML framework for Rust.
- [Tokenizers](https://github.com/huggingface/tokenizers) 
: 💥 Fast State-of-the-Art Tokenizers optimized for Research and Production. [huggingface.co/docs/tokenizers](https://huggingface.co/docs/tokenizers/index)
- [Safetensors](https://github.com/huggingface/safetensors) 
: Simple, safe way to store and distribute tensors. [huggingface.co/docs/safetensors](https://huggingface.co/docs/safetensors/index)
- [Burn](https://github.com/burn-rs/burn) 
: Burn - A Flexible and Comprehensive Deep Learning Framework in Rust. [burn-rs.github.io/](https://burn-rs.github.io/)
- [TensorFlow Rust](https://github.com/tensorflow/rust) 
: Rust language bindings for TensorFlow.
- [tch-rs](https://github.com/LaurentMazare/tch-rs) 
: Rust bindings for the C++ api of PyTorch.
- [dfdx](https://github.com/coreylowman/dfdx) 
: Deep learning in Rust, with shape checked tensors and neural networks.
- [tract](https://github.com/sonos/tract) 
: Sonos' Neural Network inference engine. Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference
- [ort](https://github.com/pykeio/ort) 
: A Rust wrapper for ONNX Runtime. [docs.rs/ort](https://docs.rs/ort/latest/ort/)
- [usls](https://github.com/jamjamjon/usls) 
: A Rust library integrated with ONNXRuntime, providing a collection of Computer Vison and Vision-Language models.
- [ptaxom/pnn](https://github.com/ptaxom/pnn) 
: pnn is [Darknet](https://github.com/alexeyAB/darknet) compatible neural nets inference engine implemented in Rust. By optimizing was achieved significant performance increment(especially in FP16 mode). pnn provide CUDNN-based and TensorRT-based inference engines.
- [bencevans/rust-opencv-yolov5](https://github.com/bencevans/rust-opencv-yolov5) 
: YOLOv5 Inference with ONNX & OpenCV in Rust.
- [masc-it/yolov5-api-rust](https://github.com/masc-it/yolov5-api-rust) 
: Rust API to run predictions with YoloV5 models.
- [AndreyGermanov/yolov8_onnx_rust](https://github.com/AndreyGermanov/yolov8_onnx_rust) 
: YOLOv8 inference using Rust.
- [igor-yusupov/rusty-yolo](https://github.com/igor-yusupov/rusty-yolo) 
: rusty-yolo.
- [gsuyemoto/yolo-rust](https://github.com/gsuyemoto/yolo-rust) 
: Run YOLO computer vision model using Rust and OpenCV and/or Torch.
- [alianse777/darknet-rust](https://github.com/alianse777/darknet-rust) 
: A Rust wrapper for Darknet, an open source neural network framework written in C and CUDA. [pjreddie.com/darknet/](https://pjreddie.com/darknet/)
- [12101111/yolo-rs](https://github.com/12101111/yolo-rs) 
: Yolov3 & Yolov4 with TVM and rust.
- [TKGgunter/yolov4_tiny_rs](https://github.com/TKGgunter/yolov4_tiny_rs) 
: A rust implementation of yolov4_tiny algorithm.
- [flixstn/You-Only-Look-Once](https://github.com/flixstn/You-Only-Look-Once) 
: A Rust implementation of Yolo for object detection and tracking.
- [lenna-project/yolo-plugin](https://github.com/lenna-project/yolo-plugin) 
: Yolo Object Detection Plugin for Lenna.
- [laclouis5/globox-rs](https://github.com/laclouis5/globox-rs) 
: Object detection toolbox for parsing, converting and evaluating bounding box annotations.
- [metobom/tchrs-opencv-webcam-inference](https://github.com/metobom/tchrs-opencv-webcam-inference) 
: This example shows steps for running a Python trained model on webcam feed with opencv and tch-rs. Model will run on GPU.
- ### Go Implementation
- [LdDl/go-darknet](https://github.com/LdDl/go-darknet) 
: go-darknet: Go bindings for Darknet (Yolo V4, Yolo V7-tiny, Yolo V3).
- [adalkiran/distributed-inference](https://github.com/adalkiran/distributed-inference) 
: Cross-language and distributed deep learning inference pipeline for WebRTC video streams over Redis Streams. Currently supports YOLOX model, which can run well on CPU.
- [wimspaargaren/yolov3](https://github.com/wimspaargaren/yolov3) 
: Go implementation of the yolo v3 object detection system.
- [wimspaargaren/yolov5](https://github.com/wimspaargaren/yolov5) 
: Go implementation of the yolo v5 object detection system.
- [genert/real_time_object_detection_go](https://github.com/genert/real_time_object_detection_go) 
: Real Time Object Detection with OpenCV, Go, and Yolo v4.
- ### CSharp Implementation
- [ML.NET](https://github.com/dotnet/machinelearning) 
: ML.NET is an open source and cross-platform machine learning framework for .NET.
- [TorchSharp](https://github.com/dotnet/TorchSharp) 
: A .NET library that provides access to the library that powers PyTorch.
- [TensorFlow.NET](https://github.com/SciSharp/TensorFlow.NET) 
: .NET Standard bindings for Google's TensorFlow for developing, training and deploying Machine Learning models in C# and F#.
- [DlibDotNet](https://github.com/takuya-takeuchi/DlibDotNet) 
: Dlib .NET wrapper written in C++ and C# for Windows, MacOS, Linux and iOS.
- [DiffSharp](https://github.com/DiffSharp/DiffSharp) 
: DiffSharp: Differentiable Functional Programming.
- [dme-compunet/YOLOv8](https://github.com/dme-compunet/YOLOv8) 
: Use YOLOv8 in real-time, for object detection, instance segmentation, pose estimation and image classification, via ONNX Runtime. [www.nuget.org/packages/YoloV8](https://www.nuget.org/packages/YoloV8)
- [techwingslab/yolov5-net](https://github.com/techwingslab/yolov5-net) 
: YOLOv5 object detection with C#, ML.NET, ONNX.
- [sstainba/Yolov8.Net](https://github.com/sstainba/Yolov8.Net) 
: A .net 6 implementation to use Yolov5 and Yolov8 models via the ONNX Runtime.
- [Alturos.Yolo](https://github.com/AlturosDestinations/Alturos.Yolo) 
: C# Yolo Darknet Wrapper (real-time object detection).
- [ivilson/Yolov7net](https://github.com/ivilson/Yolov7net) 
: Yolov7 Detector for .Net 6.
- [sangyuxiaowu/ml_yolov7](https://github.com/sangyuxiaowu/ml_yolov7) 
: ML.NET Yolov7. "微信公众号「桑榆肖物」《[YOLOv7 在 ML.NET 中使用 ONNX 检测对象](https://mp.weixin.qq.com/s/vXz6gavYJR2mh5KuJO_slA)》"
- [keijiro/TinyYOLOv2Barracuda](https://github.com/keijiro/TinyYOLOv2Barracuda) 
: Tiny YOLOv2 on Unity Barracuda.
- [derenlei/Unity_Detection2AR](https://github.com/derenlei/Unity_Detection2AR) 
: Localize 2D image object detection in 3D Scene with Yolo in Unity Barracuda and ARFoundation.
- [died/YOLO3-With-OpenCvSharp4](https://github.com/died/YOLO3-With-OpenCvSharp4) 
: Demo of implement YOLO v3 with OpenCvSharp v4 on C#.
- [mbaske/yolo-unity](https://github.com/mbaske/yolo-unity) 
: YOLO In-Game Object Detection for Unity (Windows).
- [BobLd/YOLOv4MLNet](https://github.com/BobLd/YOLOv4MLNet) 
: Use the YOLO v4 and v5 (ONNX) models for object detection in C# using ML.Net.
- [keijiro/YoloV4TinyBarracuda](https://github.com/keijiro/YoloV4TinyBarracuda) 
: YoloV4TinyBarracuda is an implementation of the YOLOv4-tiny object detection model on the Unity Barracuda neural network inference library.
- [zhang8043/YoloWrapper](https://github.com/zhang8043/YoloWrapper) 
: C#封装YOLOv4算法进行目标检测。
- [maalik0786/FastYolo](https://github.com/maalik0786/FastYolo) 
: Fast Yolo for fast initializing, object detection and tracking.
- [Uehwan/CSharp-Yolo-Video](https://github.com/Uehwan/CSharp-Yolo-Video) 
: C# Yolo for Video.
- [HTTP123-A/HumanDetection_Yolov5NET](https://github.com/https://github.com/HTTP123-A/HumanDetection_Yolov5NET) 
: YOLOv5 object detection with ML.NET, ONNX.
- [Celine-Hsieh/Hand_Gesture_Training--yolov4](https://github.com/Celine-Hsieh/Hand_Gesture_Training--yolov4) 
: Recognize the gestures' features using the YOLOv4 algorithm.
- [lin-tea/YOLOv5DetectionWithCSharp](https://github.com/lin-tea/YOLOv5DetectionWithCSharp) 
: YOLOv5s inference In C# and Training In Python.
- [MirCore/Unity-Object-Detection-and-Localization-with-VR](https://github.com/MirCore/Unity-Object-Detection-and-Localization-with-VR) 
: Detect and localize objects from the front-facing camera image of a VR Headset in a 3D Scene in Unity using Yolo and Barracuda.
- [CarlAreDHopen-eaton/YoloObjectDetection](https://github.com/CarlAreDHopen-eaton/YoloObjectDetection) 
: Yolo Object Detection Application for RTSP streams.
- [TimothyMeadows/Yolo6.NetCore](https://github.com/TimothyMeadows/Yolo6.NetCore) 
: You Only Look Once (v6) for .NET Core LTS.
- [mwetzko/EasyYoloDarknet](https://github.com/mwetzko/EasyYoloDarknet) 
: EasyYoloDarknet.
- [mwetzko/EasyYoloDarknet](https://github.com/mwetzko/EasyYoloDarknet) : Windows optimized Yolo / Darknet Compile, Train and Detect.
- [cj-mills/Unity-OpenVINO-YOLOX](https://github.com/cj-mills/Unity-OpenVINO-YOLOX) 
: This tutorial series covers how to perform object detection in the Unity game engine with the OpenVINO™ Toolkit.
- [natml-hub/YOLOX](https://github.com/natml-hub/YOLOX) 
: High performance object detector based on YOLO series.
- [thisistherealdiana/YOLO_project](https://github.com/thisistherealdiana/YOLO_project) 
: YOLO project made by Diana Kereselidze.
- [oujunke/Yolo5Net](https://github.com/oujunke/Yolo5Net) 
: Yolo5实现于TensorFlow.Net.
- [wojciechp6/YOLO-UnityBarracuda](https://github.com/wojciechp6/YOLO-UnityBarracuda) 
: Object detection app build on Unity Barracuda and YOLOv2 Tiny.
- [RaminAbbaszadi/YoloWrapper-WPF](https://github.com/RaminAbbaszadi/YoloWrapper-WPF) 
: WPF (C#) Yolo Darknet Wrapper.
- [fengyhack/YoloWpf](https://github.com/fengyhack/YoloWpf) 
: GUI demo for Object Detection with YOLO and OpenCVSharp.
- [hanzhuang111/Yolov5Wpf](https://github.com/hanzhuang111/Yolov5Wpf) 
: 使用ML.NET部署YOLOV5 的ONNX模型。
- [MaikoKingma/yolo-winforms-test](https://github.com/MaikoKingma/yolo-winforms-test) 
: A Windows forms application that can execute pre-trained object detection models via ML.NET. In this instance the You Only Look Once version 4 (yolov4) is used.
- [SeanAnd/WebcamObjectDetection](https://github.com/SeanAnd/WebcamObjectDetection) 
: YOLO object detection using webcam in winforms.
- [Devmawi/BlazorObjectDetection-Sample](https://github.com/Devmawi/BlazorObjectDetection-Sample) 
: Simple project for demonstrating how to embed a continuously object detection with Yolo on a video in a hybrid Blazor app (WebView2).
- [Soju06/yolov5-annotation-viewer](https://github.com/Soju06/yolov5-annotation-viewer) 
: yolov5 annotation viewer.
- [developer-ken/YoloPredictorMLDotNet](https://github.com/developer-ken/YoloPredictorMLDotNet) 
: YoloPredictorMLDotNet.
- [LionelC-Kyo/CSharp_YoloV5_Torch](https://github.com/LionelC-Kyo/CSharp_YoloV5_Torch) 
: Run Yolo V5 in C# By Torch.
- [wanglvhang/OnnxYoloDemo](https://github.com/wanglvhang/OnnxYoloDemo) 
: demo of using c# to run yolo onnx model with onnx runtime, and contains a windows capture tool to get bitmap from windows desktop and window.
- [BobLd/YOLOv3MLNet](https://github.com/BobLd/YOLOv3MLNet) 
: Use the YOLO v3 (ONNX) model for object detection in C# using ML.Net.
- [zgabi/Yolo.Net](https://github.com/zgabi/Yolo.Net) 
: zgabi/Yolo.Net
- [aliardan/RoadMarkingDetection](https://github.com/aliardan/RoadMarkingDetection) 
: Road markings detection using yolov5 model based on ONNX.
- [TimothyMeadows/Yolo5.NetCore](https://github.com/TimothyMeadows/Yolo5.NetCore) 
: You Only Look Once (v5) for .NET Core LTS.
- [AD-HO/YOLOv5-ML.NET](https://github.com/AD-HO/YOLOv5-ML.NET) 
: Inferencing Yolov5 ONNX model using ML.NET and ONNX Runtime.
- [ToxicSkill/YOLOV7-Webcam-inference](https://github.com/ToxicSkill/YOLOV7-Webcam-inference) 
: Simple WPF program for webcam inference with yoloV7 models.
- [aliardan/RoadMarkingDetection](https://github.com/aliardan/RoadMarkingDetection) : Road markings detection using yolov5 model based on ONNX.
- [rabbitsun2/csharp_and_microsoft_ml_and_yolo_v5_sample](https://github.com/rabbitsun2/csharp_and_microsoft_ml_and_yolo_v5_sample) 
: C#, Microsoft ML, Yolo v5, Microsoft ML.DNN, OpenCVSharp4 연계 프로젝트.
- [hsysfan/YOLOv5-Seg-OnnxRuntime](https://github.com/hsysfan/YOLOv5-Seg-OnnxRuntime) 
: YOLOv5 Segmenation Implementation in C# and OnnxRuntime.
- [dme-compunet/YOLOv8](https://github.com/dme-compunet/YOLOv8) : Use YOLOv8 in real-time, for object detection, instance segmentation, pose estimation and image classification, via ONNX Runtime.
- ### Tensorflow and Keras Implementation
- [YunYang1994/tensorflow-yolov3](https://github.com/YunYang1994/tensorflow-yolov3) 
: 🔥 TensorFlow Code for technical report: "YOLOv3: An Incremental Improvement".
- [zzh8829/yolov3-tf2](https://github.com/zzh8829/yolov3-tf2) 
: YoloV3 Implemented in Tensorflow 2.0.
- [hunglc007/tensorflow-yolov4-tflite](https://github.com/hunglc007/tensorflow-yolov4-tflite) 
: YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite.
- [gliese581gg/YOLO_tensorflow](https://github.com/gliese581gg/YOLO_tensorflow) 
: tensorflow implementation of 'YOLO : Real-Time Object Detection'.
- [llSourcell/YOLO_Object_Detection](https://github.com/llSourcell/YOLO_Object_Detection) 
: This is the code for "YOLO Object Detection" by Siraj Raval on Youtube.
- [wizyoung/YOLOv3_TensorFlow](https://github.com/wizyoung/YOLOv3_TensorFlow) 
: Complete YOLO v3 TensorFlow implementation. Support training on your own dataset.
- [theAIGuysCode/yolov4-deepsort](https://github.com/theAIGuysCode/yolov4-deepsort) 
: Object tracking implemented with YOLOv4, DeepSort, and TensorFlow.
- [mystic123/tensorflow-yolo-v3](https://github.com/mystic123/tensorflow-yolo-v3) 
: Implementation of YOLO v3 object detector in Tensorflow (TF-Slim).
- [hizhangp/yolo_tensorflow](https://github.com/hizhangp/yolo_tensorflow) 
: Tensorflow implementation of YOLO, including training and test phase.
- [nilboy/tensorflow-yolo](https://github.com/nilboy/tensorflow-yolo) 
: tensorflow implementation of 'YOLO : Real-Time Object Detection'(train and test).
- [qqwweee/keras-yolo3](https://github.com/qqwweee/keras-yolo3) 
: A Keras implementation of YOLOv3 (Tensorflow backend).
- [allanzelener/YAD2K](https://github.com/allanzelener/YAD2K) 
: YAD2K: Yet Another Darknet 2 Keras.
- [experiencor/keras-yolo2](https://github.com/experiencor/keras-yolo2) 
: YOLOv2 in Keras and Applications.
- [experiencor/keras-yolo3](https://github.com/experiencor/keras-yolo3) 
: Training and Detecting Objects with YOLO3.
- [SpikeKing/keras-yolo3-detection](https://github.com/SpikeKing/keras-yolo3-detection) 
: YOLO v3 物体检测算法。
- [xiaochus/YOLOv3](https://github.com/xiaochus/YOLOv3) 
: Keras implementation of yolo v3 object detection.
- [bubbliiiing/yolo3-keras](https://github.com/bubbliiiing/yolo3-keras) 
: 这是一个yolo3-keras的源码，可以用于训练自己的模型。
- [bubbliiiing/yolov4-keras](https://github.com/bubbliiiing/yolov4-keras) 
: 这是一个YoloV4-keras的源码，可以用于训练自己的模型。
- [bubbliiiing/yolov4-tf2](https://github.com/bubbliiiing/yolov4-tf2) 
: 这是一个yolo4-tf2（tensorflow2）的源码，可以用于训练自己的模型。
- [bubbliiiing/yolov4-tiny-tf2](https://github.com/bubbliiiing/yolov4-tiny-tf2) 
: 这是一个YoloV4-tiny-tf2的源码，可以用于训练自己的模型。
- [pythonlessons/TensorFlow-2.x-YOLOv3](https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3) 
: YOLOv3 implementation in TensorFlow 2.3.1.
- [miemie2013/Keras-YOLOv4](https://github.com/miemie2013/Keras-YOLOv4) 
: PPYOLO AND YOLOv4.
- [Ma-Dan/keras-yolo4](https://github.com/Ma-Dan/keras-yolo4) 
: A Keras implementation of YOLOv4 (Tensorflow backend).
- [miranthajayatilake/YOLOw-Keras](https://github.com/miranthajayatilake/YOLOw-Keras) 
: YOLOv2 Object Detection w/ Keras (in just 20 lines of code).
- [maiminh1996/YOLOv3-tensorflow](https://github.com/maiminh1996/YOLOv3-tensorflow) 
: Re-implement YOLOv3 with TensorFlow.
- [Stick-To/Object-Detection-Tensorflow](https://github.com/Stick-To/Object-Detection-Tensorflow) 
: Object Detection API Tensorflow.
- [avBuffer/Yolov5_tf](https://github.com/avBuffer/Yolov5_tf) 
: Yolov5/Yolov4/ Yolov3/ Yolo_tiny in tensorflow.
- [ruiminshen/yolo-tf](https://github.com/ruiminshen/yolo-tf) 
: TensorFlow implementation of the YOLO (You Only Look Once).
- [xiao9616/yolo4_tensorflow2](https://github.com/xiao9616/yolo4_tensorflow2) 
: yolo 4th edition implemented by tensorflow2.0.
- [sicara/tf2-yolov4](https://github.com/sicara/tf2-yolov4) 
: A TensorFlow 2.0 implementation of YOLOv4: Optimal Speed and Accuracy of Object Detection.
- [LongxingTan/Yolov5](https://github.com/LongxingTan/Yolov5) 
: Efficient implementation of YOLOV5 in TensorFlow2.
- [geekjr/quickai](https://github.com/geekjr/quickai) 
: QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.
- [CV_Lab/yolov5_rt_tfjs](https://gitee.com/CV_Lab/yolov5_rt_tfjs) : 🚀 基于TensorFlow.js的YOLOv5实时目标检测项目。
- [Burf/TFDetection](https://github.com/Burf/TFDetection) 
: A Detection Toolbox for Tensorflow2.
- [taipingeric/yolo-v4-tf.keras](https://github.com/taipingeric/yolo-v4-tf.keras) 
: A simple tf.keras implementation of YOLO v4.
- [david8862/keras-YOLOv3-model-set](https://github.com/david8862/keras-YOLOv3-model-set) 
: end-to-end YOLOv4/v3/v2 object detection pipeline, implemented on tf.keras with different technologies.
- ### PaddlePaddle Implementation
- [PaddlePaddle/PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) : Object Detection toolkit based on PaddlePaddle. "PP-YOLO: An Effective and Efficient Implementation of Object Detector". (**[arXiv 2020](https://arxiv.org/abs/2007.12099)**)
- [nemonameless/PaddleDetection_YOLOv5](https://github.com/nemonameless/PaddleDetection_YOLOv5) 
: YOLOv5 of PaddleDetection, Paddle implementation of YOLOv5.
- [nemonameless/PaddleDetection_YOLOX](https://github.com/nemonameless/PaddleDetection_YOLOX) 
: Paddle YOLOX, 51.8% on COCO val by YOLOX-x, 44.6% on YOLOX-ConvNeXt-s.
- [nemonameless/PaddleDetection_YOLOset](https://github.com/nemonameless/PaddleDetection_YOLOset) 
: Paddle YOLO set: YOLOv3, PPYOLO, PPYOLOE, YOLOX, YOLOv5, YOLOv7 and so on.
- [miemie2013/Paddle-YOLOv4](https://github.com/miemie2013/Paddle-YOLOv4) 
: Paddle-YOLOv4.
- [Sharpiless/PaddleDetection-Yolov5](https://github.com/Sharpiless/PaddleDetection-Yolov5) 
: 基于Paddlepaddle复现yolov5，支持PaddleDetection接口。
- [Nioolek/PPYOLOE_pytorch](https://github.com/Nioolek/PPYOLOE_pytorch) 
: An unofficial implementation of Pytorch version PP-YOLOE,based on Megvii YOLOX training code.
- ### Caffe Implementation
- [ChenYingpeng/caffe-yolov3](https://github.com/ChenYingpeng/caffe-yolov3) 
: A real-time object detection framework of Yolov3/v4 based on caffe.
- [ChenYingpeng/darknet2caffe](https://github.com/ChenYingpeng/darknet2caffe) 
: Convert darknet weights to caffemodel.
- [eric612/Caffe-YOLOv3-Windows](https://github.com/eric612/Caffe-YOLOv3-Windows) 
: A windows caffe implementation of YOLO detection network.
- [Harick1/caffe-yolo](https://github.com/Harick1/caffe-yolo) 
: Caffe for YOLO.
- [choasup/caffe-yolo9000](https://github.com/choasup/caffe-yolo9000) 
: Caffe for YOLOv2 & YOLO9000.
- [gklz1982/caffe-yolov2](https://github.com/gklz1982/caffe-yolov2) 
: caffe-yolov2.
- ### MXNet Implementation
- [Gluon CV Toolkit](https://github.com/dmlc/gluon-cv) 
: GluonCV provides implementations of the state-of-the-art (SOTA) deep learning models in computer vision.
- [zhreshold/mxnet-yolo](https://github.com/zhreshold/mxnet-yolo) 
: YOLO: You only look once real-time object detector.
- ### Web Implementation
- [ModelDepot/tfjs-yolo-tiny](https://github.com/ModelDepot/tfjs-yolo-tiny) 
: In-Browser Object Detection using Tiny YOLO on Tensorflow.js.
- [justadudewhohacks/tfjs-tiny-yolov2](https://github.com/justadudewhohacks/tfjs-tiny-yolov2) 
: Tiny YOLO v2 object detection with tensorflow.js.
- [reu2018DL/YOLO-LITE](https://github.com/reu2018DL/YOLO-LITE) 
: YOLO-LITE is a web implementation of YOLOv2-tiny.
- [mobimeo/node-yolo](https://github.com/mobimeo/node-yolo) 
: Node bindings for YOLO/Darknet image recognition library.
- [Sharpiless/Yolov5-Flask-VUE](https://github.com/Sharpiless/Yolov5-Flask-VUE) 
: 基于Flask开发后端、VUE开发前端框架，在WEB端部署YOLOv5目标检测模型。
- [shaqian/tfjs-yolo](https://github.com/shaqian/tfjs-yolo) 
: YOLO v3 and Tiny YOLO v1, v2, v3 with Tensorflow.js.
- [zqingr/tfjs-yolov3](https://github.com/zqingr/tfjs-yolov3) 
: A Tensorflow js implementation of YOLOv3 and YOLOv3-tiny.
- [bennetthardwick/darknet.js](https://github.com/bennetthardwick/darknet.js) 
: A NodeJS wrapper of pjreddie's darknet / yolo.
- [nihui/ncnn-webassembly-yolov5](https://github.com/nihui/ncnn-webassembly-yolov5) 
: Deploy YOLOv5 in your web browser with ncnn and webassembly.
- [muhk01/Yolov5-on-Flask](https://github.com/muhk01/Yolov5-on-Flask) 
: Running YOLOv5 through web browser using Flask microframework.
- [tcyfree/yolov5](https://github.com/tcyfree/yolov5) 
: 基于Flask开发后端、VUE开发前端框架，在WEB端部署YOLOv5目标检测模型。
- [siffyy/YOLOv5-Web-App-for-Vehicle-Detection](https://github.com/siffyy/YOLOv5-Web-App-for-Vehicle-Detection) 
: Repo for Web Application for Vehicle detection from Satellite Imagery using YOLOv5 model.
- [Devmawi/BlazorObjectDetection-Sample](https://github.com/Devmawi/BlazorObjectDetection-Sample) : A sample for demonstrating online execution of an onnx model by a Blazor app.
- [Hyuto/yolov5-onnxruntime-web](https://github.com/Hyuto/yolov5-onnxruntime-web) 
: YOLOv5 right in your browser with onnxruntime-web.
- ### Others
- [jinfagang/yolov7_d2](https://github.com/jinfagang/yolov7_d2) 
: 🔥🔥🔥🔥 (Earlier YOLOv7 not official one) YOLO with Transformers and Instance Segmentation, with TensorRT acceleration! 🔥🔥🔥
- [yang-0201/YOLOv6_pro](https://github.com/yang-0201/YOLOv6_pro) 
: Make it easier for yolov6 to change the network structure.
- [j-marple-dev/AYolov2](https://github.com/j-marple-dev/AYolov2) 
: The main goal of this repository is to rewrite the object detection pipeline with a better code structure for better portability and adaptability to apply new experimental methods. The object detection pipeline is based on [Ultralytics YOLOv5](https://github.com/ultralytics/yolov5).
- [fcakyon/yolov5-pip](https://github.com/fcakyon/yolov5-pip) 
: Packaged version of ultralytics/yolov5.
- [kadirnar/yolov6-pip](https://github.com/kadirnar/yolov6-pip) 
: Packaged version of yolov6 model.
- [kadirnar/yolov7-pip](https://github.com/kadirnar/yolov7-pip) 
: Packaged version of yolov7 model.
- [kadirnar/torchyolo](https://github.com/kadirnar/torchyolo) 
: PyTorch implementation of YOLOv5, YOLOv6, YOLOv7, YOLOX.
- [CvPytorch](https://github.com/shanglianlm0525/CvPytorch) 
: CvPytorch is an open source COMPUTER VISION toolbox based on PyTorch.
- [Holocron](https://github.com/frgfm/Holocron) 
: PyTorch implementations of recent Computer Vision tricks (ReXNet, RepVGG, Unet3p, YOLOv4, CIoU loss, AdaBelief, PolyLoss).
- [DL-Practise/YoloAll](https://github.com/DL-Practise/YoloAll) 
: YoloAll is a collection of yolo all versions. you you use YoloAll to test yolov3/yolov5/yolox/yolo_fastest.
- [msnh2012/Msnhnet](https://github.com/msnh2012/Msnhnet) 
: (yolov3 yolov4 yolov5 unet ...)A mini pytorch inference framework which inspired from darknet.
- [xinghanliuying/yolov5-trick](https://github.com/xinghanliuying/yolov5-trick) 
: 基于yolov5的改进库。
- [BMW-InnovationLab/BMW-YOLOv4-Training-Automation](https://github.com/BMW-InnovationLab/BMW-YOLOv4-Training-Automation) 
: YOLOv4-v3 Training Automation API for Linux.
- [AntonMu/TrainYourOwnYOLO](https://github.com/AntonMu/TrainYourOwnYOLO) 
: Train a state-of-the-art yolov3 object detector from scratch!
- [madhawav/YOLO3-4-Py](https://github.com/madhawav/YOLO3-4-Py) 
: A Python wrapper on Darknet. Compatible with YOLO V3.
- [theAIGuysCode/yolov4-custom-functions](https://github.com/theAIGuysCode/yolov4-custom-functions) 
: A Wide Range of Custom Functions for YOLOv4, YOLOv4-tiny, YOLOv3, and YOLOv3-tiny Implemented in TensorFlow, TFLite, and TensorRT.
- [tiquasar/FLAITER](https://github.com/tiquasar/FLAITER) 
: Machine Learning and AI Mobile Application.
- [kadirnar/Minimal-Yolov6](https://github.com/kadirnar/Minimal-Yolov6) 
: Minimal-Yolov6.
- [DataXujing/YOLOv6](https://github.com/DataXujing/YOLOv6) 
: 🌀 🌀 手摸手 美团 YOLOv6模型训练和TensorRT端到端部署方案教程。
- [DataXujing/YOLOv7](https://github.com/DataXujing/YOLOv7) 
: 🔥🔥🔥 Official YOLOv7训练自己的数据集并实现端到端的TensorRT模型加速推断。
- [DataXujing/YOLOv8](https://github.com/DataXujing/YOLOv8) 
: 🔥 Official YOLOv8模型训练和部署。Official YOLOv8 训练自己的数据集并基于NVIDIA TensorRT和华为昇腾端到端模型加速以及安卓手机端部署。
- [DataXujing/YOLOv9](https://github.com/DataXujing/YOLOv9) 
: 🔥 YOLOv9 paper解析，训练自己的数据集，TensorRT端到端部署， NCNN安卓手机部署。
- [Code-keys/yolov5-darknet](https://github.com/Code-keys/yolov5-darknet) 
: yolov5-darknet support yaml && cfg.
- [Code-keys/yolo-darknet](https://github.com/Code-keys/yolo-darknet) 
: YOLO-family complemented by darknet. yolov5 yolov7 et al ...
- [pooya-mohammadi/deep_utils](https://github.com/pooya-mohammadi/deep_utils) 
: A toolkit full of handy functions including most used models and utilities for deep-learning practitioners!
- [yl-jiang/YOLOSeries](https://github.com/yl-jiang/YOLOSeries) 
: YOLO Series.
- [yjh0410/FreeYOLO](https://github.com/yjh0410/FreeYOLO) 
: FreeYOLO is inspired by many other excellent works, such as YOLOv7 and YOLOX.
- [open-yolo/yolov7](https://github.com/open-yolo/yolov7) 
: Improved and packaged version of WongKinYiu/yolov7.
- [iloveai8086/YOLOC](https://github.com/iloveai8086/YOLOC) 
: 🚀YOLOC is Combining different modules to build an different Object detection model.
- [miemie2013/miemiedetection](https://github.com/miemie2013/miemiedetection) : Pytorch and ncnn implementation of PPYOLOE、YOLOX、PPYOLO、PPYOLOv2、SOLOv2 an so on.
- [RyanCCC/YOLOSeries](https://github.com/RyanCCC/YOLOSeries) 
: YOLO算法的实现。
- [HuKai97/YOLOX-Annotations](https://github.com/HuKai97/YOLOX-Annotations) 
: 一个YOLOX的中文注释版本，供大家参考学习！
- [isLinXu/YOLOv8_Efficient](https://github.com/isLinXu/YOLOv8_Efficient) 
: 🚀Simple and efficient use for Ultralytics yolov8🚀
- [z1069614715/objectdetection_script](https://github.com/z1069614715/objectdetection_script) 
: 一些关于目标检测的脚本的改进思路代码。
## Lighter and Deployment Frameworks
- ### Lightweight Backbones and FPN
#### 轻量级骨干网络和特征金字塔网络
- [murufeng/awesome_lightweight_networks](https://github.com/murufeng/awesome_lightweight_networks) 
: The implementation of various lightweight networks by using PyTorch. such as：MobileNetV2，MobileNeXt，GhostNet，ParNet，MobileViT、AdderNet，ShuffleNetV1-V2，LCNet，ConvNeXt，etc. ⭐⭐⭐⭐⭐
- [Bobo-y/flexible-yolov5](https://github.com/Bobo-y/flexible-yolov5) 
: More readable and flexible yolov5 with more backbone(resnet, shufflenet, moblienet, efficientnet, hrnet, swin-transformer) and (cbam，dcn and so on), and tensorrt.
- [XingZeng307/YOLOv5_with_BiFPN](https://github.com/XingZeng307/YOLOv5_with_BiFPN) 
: This repo is mainly for replacing PANet with BiFPN in YOLOv5.
- [dog-qiuqiu/MobileNet-Yolo](https://github.com/dog-qiuqiu/MobileNet-Yolo) 
: MobileNetV2-YoloV3-Nano: 0.5BFlops 3MB HUAWEI P40: 6ms/img, YoloFace-500k:0.1Bflops 420KB🔥🔥🔥.
- [eric612/MobileNet-YOLO](https://github.com/eric612/MobileNet-YOLO) 
: A caffe implementation of MobileNet-YOLO detection network.
- [eric612/Mobilenet-YOLO-Pytorch](https://github.com/eric612/Mobilenet-YOLO-Pytorch) 
: Include mobilenet series (v1,v2,v3...) and yolo series (yolov3,yolov4,...) .
- [Adamdad/keras-YOLOv3-mobilenet](https://github.com/Adamdad/keras-YOLOv3-mobilenet) 
: A Keras implementation of YOLOv3 (Tensorflow backend) inspired by [allanzelener/YAD2K](https://github.com/allanzelener/YAD2K).
- [fsx950223/mobilenetv2-yolov3](https://github.com/fsx950223/mobilenetv2-yolov3) 
: yolov3 with mobilenetv2 and efficientnet.
- [liux0614/yolo_nano](https://github.com/liux0614/yolo_nano) 
: Unofficial implementation of yolo nano.
- [lingtengqiu/Yolo_Nano](https://github.com/lingtengqiu/Yolo_Nano) 
: Pytorch implementation of yolo_Nano for pedestrian detection.
- [bubbliiiing/mobilenet-yolov4-pytorch](https://github.com/bubbliiiing/mobilenet-yolov4-pytorch) 
: 这是一个mobilenet-yolov4的库，把yolov4主干网络修改成了mobilenet，修改了Panet的卷积组成，使参数量大幅度缩小。
- [bubbliiiing/efficientnet-yolo3-pytorch](https://github.com/bubbliiiing/efficientnet-yolo3-pytorch) 
: 这是一个efficientnet-yolo3-pytorch的源码，将yolov3的主干特征提取网络修改成了efficientnet。
- [HuKai97/YOLOv5-ShuffleNetv2](https://github.com/HuKai97/YOLOv5-ShuffleNetv2) 
: YOLOv5的轻量化改进(蜂巢检测项目)。
- [YOLO-ReT](https://github.com/guotao0628/yoloret) 
: "YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUs". (**[WACV 2022](https://openaccess.thecvf.com/content/WACV2022/html/Ganesh_YOLO-ReT_Towards_High_Accuracy_Real-Time_Object_Detection_on_Edge_GPUs_WACV_2022_paper.html)**)
- ### Pruning Knoweldge-Distillation Quantization
- ##### Pruning
###### 剪枝
- [Torch-Pruning](https://github.com/VainF/Torch-Pruning) 
: Towards Any Structural Pruning; LLMs / SAM / Diffusion / Transformers / YOLOv8 / CNNs. "Towards Any Structural Pruning". (**[CVPR 2023](https://openaccess.thecvf.com/content/CVPR2023/html/Fang_DepGraph_Towards_Any_Structural_Pruning_CVPR_2023_paper.html)**)
- [SparseML](https://github.com/neuralmagic/sparseml) 
: Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models. "Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks". (**[PMLR 2020](http://proceedings.mlr.press/v119/kurtz20a.html)**). "Woodfisher: Efficient second-order approximation for neural network compression". (**[NeurIPS 2020](https://proceedings.neurips.cc/paper/2020/hash/d1ff1ec86b62cd5f3903ff19c3a326b2-Abstract.html)**)
- [SparseZoo](https://github.com/neuralmagic/sparsezoo) 
: Neural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes.
- [Gumpest/YOLOv5-Multibackbone-Compression](https://github.com/Gumpest/YOLOv5-Multibackbone-Compression) 
: YOLOv5 Series Multi-backbone(TPH-YOLOv5, Ghostnet, ShuffleNetv2, Mobilenetv3Small, EfficientNetLite, PP-LCNet, SwinTransformer YOLO), Module(CBAM, DCN), Pruning (EagleEye, Network Slimming) and Quantization (MQBench) Compression Tool Box.
- [SlimYOLOv3](https://github.com/PengyiZhang/SlimYOLOv3) 
: "SlimYOLOv3: Narrower, Faster and Better for UAV Real-Time Applications". (**[arXiv 2019](https://arxiv.org/abs/1907.11093)**)
- [uyzhang/yolov5_prune](https://github.com/uyzhang/yolov5_prune) 
: Yolov5 pruning on COCO Dataset.
- [midasklr/yolov5prune](https://github.com/midasklr/yolov5prune) 
: yolov5模型剪枝。
- [ZJU-lishuang/yolov5_prune](https://github.com/ZJU-lishuang/yolov5_prune) 
: yolov5 prune，Support V2, V3, V4 and V6 versions of yolov5.
- [sbbug/yolov5-prune-multi](https://github.com/sbbug/yolov5-prune-multi) 
: yolov5-prune-multi 无人机视角、多模态、模型剪枝、国产AI芯片部署。
- [Syencil/mobile-yolov5-pruning-distillation](https://github.com/Syencil/mobile-yolov5-pruning-distillation) 
: mobilev2-yolov5s剪枝、蒸馏，支持ncnn，tensorRT部署。ultra-light but better performence！
- [Lam1360/YOLOv3-model-pruning](https://github.com/Lam1360/YOLOv3-model-pruning) 
: 在 oxford hand 数据集上对 YOLOv3 做模型剪枝（network slimming）。
- [tanluren/yolov3-channel-and-layer-pruning](https://github.com/tanluren/yolov3-channel-and-layer-pruning) 
: yolov3 yolov4 channel and layer pruning, Knowledge Distillation 层剪枝，通道剪枝，知识蒸馏。
- [coldlarry/YOLOv3-complete-pruning](https://github.com/coldlarry/YOLOv3-complete-pruning) 
: 提供对YOLOv3及Tiny的多种剪枝版本，以适应不同的需求。
- [SpursLipu/YOLOv3v4-ModelCompression-MultidatasetTraining-Multibackbone](https://github.com/SpursLipu/YOLOv3v4-ModelCompression-MultidatasetTraining-Multibackbone) 
: YOLO ModelCompression MultidatasetTraining.
- [talebolano/yolov3-network-slimming](https://github.com/talebolano/yolov3-network-slimming) 
: yolov3 network slimming剪枝的一种实现。
- [Bigtuo/YOLOX-Lite](https://github.com/Bigtuo/YOLOX-Lite) 
: 将YOLOv5-Lite代码中的head更换为YOLOX head。
- [YINYIPENG-EN/Pruning_for_YOLOV5_pytorch](https://github.com/YINYIPENG-EN/Pruning_for_YOLOV5_pytorch) 
: Pruning_for_YOLOV5_pytorch.
- [chumingqian/Model_Compression_For_YOLOV3-V4](https://github.com/chumingqian/Model_Compression_For_YOLOV3-V4) 
: In this repository using the dynamic sparse training( variable sparse rate s which can speed up the sparse training process), channel pruning and knowledge distilling for YOLOV3 and YOLOV4.
- [xhwNobody/yolov5_prune_sfp](https://github.com/xhwNobody/yolov5_prune_sfp) 
: 基于SFP和FPGM的yolov5的软剪枝实现。
- ##### Quantization
###### 量化
- [dog-qiuqiu/FastestDet](https://github.com/dog-qiuqiu/FastestDet) 
: ⚡ A newly designed ultra lightweight anchor free target detection algorithm， weight only 250K parameters， reduces the time consumption by 10% compared with yolo-fastest, and the post-processing is simpler. "知乎「马雪浩」《[FastestDet: 比yolo-fastest更快！更强！更简单！全新设计的超实时Anchor-free目标检测算法](https://zhuanlan.zhihu.com/p/536500269)》"。 "微信公众号「计算机视觉研究院」《[FastestDet：比yolov5更快！更强！全新设计的超实时Anchor-free目标检测算法（附源代码下载）](https://mp.weixin.qq.com/s/Bskc5WQd8ujy16Jl4qekjQ)》"。
- [dog-qiuqiu/Yolo-Fastest](https://github.com/dog-qiuqiu/Yolo-Fastest) 
: Yolo-Fastest：超超超快的开源ARM实时目标检测算法。 [Zenodo 2021](http://doi.org/10.5281/zenodo.5131532). "知乎「马雪浩」《[Yolo-Fastest：超超超快的开源ARM实时目标检测算法](https://zhuanlan.zhihu.com/p/234506503)》"。
- [dog-qiuqiu/Yolo-FastestV2](https://github.com/dog-qiuqiu/Yolo-FastestV2) 
: Yolo-FastestV2:更快，更轻，移动端可达300FPS，参数量仅250k。 "知乎「马雪浩」《[Yolo-FastestV2:更快，更轻，移动端可达300FPS，参数量仅250k](https://zhuanlan.zhihu.com/p/400474142)》"。
- [YOLObile](https://github.com/nightsnack/YOLObile) 
: "YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design". (**[AAAI 2021](https://www.aaai.org/AAAI21Papers/AAAI-7561.CaiY.pdf)**)
- [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim) 
: PaddleSlim is an open-source library for deep model compression and architecture search. PaddleSlim是一个专注于深度学习模型压缩的工具库，提供低比特量化、知识蒸馏、稀疏化和模型结构搜索等模型压缩策略，帮助用户快速实现模型的小型化。
- [PPL量化工具](https://github.com/openppl-public/ppq) 
: PPL Quantization Tool (PPQ) is a powerful offline neural network quantization tool. PPL QuantTool 是一个高效的工业级神经网络量化工具。
- [PINTO_model_zoo](https://github.com/PINTO0309/PINTO_model_zoo) 
: A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.
- [ppogg/YOLOv5-Lite](https://github.com/ppogg/YOLOv5-Lite) 
: 🍅🍅🍅YOLOv5-Lite: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 930+kb (int8) and 1.7M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320×320~
- [AlexeyAB/yolo2_light](https://github.com/AlexeyAB/yolo2_light) 
: Light version of convolutional neural network Yolo v3 & v2 for objects detection with a minimum of dependencies (INT8-inference, BIT1-XNOR-inference).
- ##### Knoweldge-Distillation
###### 知识蒸馏
- [torchdistill](https://github.com/yoshitomo-matsubara/torchdistill) 
: torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation. A coding-free framework built on PyTorch for reproducible deep learning studies. 🏆20 knowledge distillation methods presented at CVPR, ICLR, ECCV, NeurIPS, ICCV, etc are implemented so far. 🎁 Trained models, training logs and configurations are available for ensuring the reproducibiliy and benchmark.
- [wonbeomjang/yolov5-knowledge-distillation](https://github.com/wonbeomjang/yolov5-knowledge-distillation) 
: implementation of [Distilling Object Detectors with Fine-grained Feature Imitation](https://github.com/twangnh/Distilling-Object-Detectors) on yolov5. "Distilling Object Detectors with Fine-grained Feature Imitation". (**[CVPR 2019](https://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Distilling_Object_Detectors_With_Fine-Grained_Feature_Imitation_CVPR_2019_paper.html)**)
- [Sharpiless/Yolov5-distillation-train-inference](https://github.com/Sharpiless/Yolov5-distillation-train-inference) 
: Yolov5 distillation training | Yolov5知识蒸馏训练，支持训练自己的数据。
- [Sharpiless/yolov5-distillation-5.0](https://github.com/Sharpiless/yolov5-distillation-5.0) 
: yolov5 5.0 version distillation || yolov5 5.0版本知识蒸馏，yolov5l >> yolov5s。
- [Sharpiless/yolov5-knowledge-distillation](https://github.com/Sharpiless/yolov5-knowledge-distillation) 
: yolov5目标检测模型的知识蒸馏（基于响应的蒸馏）。
- [chengpanghu/Knowledge-Distillation-yolov5](https://github.com/chengpanghu/Knowledge-Distillation-yolov5) 
: Knowledge-Distillation-yolov5 基于yolov5的知识蒸馏。
- [magicshuang/yolov5_distillation](https://github.com/magicshuang/yolov5_distillation) 
: yolov5 知识蒸馏，yolov5-l模型压缩至yolov5-s 压缩算法是 [Distilling Object Detectors with Fine-grained Feature Imitation](https://github.com/twangnh/Distilling-Object-Detectors)。
- [Sharpiless/Yolov3-MobileNet-Distillation](https://github.com/Sharpiless/Yolov3-MobileNet-Distillation) 
: 在Yolov3-MobileNet上进行模型蒸馏训练。
- [SsisyphusTao/Object-Detection-Knowledge-Distillation](https://github.com/SsisyphusTao/Object-Detection-Knowledge-Distillation) 
: An Object Detection Knowledge Distillation framework powered by pytorch, now having SSD and yolov5.
- ### High-performance Inference Engine
#### 高性能推理引擎
- ##### ONNX
- [ONNX Runtime](https://github.com/microsoft/onnxruntime) 
: ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator. [onnxruntime.ai](https://onnxruntime.ai/)
- [ONNX](https://github.com/onnx/onnx) 
: Open Neural Network Exchange. Open standard for machine learning interoperability. [onnx.ai](https://onnx.ai/)
- [ONNXMLTools](https://github.com/onnx/onnxmltools) 
: ONNXMLTools enables you to convert models from different machine learning toolkits into [ONNX](https://github.com/onnx/onnx). [onnx.ai](https://onnx.ai/)
- [xboot/libonnx](https://github.com/xboot/libonnx) 
: A lightweight, portable pure C99 onnx inference engine for embedded devices with hardware acceleration support.
- [kraiskil/onnx2c](https://github.com/kraiskil/onnx2c) 
: Open Neural Network Exchange to C compiler. Onnx2c is a [ONNX](https://onnx.ai/) to C compiler. It will read an ONNX file, and generate C code to be included in your project. Onnx2c's target is "Tiny ML", meaning running the inference on microcontrollers.
- [tract](https://github.com/sonos/tract) : Sonos' Neural Network inference engine. Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference
- [ort](https://github.com/pykeio/ort) : A Rust wrapper for ONNX Runtime. [docs.rs/ort](https://docs.rs/ort/latest/ort/)
- [onnxruntime-rs](https://github.com/nbigaouette/onnxruntime-rs) 
: This is an attempt at a Rust wrapper for [Microsoft's ONNX Runtime](https://github.com/microsoft/onnxruntime) (version 1.8).
- [Wonnx](https://github.com/webonnx/wonnx) 
: Wonnx is a GPU-accelerated ONNX inference run-time written 100% in Rust, ready for the web.
- [altius](https://github.com/maekawatoshiki/altius) 
: Small ONNX inference runtime written in Rust.
- [Hyuto/yolo-nas-onnx](https://github.com/Hyuto/yolo-nas-onnx) 
: Inference YOLO-NAS ONNX model. [hyuto.github.io/yolo-nas-onnx/](https://hyuto.github.io/yolo-nas-onnx/)
- [DanielSarmiento04/yolov10cpp](https://github.com/DanielSarmiento04/yolov10cpp) 
: Implementation of yolo v10 in c++ std 17 over opencv and onnxruntime.
- ##### TensorRT
- [NVIDIA/TensorRT](https://github.com/NVIDIA/TensorRT) 
: NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT. [developer.nvidia.com/tensorrt](https://developer.nvidia.com/tensorrt)
- [NVIDIA/TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) 
: TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM also contains components to create Python and C++ runtimes that execute those TensorRT engines. [nvidia.github.io/TensorRT-LLM](https://nvidia.github.io/TensorRT-LLM)
- [kalfazed/tensorrt_starter](https://github.com/kalfazed/tensorrt_starter) 
: This repository give a guidline to learn CUDA and TensorRT from the beginning.
- [wang-xinyu/tensorrtx](https://github.com/wang-xinyu/tensorrtx) 
: TensorRTx aims to implement popular deep learning networks with tensorrt network definition APIs.
- [laugh12321/TensorRT-YOLO](https://github.com/laugh12321/TensorRT-YOLO) 
: 🚀 你的YOLO部署神器。TensorRT Plugin、CUDA Kernel、CUDA Graphs三管齐下，享受闪电般的推理速度。| Your YOLO Deployment Powerhouse. With the synergy of TensorRT Plugins, CUDA Kernels, and CUDA Graphs, experience lightning-fast inference speeds. TensorRT-YOLO 是一个支持 YOLOv3、YOLOv5、YOLOv6、YOLOv7、YOLOv8、YOLOv9、YOLOv10、YOLO11、PP-YOLOE 和 PP-YOLOE+ 的推理加速项目，使用 NVIDIA TensorRT 进行优化。项目不仅集成了 TensorRT 插件以增强后处理效果，还使用了 CUDA 核函数以及 CUDA 图来加速推理。TensorRT-YOLO 提供了 C++ 和 Python 推理的支持，旨在提供快速而优化的目标检测解决方案。
- [olibartfast/object-detection-inference](https://github.com/olibartfast/object-detection-inference) 
: C++ object detection inference from video or image input source. Inference for object detection from a video or image input source, with support for multiple switchable frameworks to manage the inference process, and optional GStreamer integration for video capture.
- [spacewalk01/yolov11-tensorrt](https://github.com/spacewalk01/yolov11-tensorrt) 
: C++ implementation of YOLOv11 using TensorRT API.
- [shouxieai/tensorRT_Pro](https://github.com/shouxieai/tensorRT_Pro) 
: C++ library based on tensorrt integration.
- [shouxieai/infer](https://github.com/shouxieai/infer) 
: A new tensorrt integrate. Easy to integrate many tasks.
- [Melody-Zhou/tensorRT_Pro-YOLOv8](https://github.com/Melody-Zhou/tensorRT_Pro-YOLOv8) 
: This repository is based on [shouxieai/tensorRT_Pro](https://github.com/shouxieai/tensorRT_Pro), with adjustments to support YOLOv8. 前已支持 YOLOv8、YOLOv8-Cls、YOLOv8-Seg、YOLOv8-OBB、YOLOv8-Pose、RT-DETR、ByteTrack、YOLOv9、YOLOv10、RTMO、PP-OCRv4、LaneATT 高性能推理！！！🚀🚀🚀
- [emptysoal/TensorRT-YOLOv8-ByteTrack](https://github.com/emptysoal/TensorRT-YOLOv8-ByteTrack) 
: An object tracking project with YOLOv8 and ByteTrack, speed up by C++ and TensorRT.
- [Linaom1214/TensorRT-For-YOLO-Series](https://github.com/Linaom1214/TensorRT-For-YOLO-Series) 
: tensorrt for yolo series (YOLOv10,YOLOv9,YOLOv8,YOLOv7,YOLOv6,YOLOX,YOLOv5), nms plugin support.
- [l-sf/Linfer](https://github.com/l-sf/Linfer) 
: 基于TensorRT的C++高性能推理库，Yolov10, YoloPv2，Yolov5/7/X/8，RT-DETR，单目标跟踪OSTrack、LightTrack。
- [taifyang/yolo-inference](https://github.com/taifyang/yolo-inference) 
: C++ and Python implementations of YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOv9 and YOLOv10 inference.
- [1461521844lijin/trt_yolo_video_pipeline](https://github.com/1461521844lijin/trt_yolo_video_pipeline) 
: TensorRT+YOLO系列的 多路 多卡 多实例 并行视频分析处理案例。
- [FeiYull/TensorRT-Alpha](https://github.com/FeiYull/TensorRT-Alpha) 
: 🔥🔥🔥TensorRT for YOLOv8、YOLOv8-Pose、YOLOv8-Seg、YOLOv8-Cls、YOLOv7、YOLOv6、YOLOv5、YOLONAS......🚀🚀🚀CUDA IS ALL YOU NEED.🍎🍎🍎
- [triple-Mu/YOLOv8-TensorRT](https://github.com/triple-Mu/YOLOv8-TensorRT) 
: YOLOv8 using TensorRT accelerate !
- [cyrusbehr/YOLOv8-TensorRT-CPP](https://github.com/cyrusbehr/YOLOv8-TensorRT-CPP) 
: YOLOv8 TensorRT C++ Implementation. A C++ Implementation of YoloV8 using TensorRT Supports object detection, semantic segmentation, and body pose estimation.
- [emptysoal/TensorRT-YOLOv8](https://github.com/emptysoal/TensorRT-YOLOv8) 
: Based on tensorrt v8.0+, deploy detect, pose, segment, tracking of YOLOv8 with C++ and python api.
- [hamdiboukamcha/yolov10-tensorrt](https://github.com/hamdiboukamcha/yolov10-tensorrt) 
: YOLOv10 C++ TensorRT : Real-Time End-to-End Object Detection.
- [VIDIA-AI-IOT/torch2trt](https://github.com/NVIDIA-AI-IOT/torch2trt) : An easy to use PyTorch to TensorRT converter.
- [zhiqwang/yolort](https://github.com/zhiqwang/yolort) 
: yolort is a runtime stack for yolov5 on specialized accelerators such as tensorrt, libtorch, onnxruntime, tvm and ncnn. [zhiqwang.com/yolort](https://zhiqwang.com/yolort/)
- [DefTruth/lite.ai.toolkit](https://github.com/DefTruth/lite.ai.toolkit) 
: 🛠 A lite C++ toolkit of awesome AI models with ONNXRuntime, NCNN, MNN and TNN. YOLOX, YOLOP, YOLOv6, YOLOR, MODNet, YOLOX, YOLOv7, YOLOv5. MNN, NCNN, TNN, ONNXRuntime. “🛠Lite.Ai.ToolKit: 一个轻量级的C++ AI模型工具箱，用户友好（还行吧），开箱即用。已经包括 100+ 流行的开源模型。这是一个根据个人兴趣整理的C++工具箱，, 涵盖目标检测、人脸检测、人脸识别、语义分割、抠图等领域。”
- [PaddlePaddle/FastDeploy](https://github.com/PaddlePaddle/FastDeploy) 
: ⚡️An Easy-to-use and Fast Deep Learning Model Deployment Toolkit for ☁️Cloud 📱Mobile and 📹Edge. Including Image, Video, Text and Audio 20+ main stream scenarios and 150+ SOTA models with end-to-end optimization, multi-platform and multi-framework support.
- [enazoe/yolo-tensorrt](https://github.com/enazoe/yolo-tensorrt) 
: TensorRT8.Support Yolov5n,s,m,l,x .darknet -> tensorrt. Yolov4 Yolov3 use raw darknet *.weights and *.cfg fils. If the wrapper is useful to you,please Star it.
- [guojianyang/cv-detect-robot](https://github.com/guojianyang/cv-detect-robot) 
: 🔥🔥🔥🔥🔥🔥Docker NVIDIA Docker2 YOLOV5 YOLOX YOLO Deepsort TensorRT ROS Deepstream Jetson Nano TX2 NX for High-performance deployment(高性能部署)。
- [BlueMirrors/Yolov5-TensorRT](https://github.com/BlueMirrors/Yolov5-TensorRT) 
: Yolov5 TensorRT Implementations.
- [lewes6369/TensorRT-Yolov3](https://github.com/lewes6369/TensorRT-Yolov3) 
: TensorRT for Yolov3.
- [CaoWGG/TensorRT-YOLOv4](https://github.com/CaoWGG/TensorRT-YOLOv4) 
:tensorrt5, yolov4, yolov3,yolov3-tniy,yolov3-tniy-prn.
- [isarsoft/yolov4-triton-tensorrt](https://github.com/isarsoft/yolov4-triton-tensorrt) 
: YOLOv4 on Triton Inference Server with TensorRT.
- [TrojanXu/yolov5-tensorrt](https://github.com/TrojanXu/yolov5-tensorrt) 
: A tensorrt implementation of yolov5.
- [tjuskyzhang/Scaled-YOLOv4-TensorRT](https://github.com/tjuskyzhang/Scaled-YOLOv4-TensorRT) 
: Implement yolov4-tiny-tensorrt, yolov4-csp-tensorrt, yolov4-large-tensorrt(p5, p6, p7) layer by layer using TensorRT API.
- [Syencil/tensorRT](https://github.com/Syencil/tensorRT) 
: TensorRT-7 Network Lib 包括常用目标检测、关键点检测、人脸检测、OCR等 可训练自己数据。
- [SeanAvery/yolov5-tensorrt](https://github.com/SeanAvery/yolov5-tensorrt) 
: YOLOv5 in TensorRT.
- [Monday-Leo/YOLOv7_Tensorrt](https://github.com/Monday-Leo/YOLOv7_Tensorrt) 
: A simple implementation of Tensorrt YOLOv7.
- [ibaiGorordo/ONNX-YOLOv6-Object-Detection](https://github.com/ibaiGorordo/ONNX-YOLOv6-Object-Detection) 
: Python scripts performing object detection using the YOLOv6 model in ONNX.
- [ibaiGorordo/ONNX-YOLOv7-Object-Detection](https://github.com/ibaiGorordo/ONNX-YOLOv7-Object-Detection) 
: Python scripts performing object detection using the YOLOv7 model in ONNX.
- [triple-Mu/yolov7](https://github.com/triple-Mu/yolov7) 
: End2end TensorRT YOLOv7.
- [hewen0901/yolov7_trt](https://github.com/hewen0901/yolov7_trt) 
: yolov7目标检测算法的c++ tensorrt部署代码。
- [tsutof/tiny_yolov2_onnx_cam](https://github.com/tsutof/tiny_yolov2_onnx_cam) 
: Tiny YOLO v2 Inference Application with NVIDIA TensorRT.
- [Monday-Leo/Yolov5_Tensorrt_Win10](https://github.com/Monday-Leo/Yolov5_Tensorrt_Win10) 
: A simple implementation of tensorrt yolov5 python/c++🔥
- [Wulingtian/yolov5_tensorrt_int8](https://github.com/Wulingtian/yolov5_tensorrt_int8) 
: TensorRT int8 量化部署 yolov5s 模型，实测3.3ms一帧！
- [Wulingtian/yolov5_tensorrt_int8_tools](https://github.com/Wulingtian/yolov5_tensorrt_int8_tools) 
: tensorrt int8 量化yolov5 onnx模型。
- [MadaoFY/yolov5_TensorRT_inference](https://github.com/MadaoFY/yolov5_TensorRT_inference) 
: 记录yolov5的TensorRT量化及推理代码，经实测可运行于Jetson平台。
- [ibaiGorordo/ONNX-YOLOv8-Object-Detection](https://github.com/ibaiGorordo/ONNX-YOLOv8-Object-Detection) 
: Python scripts performing object detection using the YOLOv8 model in ONNX.
- [we0091234/yolov8-tensorrt](https://github.com/we0091234/yolov8-tensorrt) 
: yolov8 tensorrt 加速.
- [FeiYull/yolov8-tensorrt](https://github.com/FeiYull/yolov8-tensorrt) 
: YOLOv8的TensorRT+CUDA加速部署，代码可在Win、Linux下运行。
- [cvdong/YOLO_TRT_SIM](https://github.com/cvdong/YOLO_TRT_SIM) 
: 🐇 一套代码同时支持YOLO X, V5, V6, V7, V8 TRT推理 ™️ 🔝 ,前后处理均由CUDA核函数实现 CPP/CUDA🚀
- [cvdong/YOLO_TRT_PY](https://github.com/cvdong/YOLO_TRT_PY) 
: 🐰 一套代码同时支持YOLOV5, V6, V7, V8 TRT推理 ™️ PYTHON ✈️
- [Psynosaur/Jetson-SecVision](https://github.com/Psynosaur/Jetson-SecVision) 
: Person detection for Hikvision DVR with AlarmIO ports, uses TensorRT and yolov4.
- [tatsuya-fukuoka/yolov7-onnx-infer](https://github.com/tatsuya-fukuoka/yolov7-onnx-infer) 
: Inference with yolov7's onnx model.
- [MadaoFY/yolov5_TensorRT_inference](https://github.com/MadaoFY/yolov5_TensorRT_inference) : 记录yolov5的TensorRT量化及推理代码，经实测可运行于Jetson平台。
- [ervgan/yolov5_tensorrt_inference](https://github.com/ervgan/yolov5_tensorrt_inference) 
: TensorRT cpp inference for Yolov5 model. Supports yolov5 v1.0, v2.0, v3.0, v3.1, v4.0, v5.0, v6.0, v6.2, v7.0.
- [AlbinZhu/easy-trt](https://github.com/AlbinZhu/easy-trt) 
: TensorRT for YOLOv10 with CUDA.
- [PrinceP/tensorrt-cpp-for-onnx](https://github.com/PrinceP/tensorrt-cpp-for-onnx) 
: Tensorrt codebase to inference in c++ for all major neural arch using onnx.
- [hamdiboukamcha/Yolo-V10-cpp-TensorRT](https://github.com/hamdiboukamcha/Yolo-V10-cpp-TensorRT) 
: The YOLOv10 C++ TensorRT Project in C++ and optimized using NVIDIA TensorRT.
- ##### DeepStream
- [NVIDIA-AI-IOT/deepstream_reference_apps](https://github.com/NVIDIA-AI-IOT/deepstream_reference_apps) 
: Reference Apps using DeepStream 6.1.
- [NVIDIA-AI-IOT/deepstream_python_apps](https://github.com/NVIDIA-AI-IOT/deepstream_python_apps) 
: DeepStream SDK Python bindings and sample applications.
- [NVIDIA-AI-IOT/deepstream_python_apps](https://github.com/NVIDIA-AI-IOT/yolov5_gpu_optimization) 
: This repository provides YOLOV5 GPU optimization sample.
- [marcoslucianops/DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo) 
: NVIDIA DeepStream SDK 6.1.1 / 6.1 / 6.0.1 / 6.0 implementation for YOLO models.
- [DanaHan/Yolov5-in-Deepstream-5.0](https://github.com/DanaHan/Yolov5-in-Deepstream-5.0) 
: Describe how to use yolov5 in Deepstream 5.0.
- [ozinc/Deepstream6_YoloV5_Kafka](https://github.com/ozinc/Deepstream6_YoloV5_Kafka) 
: This repository gives a detailed explanation on making custom trained deepstream-Yolo models predict and send message over kafka.
- [kn1ghtf1re/yolov8-deepstream-6-1](https://github.com/kn1ghtf1re/yolov8-deepstream-6-1) 
: YOLOv8 by Ultralytics in DeepStream 6.1.
- [bharath5673/Deepstream](https://github.com/bharath5673/Deepstream) 
: yolov2 ,yolov5 ,yolov6 ,yolov7 ,yolov7,yolovR ,yolovX on deepstream.
- [Savant](https://github.com/insight-platform/Savant) 
: Python Computer Vision & Video Analytics Framework With Batteries Included. [savant-ai.io](https://savant-ai.io/)
- [Savant](https://github.com/quangdungluong/DeepStream-YOLOv11) 
: Plug-and-Play Custom Parsers for AI Models in NVIDIA DeepStream SDK. Supported YOLOv11 model.
- ##### OpenVINO
- [OpenVINO](https://github.com/openvinotoolkit/openvino) 
: This open source version includes several components: namely Model Optimizer, OpenVINO™ Runtime, Post-Training Optimization Tool, as well as CPU, GPU, MYRIAD, multi device and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics.
- [PINTO0309/OpenVINO-YoloV3](https://github.com/PINTO0309/OpenVINO-YoloV3) 
: YoloV3/tiny-YoloV3 + RaspberryPi3/Ubuntu LaptopPC + NCS/NCS2 + USB Camera + Python + OpenVINO.
- [TNTWEN/OpenVINO-YOLOV4](https://github.com/TNTWEN/OpenVINO-YOLOV4) 
: This is implementation of YOLOv4,YOLOv4-relu,YOLOv4-tiny,YOLOv4-tiny-3l,Scaled-YOLOv4 and INT8 Quantization in OpenVINO2021.3.
- [fb029ed/yolov5_cpp_openvino](https://github.com/fb029ed/yolov5_cpp_openvino) 
: 用c++实现了yolov5使用openvino的部署。
- [dlod-openvino/yolov5_infer](https://github.com/dlod-openvino/yolov5_infer) 
: Do the YOLOv5 model inference by OpenCV/OpenVINO based on onnx model format.
- [snail0614/yolov5.6_openvino_cpp](https://github.com/snail0614/yolov5.6_openvino_cpp) 
: yolov5.6.1 OpenVINO的C++实现。
- [shungfu/openvino_yolov5v7](https://github.com/shungfu/openvino_yolov5v7) 
: YOLOv5 YOLOv7 INT8 quantization using OpenVINO.
- [dacquaviva/yolov5-openvino-cpp-python](https://github.com/dacquaviva/yolov5-openvino-cpp-python) 
: Example of using ultralytics YOLOv5 with Openvino in C++ and Python.
- [rlggyp/YOLOv10-OpenVINO-CPP-Inference](https://github.com/rlggyp/YOLOv10-OpenVINO-CPP-Inference) 
: YOLOv10 C++ implementation using OpenVINO for efficient and accurate real-time object detection.
- ##### NCNN
- [NCNN](https://github.com/Tencent/ncnn) 
: ncnn is a high-performance neural network inference framework optimized for the mobile platform.
- [Baiyuetribe/ncnn-models](https://github.com/Baiyuetribe/ncnn-models) 
: awesome AI models with NCNN, and how they were converted ✨✨✨
- [Qengineering/YoloV10-ncnn-Raspberry-Pi-4](https://github.com/Qengineering/YoloV10-ncnn-Raspberry-Pi-4) 
: YoloV10 for a bare Raspberry Pi 4 or 5.
- [cmdbug/YOLOv5_NCNN](https://github.com/cmdbug/YOLOv5_NCNN) 
: 🍅 Deploy ncnn on mobile phones. Support Android and iOS. 移动端ncnn部署，支持Android与iOS。
- [natanielruiz/android-yolo](https://github.com/natanielruiz/android-yolo) 
: Real-time object detection on Android using the YOLO network with TensorFlow.
- [nihui/ncnn-android-yolov5](https://github.com/nihui/ncnn-android-yolov5) 
: The YOLOv5 object detection android example.
- [szaza/android-yolo-v2](https://github.com/szaza/android-yolo-v2) 
: Android YOLO real time object detection sample application with Tensorflow mobile.
- [FeiGeChuanShu/ncnn-android-yolox](https://github.com/FeiGeChuanShu/ncnn-android-yolox) 
: Real time yolox Android demo by ncnn.
- [xiangweizeng/darknet2ncnn](https://github.com/xiangweizeng/darknet2ncnn) 
: Darknet2ncnn converts the darknet model to the ncnn model.
- [sunnyden/YOLOV5_NCNN_Android](https://github.com/sunnyden/YOLOV5_NCNN_Android) 
: YOLOv5 C++ Implementation on Android using NCNN framework.
- [duangenquan/YoloV2NCS](https://github.com/duangenquan/YoloV2NCS) 
: This project shows how to run tiny yolo v2 with movidius stick.
- [lp6m/yolov5s_android](https://github.com/lp6m/yolov5s_android) 
: Run yolov5s on Android device!
- [KoheiKanagu/ncnn_yolox_flutter](https://github.com/KoheiKanagu/ncnn_yolox_flutter) 
: This is a plugin to run YOLOX on ncnn.
- [cyrillkuettel/ncnn-android-yolov5](https://github.com/cyrillkuettel/ncnn-android-yolov5) 
: This is a sample ncnn android project, it depends on ncnn library and opencv.
- [DataXujing/ncnn_android_yolov6](https://github.com/DataXujing/ncnn_android_yolov6) 
: 手摸手实现基于QT和NCNN的安卓手机YOLOv6模型的部署！
- [Qengineering/YoloV3-ncnn-Raspberry-Pi-4](https://github.com/Qengineering/YoloV3-ncnn-Raspberry-Pi-4) 
: YoloV3 Raspberry Pi 4.
- [Qengineering/YoloV4-ncnn-Raspberry-Pi-4](https://github.com/Qengineering/YoloV4-ncnn-Raspberry-Pi-4) 
: YoloV4 on a bare Raspberry Pi 4 with ncnn framework.
- [Qengineering/YoloV5-ncnn-Raspberry-Pi-4](https://github.com/Qengineering/YoloV5-ncnn-Raspberry-Pi-4) 
: YoloV5 for a bare Raspberry Pi 4.
- [Qengineering/YoloV6-ncnn-Raspberry-Pi-4](https://github.com/Qengineering/YoloV6-ncnn-Raspberry-Pi-4) 
: YoloV6 for a bare Raspberry Pi using ncnn.
- [Qengineering/YoloV7-ncnn-Raspberry-Pi-4](https://github.com/Qengineering/YoloV7-ncnn-Raspberry-Pi-4) 
: YoloV7 for a bare Raspberry Pi using ncnn.
- [Qengineering/YoloV8-ncnn-Raspberry-Pi-4](https://github.com/Qengineering/YoloV8-ncnn-Raspberry-Pi-4) 
: YoloV8 for a bare Raspberry Pi 4.
- [FeiGeChuanShu/ncnn-android-yolov8](https://github.com/FeiGeChuanShu/ncnn-android-yolov8) 
: Real time yolov8 Android demo by ncnn.
- [FLamefiREz/yolov10-android-ncnn](https://github.com/FLamefiREz/yolov10-android-ncnn) 
: yolov10-android-ncnn.
- ##### MNN
- [MNN](https://github.com/alibaba/MNN) 
: MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba. (**[MLSys 2020](https://proceedings.mlsys.org/paper/2020/hash/8f14e45fceea167a5a36dedd4bea2543-Abstract.html)**)
- [apxlwl/MNN-yolov3](https://github.com/apxlwl/MNN-yolov3) 
: MNN demo of Strongeryolo, including channel pruning, android support...
- ##### Other Engine
- [TVM](https://github.com/apache/tvm) 
: Open deep learning compiler stack for cpu, gpu and specialized accelerators.
- [ceccocats/tkDNN](https://github.com/ceccocats/tkDNN) 
: Deep neural network library and toolkit to do high performace inference on NVIDIA jetson platforms. "A Systematic Assessment of Embedded Neural Networks for Object Detection". (**[IEEE ETFA 2020](https://ieeexplore.ieee.org/document/9212130)**)
- [Tengine](https://github.com/OAID/Tengine) 
: Tengine is a lite, high performance, modular inference engine for embedded device.
- [Paddle Lite](https://github.com/paddlepaddle/paddle-lite) 
: Multi-platform high performance deep learning inference engine (飞桨多端多平台高性能深度学习推理引擎）。
- [DeployAI/nndeploy](https://github.com/DeployAI/nndeploy) 
: nndeploy is a cross-platform, high-performing, and straightforward AI model deployment framework. We strive to deliver a consistent and user-friendly experience across various inference framework in complex deployment environments and focus on performance. nndeploy一款跨平台、高性能、简单易用的模型端到端部署框架。我们致力于屏蔽不同推理框架的差异，提供一致且用户友好的编程体验，同时专注于部署全流程的性能。
- [yhwang-hub/dl_model_infer](https://github.com/yhwang-hub/dl_model_infer) 
: his is a c++ version of the AI reasoning library. Currently, it only supports the reasoning of the tensorrt model. The follow-up plan supports the c++ reasoning of frameworks such as Openvino, NCNN, and MNN. There are two versions for pre- and post-processing, c++ version and cuda version. It is recommended to use the cuda version., This repository provides accelerated deployment cases of deep learning CV popular models, and cuda c supports dynamic-batch image process, infer, decode, NMS.
- [hollance/YOLO-CoreML-MPSNNGraph](https://github.com/hollance/YOLO-CoreML-MPSNNGraph) 
: Tiny YOLO for iOS implemented using CoreML but also using the new MPS graph API.
- [r4ghu/iOS-CoreML-Yolo](https://github.com/r4ghu/iOS-CoreML-Yolo) 
: This is the implementation of Object Detection using Tiny YOLO v1 model on Apple's CoreML Framework.
- [airockchip/rknn_model_zoo](https://github.com/airockchip/rknn_model_zoo) 
: Rockchip Neural Network(RKNN)是瑞芯微为了加速模型推理而基于自身NPU硬件架构定义的一套模型格式.使用该格式定义的模型在Rockchip NPU上可以获得远高于CPU/GPU的性能。
- [LynxiTechnology/Lynxi-model-zoo](https://github.com/LynxiTechnology/Lynxi-model-zoo) 
: Lynxi-model-zoo.
- ### FPGA TPU NPU Hardware Deployment
#### FPGA TPU NPU 硬件部署
- ##### FPGA
- [Xilinx/Vitis-AI](https://github.com/Xilinx/Vitis-AI/tree/master/demo) 
: Vitis AI offers a unified set of high-level C++/Python programming APIs to run AI applications across edge-to-cloud platforms, including DPU for Alveo, and DPU for Zynq Ultrascale+ MPSoC and Zynq-7000. It brings the benefits to easily port AI applications from cloud to edge and vice versa. 10 samples in [VART Samples](https://github.com/Xilinx/Vitis-AI/tree/master/demo/VART) are available to help you get familiar with the unfied programming APIs. [Vitis-AI-Library](https://github.com/Xilinx/Vitis-AI/tree/master/demo/Vitis-AI-Library) provides an easy-to-use and unified interface by encapsulating many efficient and high-quality neural networks.
- [tensil-ai/tensil](https://github.com/tensil-ai/tensil) 
: Open source machine learning accelerators. [www.tensil.ai](https://www.tensil.ai/)
- [19801201/SpinalHDL_CNN_Accelerator](https://github.com/19801201/SpinalHDL_CNN_Accelerator) 
: CNN accelerator implemented with Spinal HDL.
- [dhm2013724/yolov2_xilinx_fpga](https://github.com/dhm2013724/yolov2_xilinx_fpga) 
: YOLOv2 Accelerator in Xilinx's Zynq-7000 Soc(PYNQ-z2, Zedboard and ZCU102). (**[硕士论文 2019](https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CMFD&dbname=CMFDTEMP&filename=1019228234.nh&uid=WEEvREcwSlJHSldRa1FhdXNXaEhoOGhUTzA5T0tESzdFZ2pyR1NJR1ZBaz0=$9A4hF_YAuvQ5obgVAqNKPCYcEjKensW4IQMovwHtwkF4VYPoHbKxJw!!&v=MjE5NTN5dmdXN3JBVkYyNkY3RzZGdFBQcTVFYlBJUjhlWDFMdXhZUzdEaDFUM3FUcldNMUZyQ1VSTE9lWnVkdUY=), [电子技术应用 2019](https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFQ&dbname=CJFDLAST2019&filename=DZJY201908009&uid=WEEvREcwSlJHSldRa1FhdXNXaEhoOGhUTzA5T0tESzdFZ2pyR1NJR1ZBaz0=$9A4hF_YAuvQ5obgVAqNKPCYcEjKensW4IQMovwHtwkF4VYPoHbKxJw!!&v=MDU0NDJDVVJMT2VadWR1Rnl2Z1c3ck1JVGZCZDdHNEg5ak1wNDlGYllSOGVYMUx1eFlTN0RoMVQzcVRyV00xRnI=), [计算机科学与探索 2019](https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFQ&dbname=CJFDTEMP&filename=KXTS201910005&uid=WEEvREcwSlJHSldRa1FhdXNXaEhoOGhUTzA5T0tESzdFZ2pyR1NJR1ZBaz0=$9A4hF_YAuvQ5obgVAqNKPCYcEjKensW4IQMovwHtwkF4VYPoHbKxJw!!&v=MjkwNzdXTTFGckNVUkxPZVp1ZHVGeXZnVzdyT0xqWGZmYkc0SDlqTnI0OUZZWVI4ZVgxTHV4WVM3RGgxVDNxVHI=)**)
- [Yu-Zhewen/Tiny_YOLO_v3_ZYNQ](https://github.com/Yu-Zhewen/Tiny_YOLO_v3_ZYNQ) 
: Implement Tiny YOLO v3 on ZYNQ. "A Parameterisable FPGA-Tailored Architecture for YOLOv3-Tiny". (**[ARC 2020](https://link.springer.com/chapter/10.1007/978-3-030-44534-8_25)**)
- [HSqure/ultralytics-pt-yolov3-vitis-ai-edge](https://github.com/HSqure/ultralytics-pt-yolov3-vitis-ai-edge) 
: This demo is only used for inference testing of Vitis AI v1.4 and quantitative compilation of DPU. It is compatible with the training results of [ultralytics/yolov3](https://github.com/ultralytics/yolov3) v9.5.0 (it needs to use the model saving method of Pytorch V1.4).
- [mcedrdiego/Kria_yolov3_ppe](https://github.com/mcedrdiego/Kria_yolov3_ppe) 
: Kria KV260 Real-Time Personal Protective Equipment Detection. "Deep Learning for Site Safety: Real-Time Detection of Personal Protective Equipment". (**[Automation in Construction 2020](https://www.sciencedirect.com/science/article/abs/pii/S0926580519308325)**)
- [xlsjdjdk/Ship-Detection-based-on-YOLOv3-and-KV260](https://github.com/xlsjdjdk/Ship-Detection-based-on-YOLOv3-and-KV260) 
: This is the entry project of the Xilinx Adaptive Computing Challenge 2021. It uses YOLOv3 for ship target detection in optical remote sensing images, and deploys DPU on the KV260 platform to achieve hardware acceleration.
- [Pomiculture/YOLOv4-Vitis-AI](https://github.com/Pomiculture/YOLOv4-Vitis-AI) 
: Custom YOLOv4 for apple recognition (clean/damaged) on Alveo U280 accelerator card using Vitis AI framework.
- [mkshuvo2/ZCU104_YOLOv3_Post_Processing](https://github.com/mkshuvo2/ZCU104_YOLOv3_Post_Processing) 
: Tensor outputs form Vitis AI Runner Class for YOLOv3.
- [puffdrum/v4tiny_pt_quant](https://github.com/puffdrum/v4tiny_pt_quant) 
: quantization for yolo with xilinx/vitis-ai-pytorch.
- [chanshann/LITE_YOLOV3_TINY_VITISAI](https://github.com/chanshann/LITE_YOLOV3_TINY_VITISAI) 
: LITE_YOLOV3_TINY_VITISAI.
- [LukiBa/zybo_yolo](https://github.com/LukiBa/zybo_yolo) 
: YOLO example implementation using Intuitus CNN accelerator on ZYBO ZYNQ-7000 FPGA board.
- [matsuda-slab/YOLO_ZYNQ_MASTER](https://github.com/matsuda-slab/YOLO_ZYNQ_MASTER) 
: Implementation of YOLOv3-tiny on FPGA.
- [FerberZhang/Yolov2-FPGA-CNN-](https://github.com/FerberZhang/Yolov2-FPGA-CNN-) 
: A demo for accelerating YOLOv2 in xilinx's fpga PYNQ.
- [ChainZeeLi/FPGA_DPU](https://github.com/ChainZeeLi/FPGA_DPU) 
: This project is to implement YOLO v3 on Xilinx FPGA with DPU.
- [xbdxwyh/yolov3_fpga_project](https://github.com/xbdxwyh/yolov3_fpga_project) 
: yolov3_fpga_project.
- [ZLkanyo009/Yolo-compression-and-deployment-in-FPGA](https://github.com/ZLkanyo009/Yolo-compression-and-deployment-in-FPGA) 
: 基于FPGA量化的人脸口罩检测。
- [xiying-boy/yolov3-AX7350](https://github.com/xiying-boy/yolov3-AX7350) 
: 基于HLS_YOLOV3的驱动文件。
- [himewel/yolowell](https://github.com/himewel/yolowell) 
: A set of hardware architectures to build a co-design of convolutional neural networks inference at FPGA devices.
- [embedeep/Free-TPU](https://github.com/embedeep/Free-TPU) 
: Free TPU for FPGA with Lenet, MobileNet, Squeezenet, Resnet, Inception V3, YOLO V3, and ICNet. Deep learning acceleration using Xilinx zynq (Zedboard or ZC702 ) or kintex-7 to solve image classification, detection, and segmentation problem.
- [yarakigit/design_contest_yolo_change_ps_to_pl](https://github.com/yarakigit/design_contest_yolo_change_ps_to_pl) 
: Converts pytorch yolo format weights to C header files for bare-metal (FPGA implementation).
- [MasLiang/CNN-On-FPGA](https://github.com/MasLiang/CNN-On-FPGA) 
: This is the code of the CNN on FPGA.But this can only be used for reference at present for some files are write coarsly using ISE.
- [adamgallas/fpga_accelerator_yolov3tiny](https://github.com/adamgallas/fpga_accelerator_yolov3tiny) 
: fpga_accelerator_yolov3tiny.
- [ylk678910/tiny-yolov3-fpga](https://github.com/ylk678910/tiny-yolov3-fpga) 
: Use an all-programmable SoC board to implement locating and tracking tasks. The hardware algorithm, a row-stationary-like strategy, can parallel calculate and reduce the storage buffer area on FPGA.
- [zhen8838/K210_Yolo_framework](https://github.com/zhen8838/K210_Yolo_framework) 
: Yolo v3 framework base on tensorflow, support multiple models, multiple datasets, any number of output layers, any number of anchors, model prune, and portable model to K210 !
- [SEASKY-Master/SEASKY_K210](https://github.com/SEASKY-Master/SEASKY_K210) 
: K210 PCB YOLO.
- [SEASKY-Master/Yolo-for-k210](https://github.com/SEASKY-Master/Yolo-for-k210) 
: Yolo-for-k210.
- [TonyZ1Min/yolo-for-k210](https://github.com/TonyZ1Min/yolo-for-k210) 
: keras-yolo-for-k210.
- [vseasky/yolo-for-k210](https://github.com/vseasky/yolo-for-k210) 
: Yolo-for-k210.
- [InnoIPA/dpu-sc](https://github.com/InnoIPA/dpu-sc) 
: dpu-sc presented how to create quick demos to run AI inference(YOLOv4-Tiny, LPRNet) on DPU with MPSoC.
- [InnoIPA/vaiGO](https://github.com/InnoIPA/vaiGo) 
: vaiGO means Vitis-ai GO. We provide utility and tutorial that make it easy to convert a trained AI model into a bitstream that can be deployed on an FPGA Edge AI Box.
- [InnoIPA/EXMU-X261-usermanual](https://github.com/InnoIPA/EXMU-X261-usermanual) 
: We have built more defect detection solutions with YOLOv4-tiny on EXMU-X261.
- ##### RK3588
- [leafqycc/rknn-cpp-Multithreading](https://github.com/leafqycc/rknn-cpp-Multithreading) 
: A simple demo of yolov5s running on rk3588/3588s using c++ (about 142 frames). / 一个使用c++在rk3588/3588s上运行的yolov5s简单demo(142帧/s)。
- [leafqycc/rknn-multi-threaded](https://github.com/leafqycc/rknn-multi-threaded) 
: A simple demo of yolov5s running on rk3588/3588s using Python (about 72 frames). / 一个使用Python在rk3588/3588s上运行的yolov5s简单demo(大约72帧/s)。
- [wzxzhuxi/rknn-3588-npu-yolo-accelerate](https://github.com/wzxzhuxi/rknn-3588-npu-yolo-accelerate) 
: rknn-3588部署yolov5，利用线程池实现npu推理加速；Deploying YOLOv5 on RKNN-3588, utilizing a thread pool to achieve NPU inference acceleration.
- [kaylorchen/rk3588-yolo-demo](https://github.com/kaylorchen/rk3588-yolo-demo) 
: The project is a multi-threaded inference demo of Yolo running on the RK3588 platform, which has been adapted for reading video files and camera feeds. The demo uses the Yolov8n model for file inference, with a maximum inference frame rate of up to 100 frames per second.
- [MontaukLaw/yolov5_3588_multi_thread](https://github.com/MontaukLaw/yolov5_3588_multi_thread) 
: 启动多线程, relu激活, 3588的yolo部署, 帧率150以上.
- [crab2rab/RKNN-YOLOV5-BatchInference-MultiThreading](https://github.com/crab2rab/RKNN-YOLOV5-BatchInference-MultiThreading) 
: RKNN-YOLOV5-BatchInference-MultiThreadingYOLOV5多张图片多线程C++推理。
- [Qengineering/YoloV10-NPU](https://github.com/Qengineering/YoloV10-NPU) 
: YoloV10 NPU for the RK3566/68/88.
- [cqu20160901/yolov10_rknn_Cplusplus](https://github.com/cqu20160901/yolov10_rknn_Cplusplus) 
: yolov10 瑞芯微 rknn 板端 C++部署，使用平台 rk3588。
- [Zhou-sx/yolov5_Deepsort_rknn](https://github.com/Zhou-sx/yolov5_Deepsort_rknn) 
: Track vehicles and persons on rk3588 / rk3399pro.
- [Applied-Deep-Learning-Lab/Yolov5_RK3588](https://github.com/Applied-Deep-Learning-Lab/Yolov5_RK3588) 
: Yolov5_RK3588.
- ##### Other Hardware
- [guichristmann/edge-tpu-tiny-yolo](https://github.com/guichristmann/edge-tpu-tiny-yolo) 
: Run Tiny YOLO-v3 on Google's Edge TPU USB Accelerator.
- [Charlie839242/-Trash-Classification-Car](https://github.com/Charlie839242/-Trash-Classification-Car) 
: 这是一个基于yolo-fastest模型的小车，主控是art-pi开发板，使用了rt thread操作系统。
- [Charlie839242/Deploy-yolo-fastest-tflite-on-raspberry](https://github.com/Charlie839242/Deploy-yolo-fastest-tflite-on-raspberry) 
: This project deploys a yolo fastest model in the form of tflite on raspberry 3b+.
- [mahxn0/Hisi3559A_Yolov5](https://github.com/mahxn0/Hisi3559A_Yolov5) 
: 基于hisi3559a的yolov5训练部署全流程。
- [ZhenxinYUAN/YOLO_hi3516Deploy](https://github.com/ZhenxinYUAN/YOLO_hi3516Deploy) 
: Deploy Yolo series algorithms on Hisilicon platform hi3516, including yolov3, yolov5, yolox, etc.
- [jveitchmichaelis/edgetpu-yolo](https://github.com/jveitchmichaelis/edgetpu-yolo) 
: Minimal-dependency Yolov5 export and inference demonstration for the Google Coral EdgeTPU.
- [xiaqing10/Hisi_YoLoV5](https://github.com/xiaqing10/Hisi_YoLoV5) 
: 海思nnie跑yolov5。
- [BaronLeeLZP/hi3516dv300_nnie-yolov3-demo](https://github.com/BaronLeeLZP/hi3516dv300_nnie-yolov3-demo) 
: 在海思Hisilicon的Hi3516dv300芯片上，利用nnie和opencv库，简洁了官方yolov3用例中各种复杂的嵌套调用/复杂编译，提供了交叉编译后可成功上板部署运行的demo。
- [OpenVINO-dev-contest/YOLOv7_OpenVINO](https://github.com/OpenVINO-dev-contest/YOLOv7_OpenVINO) 
: This repository will demostrate how to deploy a offical YOLOv7 pre-trained model with OpenVINO runtime api.
- [littledeep/YOLOv5-RK3399Pro](https://github.com/littledeep/YOLOv5-RK3399Pro) 
: PyTorch-->ONNX-->RKNN.
- [jnulzl/YOLOV5_RK1126](https://github.com/jnulzl/YOLOV5_RK1126) 
: yolov5 rk1126 cpp code.
- [Qengineering/YoloCam](https://github.com/Qengineering/YoloCam) 
: AI camera with live feed, email notification, Gdrive storage and event-triggered GPIO. Raspberry Pi stand-alone AI-powered camera with live feed, email notification and event-triggered cloud storage.
- [LSH9832/edgeyolo](https://github.com/LSH9832/edgeyolo) : an edge-real-time anchor-free object detector with decent performance.
- [liuyuan000/Rv1126_YOLOv5-Lite](https://github.com/liuyuan000/Rv1126_YOLOv5-Lite) 
: YOLOv5-Lite在Rv1126部署。
- [cqu20160901/yolov10_onnx_rknn_horizon_tensorRT](https://github.com/cqu20160901/yolov10_onnx_rknn_horizon_tensorRT) 
: yolov10 目标检测部署版本，便于移植不同平台（onnx、tensorRT、rknn、Horizon），全网部署最简单、运行速度最快的部署方式（全网首发）。
## Applications
- ### Video Object Detection
#### 视频目标检测
- [YOLOV](https://github.com/YuHengsss/YOLOV) 
: "YOLOV: Making Still Image Object Detectors Great at Video Object Detection". (**[arXiv 2022](https://arxiv.org/abs/2208.09686)**)
- [StreamYOLO](https://github.com/yancie-yjr/StreamYOLO) 
: "Real-time Object Detection for Streaming Perception". (**[CVPR 2022](https://arxiv.org/abs/2203.12338v1)**)
- [REPP](https://github.com/AlbertoSabater/Robust-and-efficient-post-processing-for-video-object-detection) 
: "Robust and efficient post-processing for video object detection". (**[IROS 2020](https://ieeexplore.ieee.org/abstract/document/9341600)**)
- [NoScope](https://github.com/stanford-futuredata/noscope) 
: "Noscope: optimizing neural network queries over video at scale". (**[arXiv 2017](https://arxiv.org/abs/1703.02529)**)
- ### Object Tracking
#### 目标跟踪
- #### Multi-Object Tracking
##### 多目标跟踪
- [sujanshresstha/YOLOv10_DeepSORT](https://github.com/sujanshresstha/YOLOv10_DeepSORT) 
: This repository contains code for object detection and tracking in videos using the YOLOv10 object detection model and the DeepSORT algorithm.
- [mikel-brostrom/yolo_tracking](https://github.com/mikel-brostrom/yolo_tracking) 
: BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models.
- [mikel-brostrom/Yolov7_StrongSORT_OSNet](https://github.com/mikel-brostrom/Yolov7_StrongSORT_OSNet) 
: Real-time multi-camera multi-object tracker using [YOLOv7](https://github.com/WongKinYiu/yolov7) and [StrongSORT](https://github.com/dyhBUPT/StrongSORT) with [OSNet](https://github.com/KaiyangZhou/deep-person-reid).
- [RizwanMunawar/yolov8-object-tracking](https://github.com/RizwanMunawar/yolov8-object-tracking) 
: YOLOv8 Object Tracking Using PyTorch, OpenCV and Ultralytics.
- [xuarehere/yolo_series_deepsort_pytorch](https://github.com/xuarehere/yolo_series_deepsort_pytorch) 
: Deepsort with yolo series. This project support the existing yolo detection model algorithm (YOLOv3, YOLOV4, YOLOV4Scaled, YOLOV5, YOLOV6, YOLOV7, YOLOV8, YOLOX, YOLOR, PPYOLOE ).
- [JackWoo0831/Yolov7-tracker](https://github.com/JackWoo0831/Yolov7-tracker) 
: Yolo v7 and several Multi-Object Tracker(SORT, DeepSORT, ByteTrack, BoT-SORT, etc.) in VisDrone2019 Dataset. It uses a unified style and integrated tracker for easy embedding in your own projects. YOLOv7 + 各种tracker实现多目标跟踪。
- [BoT-SORT](https://github.com/NirAharon/BoT-SORT) 
: "BoT-SORT: Robust Associations Multi-Pedestrian Tracking". (**[arXiv 2022](https://arxiv.org/abs/2206.14651)**)
- [StrongSORT](https://github.com/dyhBUPT/StrongSORT) 
: "StrongSORT: Make DeepSORT Great Again". (**[arXiv 2022](https://arxiv.org/abs/2202.13514)**)
- [UAVMOT](https://github.com/LiuShuaiyr/UAVMOT) 
: "Multi-Object Tracking Meets Moving UAV". (**[CVPR 2022](https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Multi-Object_Tracking_Meets_Moving_UAV_CVPR_2022_paper.html)**)
- [HKPolyU-UAV/AUTO](https://github.com/HKPolyU-UAV/AUTO) 
: "Dynamic Object Tracking on Autonomous UAV System for Surveillance Applications". (**[Sensors 2021](https://www.mdpi.com/1424-8220/21/23/7888)**)
- [bharath5673/StrongSORT-YOLO](https://github.com/bharath5673/StrongSORT-YOLO) 
: Real-time multi-camera multi-object tracker using (YOLOv5, YOLOv7) and [StrongSORT](https://github.com/dyhBUPT/StrongSORT) with OSNet.
- [mikel-brostrom/Yolov7_StrongSORT_OSNet](https://github.com/mikel-brostrom/Yolov7_StrongSORT_OSNet) : Real-time multi-camera multi-object tracker using YOLOv7 and StrongSORT with OSNet.
- [kadirnar/yolov5-strongsort](https://github.com/kadirnar/yolov5-strongsort) 
: Minimal PyTorch implementation of YOLOv5 and [StrongSORT](https://github.com/dyhBUPT/StrongSORT).
- [ZQPei/deep_sort_pytorch](https://github.com/ZQPei/deep_sort_pytorch) 
: MOT using deepsort and yolov3 with pytorch.
- [Qidian213/deep_sort_yolov3](https://github.com/Qidian213/deep_sort_yolov3) 
: Real-time Multi-person tracker using YOLO v3 and deep_sort with tensorflow.
- [CSTrack](https://github.com/JudasDie/SOTS) 
: "Rethinking the competition between detection and ReID in Multi-Object Tracking". (**[arXiv 2020](https://arxiv.org/abs/2010.12138)**)
- [ROLO](https://github.com/Guanghan/ROLO) 
: ROLO is short for Recurrent YOLO, aimed at simultaneous object detection and tracking.
- [FastMOT](https://github.com/GeekAlexis/FastMOT) 
: "FastMOT: High-Performance Multiple Object Tracking Based on Deep SORT and KLT". (**[Zenodo 2020](https://doi.org/10.5281/zenodo.4294717)**)
- [Sharpiless/Yolov5-deepsort-inference](https://github.com/Sharpiless/Yolov5-deepsort-inference) 
: 使用YOLOv5+Deepsort实现车辆行人追踪和计数，代码封装成一个Detector类，更容易嵌入到自己的项目中。
- [Sharpiless/Yolov5-Deepsort](https://github.com/Sharpiless/Yolov5-Deepsort) 
: 最新版本yolov5+deepsort目标检测和追踪，能够显示目标类别，支持5.0版本可训练自己数据集。
- [LeonLok/Multi-Camera-Live-Object-Tracking](https://github.com/LeonLok/Multi-Camera-Live-Object-Tracking) 
: Multi-camera live traffic and object counting with YOLO v4, Deep SORT, and Flask.
- [LeonLok/Deep-SORT-YOLOv4](https://github.com/LeonLok/Deep-SORT-YOLOv4) 
: People detection and optional tracking with Tensorflow backend.
- [obendidi/Tracking-with-darkflow](https://github.com/obendidi/Tracking-with-darkflow) 
: Real-time people Multitracker using YOLO v2 and deep_sort with tensorflow.
- [DrewNF/Tensorflow_Object_Tracking_Video](https://github.com/DrewNF/Tensorflow_Object_Tracking_Video) 
: Object Tracking in Tensorflow ( Localization Detection Classification ) developed to partecipate to ImageNET VID competition.
- [dyh/unbox_yolov5_deepsort_counting](https://github.com/dyh/unbox_yolov5_deepsort_counting) 
: yolov5 deepsort 行人 车辆 跟踪 检测 计数。
- [theAIGuysCode/yolov3_deepsort](https://github.com/theAIGuysCode/yolov3_deepsort) 
: Object tracking implemented with YOLOv3, Deep Sort and Tensorflow.
- [weixu000/libtorch-yolov3-deepsort](https://github.com/weixu000/libtorch-yolov3-deepsort) 
: libtorch-yolov3-deepsort.
- [pmj110119/YOLOX_deepsort_tracker](https://github.com/pmj110119/YOLOX_deepsort_tracker) 
: using yolox+deepsort for object-tracking.
- [abhyantrika/nanonets_object_tracking](https://github.com/abhyantrika/nanonets_object_tracking) 
: nanonets_object_tracking.
- [mattzheng/keras-yolov3-KF-objectTracking](https://github.com/mattzheng/keras-yolov3-KF-objectTracking) 
: 以kears-yolov3做detector，以Kalman-Filter算法做tracker，进行多人物目标追踪。
- [rohanchandra30/TrackNPred](https://github.com/rohanchandra30/TrackNPred) 
: A Software Framework for End-to-End Trajectory Prediction.
- [RichardoMrMu/yolov5-deepsort-tensorrt](https://github.com/RichardoMrMu/yolov5-deepsort-tensorrt) 
: A c++ implementation of yolov5 and deepsort.
- [bamwani/car-counting-and-speed-estimation-yolo-sort-python](https://github.com/bamwani/car-counting-and-speed-estimation-yolo-sort-python) 
: This project imlements the following tasks in the project: 1. Vehicle counting, 2. Lane detection. 3.Lane change detection and 4.speed estimation.
- [ArtLabss/tennis-tracking](https://github.com/ArtLabss/tennis-tracking) 
: Open-source Monocular Python HawkEye for Tennis.
- [CaptainEven/YOLOV4_MCMOT](https://github.com/CaptainEven/YOLOV4_MCMOT) 
: Using YOLOV4 as detector for MCMOT.
- [opendatacam/node-moving-things-tracker](https://github.com/opendatacam/node-moving-things-tracker) 
: javascript implementation of "tracker by detections" for realtime multiple object tracking (MOT).
- [lanmengyiyu/yolov5-deepmar](https://github.com/lanmengyiyu/yolov5-deepmar) 
: 行人轨迹和属性分析。
- [zengwb-lx/Yolov5-Deepsort-Fastreid](https://github.com/zengwb-lx/Yolov5-Deepsort-Fastreid) 
: YoloV5 + deepsort + Fast-ReID 完整行人重识别系统。
- [tensorturtle/classy-sort-yolov5](https://github.com/tensorturtle/classy-sort-yolov5) 
: Ready-to-use realtime multi-object tracker that works for any object category. YOLOv5 + SORT implementation.
- [supperted825/FairMOT-X](https://github.com/supperted825/FairMOT-X) 
: FairMOT for Multi-Class MOT using YOLOX as Detector.
- [deyiwang89/pytorch-yolov7-deepsort](https://github.com/deyiwang89/pytorch-yolov7-deepsort) 
: an implentation of yolov7-deepsort based on pytorch.
- [xuarehere/yolovx_deepsort_pytorch](https://github.com/xuarehere/yolovx_deepsort_pytorch) 
: this project support the existing yolo detection model algorithm (YOLOv3, YOLOV4, YOLOV4Scaled, YOLOV5, YOLOV6, YOLOV7 ).
- [deshwalmahesh/yolov7-deepsort-tracking](https://github.com/deshwalmahesh/yolov7-deepsort-tracking) 
: Modular and ready to deploy code to detect and track videos using YOLO-v7 and DeepSORT.
- [RizwanMunawar/yolov7-object-tracking](https://github.com/RizwanMunawar/yolov7-object-tracking) 
: YOLOv7 Object Tracking Using PyTorch, OpenCV and Sort Tracking.
- [RizwanMunawar/yolov5-object-tracking](https://github.com/RizwanMunawar/yolov5-object-tracking) 
: YOLOv5 Object Tracking + Detection + Object Blurring + Streamlit Dashboard Using OpenCV, PyTorch and Streamlit.
- [Smorodov/Multitarget-tracker](https://github.com/Smorodov/Multitarget-tracker) 
: Multiple Object Tracker, Based on Hungarian algorithm + Kalman filter.
- [Naughty-Galileo/YoloV5_MCMOT](https://github.com/Naughty-Galileo/YoloV5_MCMOT) 
: 多类别多目标跟踪YoloV5+sort/deepsort/bytetrack/BotSort/motdt.
- [MuhammadMoinFaisal/YOLOv8-DeepSORT-Object-Tracking](https://github.com/MuhammadMoinFaisal/YOLOv8-DeepSORT-Object-Tracking) 
: YOLOv8 Object Tracking using PyTorch, OpenCV and DeepSORT.
- [sujanshresstha/YOLO-NAS_DeepSORT](https://github.com/sujanshresstha/YOLO-NAS_DeepSORT) 
: This repository contains code for object tracking in videos using the YOLO-NAS object detection model and the DeepSORT algorithm.
- #### Dynamic Object Tracking
##### 动态目标跟踪
- [PolyU-AIRO-Lab/AUTO](https://github.com/PolyU-AIRO-Lab/AUTO) 
: "Dynamic Object Tracking on Autonomous UAV System for Surveillance Applications". (**[Sensors 2021](https://www.mdpi.com/1424-8220/21/23/7888)**)
- #### Deep Reinforcement Learning
#### 深度强化学习
- [uzkent/EfficientObjectDetection](https://github.com/uzkent/EfficientObjectDetection) 
: "Efficient Object Detection in Large Images with Deep Reinforcement Learning". (**[WACV 2020](https://openaccess.thecvf.com/content_WACV_2020/html/Uzkent_Efficient_Object_Detection_in_Large_Images_Using_Deep_Reinforcement_Learning_WACV_2020_paper.html)**)
- #### Motion Control Field
#### 运动控制领域
- [icns-distributed-cloud/adaptive-cruise-control](https://github.com/icns-distributed-cloud/adaptive-cruise-control) 
: YOLO-v5 기반 "단안 카메라"의 영상을 활용해 차간 거리를 일정하게 유지하며 주행하는 Adaptive Cruise Control 기능 구현.
- [LeBronLiHD/ZJU2021_MotionControl_PID_YOLOv5](https://github.com/LeBronLiHD/ZJU2021_MotionControl_PID_YOLOv5) 
: ZJU2021_MotionControl_PID_YOLOv5.
- [SananSuleymanov/PID_YOLOv5s_ROS_Diver_Tracking](https://github.com/SananSuleymanov/PID_YOLOv5s_ROS_Diver_Tracking) 
: PID_YOLOv5s_ROS_Diver_Tracking.
- [sumght-z/apex_yolov5](https://github.com/sumght-z/apex_yolov5) 
: something by yolov5 and PID.
- #### Super-Resolution Field
#### 超分辨率领域
- [Fireboltz/Psychic-CCTV](https://github.com/Fireboltz/Psychic-CCTV) 
: A video analysis tool built completely in python.
- #### Spiking Neural Network
#### SNN, 脉冲神经网络
- [SpikeYOLO](https://github.com/BICLab/SpikeYOLO) 
: Offical implementation of "Integer-Valued Training and Spike-Driven Inference Spiking Neural Network for High-performance and Energy-efficient Object Detection" (**[ECCV 2024 Oral](https://arxiv.org/abs/2407.20708)**)
- [EMS-YOLO](https://github.com/BICLab/EMS-YOLO) 
: Offical implementation of "Deep Directly-Trained Spiking Neural Networks for Object Detection" (**[ICCV 2023](https://openaccess.thecvf.com/content/ICCV2023/html/Su_Deep_Directly-Trained_Spiking_Neural_Networks_for_Object_Detection_ICCV_2023_paper.html)**)
- [Attention-SNN](https://github.com/BICLab/Attention-SNN) 
: Offical implementation of "Attention Spiking Neural Networks" (**[IEEE TPAMI 2023](https://ieeexplore.ieee.org/abstract/document/10032591)**)
- [Spike-Driven-Transformer](https://github.com/BICLab/Spike-Driven-Transformer) 
: Offical implementation of "Spike-driven Transformer" (**[NeurIPS 2023](https://openreview.net/forum?id=9FmolyOHi5)**)
- [Spike-Driven-Transformer-V2](https://github.com/BICLab/Spike-Driven-Transformer-V2) 
: Offical implementation of "Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips" (**[ICLR 2024](https://openreview.net/forum?id=1SIBN5Xyw7)**)
- [Spiking-YOLOv3](https://github.com/cwq159/PyTorch-Spiking-YOLOv3) 
: A PyTorch implementation of Spiking-YOLOv3. Two branches are provided, based on two common PyTorch implementation of YOLOv3([ultralytics/yolov3](https://github.com/ultralytics/yolov3) & [eriklindernoren/PyTorch-YOLOv3](https://github.com/eriklindernoren/PyTorch-YOLOv3)), with support for Spiking-YOLOv3-Tiny at present. (**[AAAI 2020](https://ojs.aaai.org/index.php/AAAI/article/view/6787)**)
- [fjcu-ee-islab/Spiking_Converted_YOLOv4](https://github.com/fjcu-ee-islab/Spiking_Converted_YOLOv4) 
: Object Detection Based on Dynamic Vision Sensor with Spiking Neural Network.
- [Zaabon/spiking_yolo](https://github.com/Zaabon/spiking_yolo) 
: This project is a combined neural network utilizing an spiking CNN with backpropagation and YOLOv3 for object detection.
- [Dignity-ghost/PyTorch-Spiking-YOLOv3](https://github.com/Dignity-ghost/PyTorch-Spiking-YOLOv3) 
: A modified repository based on [Spiking-YOLOv3](https://github.com/cwq159/PyTorch-Spiking-YOLOv3) and [YOLOv3](https://pjreddie.com/darknet/yolo), which makes it suitable for VOC-dataset and YOLOv2.
- [beauty-girl-cxy/spiking-yolov5](https://github.com/beauty-girl-cxy/spiking-yolov5) 
: spiking-yolov5.
- #### Attention and Transformer
#### 注意力机制
- [xmu-xiaoma666/External-Attention-pytorch](https://github.com/xmu-xiaoma666/External-Attention-pytorch) 
: 🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐.
- [MenghaoGuo/Awesome-Vision-Attentions](https://github.com/MenghaoGuo/Awesome-Vision-Attentions) 
: Summary of related papers on visual attention. Related code will be released based on Jittor gradually. "Attention Mechanisms in Computer Vision: A Survey". (**[arXiv 2021](https://arxiv.org/abs/2111.07624)**)
- [pprp/awesome-attention-mechanism-in-cv](https://github.com/pprp/awesome-attention-mechanism-in-cv) 
: 👊 CV中常用注意力模块;即插即用模块;ViT模型. PyTorch Implementation Collection of Attention Module and Plug&Play Module.
- [AbSViT](https://github.com/bfshi/AbSViT) 
: "Top-Down Visual Attention from Analysis by Synthesis". (**[CVPR 2023](https://arxiv.org/abs/2303.13043)**). "微信公众号「人工智能前沿讲习」《[【源头活水】CVPR 2023 | AbSViT：拥有自上而下注意力机制的视觉Transformer](https://mp.weixin.qq.com/s/FtVd37tOXMfu92eDSvdvbg)》"。 "微信公众号「极市平台」《[CVPR23 Highlight｜拥有top-down attention能力的vision transformer](https://mp.weixin.qq.com/s/UMA3Vk9L71zUEtNkCshYBg)》"。
- [HaloTrouvaille/YOLO-Multi-Backbones-Attention](https://github.com/HaloTrouvaille/YOLO-Multi-Backbones-Attention) 
: This Repository includes YOLOv3 with some lightweight backbones (ShuffleNetV2, GhostNet, VoVNet), some computer vision attention mechanism (SE Block, CBAM Block, ECA Block), pruning,quantization and distillation for GhostNet.
- [kay-cottage/CoordAttention_YOLOX_Pytorch](https://github.com/kay-cottage/CoordAttention_YOLOX_Pytorch) 
: CoordAttention_YOLOX(基于CoordAttention坐标注意力机制的改进版YOLOX目标检测平台）。 "Coordinate Attention for Efficient Mobile Network Design". (**[CVPR 2021](https://openaccess.thecvf.com/content/CVPR2021/html/Hou_Coordinate_Attention_for_Efficient_Mobile_Network_Design_CVPR_2021_paper.html), [ Andrew-Qibin/CoordAttention](https://github.com/Andrew-Qibin/CoordAttention)**)
- [liangzhendong123/Attention-yolov5](https://github.com/liangzhendong123/Attention-yolov5) 
: 基于注意力机制改进的yolov5模型。
- [e96031413/AA-YOLO](https://github.com/e96031413/AA-YOLO) 
: Attention ALL-CNN Twin Head YOLO (AA -YOLO). "Improving Tiny YOLO with Fewer Model Parameters". (**[IEEE BigMM 2021](https://ieeexplore.ieee.org/abstract/document/9643269/)**)
- [anonymoussss/YOLOX-SwinTransformer](https://github.com/anonymoussss/YOLOX-SwinTransformer) 
: YOLOX with Swin-Transformer backbone.
- [GuanRunwei/MAN-and-CAT](https://github.com/GuanRunwei/MAN-and-CAT) 
: "MAN and CAT: mix attention to nn and concatenate attention to YOLO". (**[ The Journal of Supercomputing, 2022](https://link.springer.com/article/10.1007/s11227-022-04726-7)**)
- ### Small Object Detection
#### 小目标检测
- [kuanhungchen/awesome-tiny-object-detection](https://github.com/kuanhungchen/awesome-tiny-object-detection) 
: 🕶 A curated list of Tiny Object Detection papers and related resources.
- [Koldim2001/YOLO-Patch-Based-Inference](https://github.com/Koldim2001/YOLO-Patch-Based-Inference) 
: Python library for YOLO small object detection and instance segmentation. This Python library simplifies SAHI-like inference for instance segmentation tasks, enabling the detection of small objects in images. It caters to both object detection and instance segmentation tasks, supporting a wide range of Ultralytics models.
- [SAHI](https://github.com/obss/sahi) 
: "Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection". (**[arXiv 2022](https://arxiv.org/abs/2202.06934v2), [Zenodo 2021](https://doi.org/10.5281/zenodo.5718950)**). A lightweight vision library for performing large scale object detection/ instance segmentation. SAHI currently supports [YOLOv5 models](https://github.com/ultralytics/yolov5/releases), [MMDetection models](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/model_zoo.md), [Detectron2 models](https://github.com/facebookresearch/detectron2/blob/main/MODEL_ZOO.md), [HuggingFace models](https://huggingface.co/models?pipeline_tag=object-detection&sort=downloads) and [TorchVision models](https://pytorch.org/docs/stable/torchvision/models.html).
- [Slim-neck by GSConv](https://github.com/AlanLi1997/slim-neck-by-gsconv) 
: "Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles". (**[arXiv 2022](https://arxiv.org/abs/2206.02424)**)
- [hustvl/TinyDet](https://github.com/hustvl/TinyDet) 
: "TinyDet: accurately detecting small objects within 1 GFLOPs". (**[Science China Information Sciences, 2023](https://link.springer.com/article/10.1007/s11432-021-3504-4)**)
- [QueryDet](https://github.com/ChenhongyiYang/QueryDet-PyTorch) 
: "QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection". (**[CVPR 2022](https://openaccess.thecvf.com/content/CVPR2022/html/Yang_QueryDet_Cascaded_Sparse_Query_for_Accelerating_High-Resolution_Small_Object_Detection_CVPR_2022_paper.html)**)
- [RFLA](https://github.com/Chasel-Tsui/mmdet-rfla) 
: "RFLA: Gaussian Receptive Field based Label Assignment for Tiny Object Detection". (**[ECCV 2022](https://arxiv.org/abs/2208.08738)**). "微信公众号「CV技术指南」《[ECCV 2022 | RFLA：基于高斯感受野的微小目标检测标签分配](https://mp.weixin.qq.com/s/h0J775I3D6zoTIeaJRnFgQ)》"
- [YOLT](https://github.com/avanetten/yolt) 
: "You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery". (**[arXiv 2018](https://arxiv.org/abs/1805.09512)**). "微信公众号「江大白」《[基于大尺寸图像的小目标检测竞赛经验总结](https://mp.weixin.qq.com/s?__biz=Mzg5NzgyNTU2Mg==&mid=2247498265&idx=1&sn=1eee95f8f4d09d761dc7b94f4ac55c34&source=41#wechat_redirect)》"
- [SIMRDWN](https://github.com/avanetten/simrdwn) 
: "Satellite Imagery Multiscale Rapid Detection with Windowed Networks". (**[arXiv 2018](https://arxiv.org/abs/1809.09978), [WACV 2019](https://ieeexplore.ieee.org/abstract/document/8659155)**)
- [YOLTv5](https://github.com/avanetten/yoltv5) 
: YOLTv5 builds upon [YOLT](https://github.com/avanetten/yolt) and [SIMRDWN](https://github.com/avanetten/simrdwn), and updates these frameworks to use the [ultralytics/yolov5](https://github.com/ultralytics/yolov5) version of the YOLO object detection family.
- [TPH-YOLOv5](https://github.com/cv516Buaa/tph-yolov5) 
: "TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-Captured Scenarios". (**[ICCV 2021](https://openaccess.thecvf.com/content/ICCV2021W/VisDrone/html/Zhu_TPH-YOLOv5_Improved_YOLOv5_Based_on_Transformer_Prediction_Head_for_Object_ICCVW_2021_paper.html)**)
- [mwaseema/Drone-Detection](https://github.com/mwaseema/Drone-Detection) 
: "Dogfight: Detecting Drones from Drones Videos". (**[CVPR 2021](https://openaccess.thecvf.com/content/CVPR2021/html/Ashraf_Dogfight_Detecting_Drones_From_Drones_Videos_CVPR_2021_paper.html)**)
- [CEASA](https://github.com/cuogeihong/ceasc) 
: "Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images". (**[arXiv 2023](https://arxiv.org/abs/2303.14488)**). "微信公众号「集智书童」《[即插即用 | CEASA模块给你所有，小目标精度提升的同时速度也变快了](https://mp.weixin.qq.com/s/-a4Wz04jLHFiAU88pUyDNQ)》"
- [KevinMuyaoGuo/yolov5s_for_satellite_imagery](https://github.com/KevinMuyaoGuo/yolov5s_for_satellite_imagery) 
: 基于YOLOv5的卫星图像目标检测demo | A demo for satellite imagery object detection based on YOLOv5。
- [Hongyu-Yue/yoloV5_modify_smalltarget](https://github.com/Hongyu-Yue/yoloV5_modify_smalltarget) 
: YOLOV5 小目标检测修改版。
- [muyuuuu/Self-Supervise-Object-Detection](https://github.com/muyuuuu/Self-Supervise-Object-Detection) 
: Self-Supervised Object Detection. 水面漂浮垃圾目标检测，分析源码改善 yolox 检测小目标的缺陷，提出自监督算法预训练无标签数据，提升检测性能。
- [swricci/small-boat-detector](https://github.com/swricci/small-boat-detector) 
: Trained yolo v3 model weights and configuration file to detect small boats in satellite imagery.
- [Resham-Sundar/sahi-yolox](https://github.com/Resham-Sundar/sahi-yolox) 
: YoloX with SAHI Implementation.
- YOLO-Z : "YOLO-Z: Improving small object detection in YOLOv5 for autonomous vehicles". (**[arXiv 2021](https://arxiv.org/abs/2112.11798)**). "微信公众号「计算机视觉研究院」《[Yolo-Z：改进的YOLOv5用于小目标检测（附原论文下载）](https://mp.weixin.qq.com/s/ehkUapLOMdDghF2kAoAV4w)》".
- M2S : "A novel Multi to Single Module for small object detection". (**[arXiv 2023](https://arxiv.org/abs/2303.14977)**). "微信公众号「集智书童」《[基于YOLOv5改进再设计 | M2S全面提升小目标精度](https://mp.weixin.qq.com/s/FlKgYYGUHtJAxCF2wrh4NA)》".
- [ultralytics/xview-yolov3](https://github.com/ultralytics/xview-yolov3) 
: xView 2018 Object Detection Challenge: YOLOv3 Training and Inference.
- [inderpreet1390/yolov5-small-target](https://github.com/inderpreet1390/yolov5-small-target) 
: Repository for improved yolov5 for small target detection.
- [AllenSquirrel/YOLOv3_ReSAM](https://github.com/AllenSquirrel/YOLOv3_ReSAM) 
: YOLOv3_ReSAM:A Small Target Detection Method With Spatial Attention Module.
- [kadirnar/yolov5-sahi](https://github.com/kadirnar/yolov5-sahi) 
: Yolov5 Modelini Kullanarak Özel Nesne Eğitimi ve SAHI Kullanımı.
- [kadirnar/Yolov6-SAHI](https://github.com/kadirnar/Yolov6-SAHI) 
: Yolov6-SAHI.
- [zRzRzRzRzRzRzR/Mult-YOLO-alogorithm-of-RoboMaster-Radar-Detection-2023](https://github.com/zRzRzRzRzRzRzR/Mult-YOLO-alogorithm-of-RoboMaster-Radar-Detection-2023) 
: 2023年西交利物浦大学动云科技GMaster战队雷达yolo小目标检测。
- [quantumxiaol/yolov8-small-target-detection](https://github.com/quantumxiaol/yolov8-small-target-detection) 
: 基于yolov8实现小目标检测，在NWPU VHR-10和DOTA上测试。
- [shaunyuan22/SODA](https://github.com/shaunyuan22/SODA) 
: Official code library for SODA: A Large-scale Benchmark for Small Object Detection. "Towards Large-Scale Small Object Detection: Survey and Benchmarks". (**[arXiv 2022](https://arxiv.org/abs/2207.14096)**)
- ### Few-shot Object Detection
#### 少样本目标检测
- [bingykang/Fewshot_Detection](https://github.com/bingykang/Fewshot_Detection) 
: "Few-shot Object Detection via Feature Reweighting". (**[ICCV 2019](https://openaccess.thecvf.com/content_ICCV_2019/html/Kang_Few-Shot_Object_Detection_via_Feature_Reweighting_ICCV_2019_paper.html)**).
- [SSDA-YOLO](https://github.com/hnuzhy/SSDA-YOLO) 
: Codes for my paper "SSDA-YOLO: Semi-supervised Domain Adaptive YOLO for Cross-Domain Object Detection". (**[Computer Vision and Image Understanding, 2023](https://www.sciencedirect.com/science/article/abs/pii/S1077314223000292)**).
- [OneTeacher](https://github.com/luogen1996/OneTeacher) 
: "Towards End-to-end Semi-supervised Learning for One-stage Object Detection". (**[arXiv 2023](https://arxiv.org/abs/2302.11299)**).
- [Efficient Teacher](https://github.com/AlibabaResearch/efficientteacher) 
: "Efficient Teacher: Semi-Supervised Object Detection for YOLOv5". (**[arXiv 2023](https://arxiv.org/abs/2302.07577)**).
- ### Open World Object Detection
#### 开放世界目标检测
- [UniDetector](https://github.com/zhenyuw16/UniDetector) 
: "Detecting Everything in the Open World: Towards Universal Object Detection". (**[CVPR 2023](https://arxiv.org/abs/2303.11749)**). "微信公众号「我爱计算机视觉」《[CVPR 2023 | 标注500类，检测7000类！清华大学等提出通用目标价测算法UniDetector](https://mp.weixin.qq.com/s/r7N8X_8riboCvafl9f1vDQ)》". "微信公众号「自动驾驶之心」《[CVPR 2023｜UniDetector：7000类通用目标检测算法（港大&清华）](https://mp.weixin.qq.com/s/iRe4RhSEm4Oe4DxKX5wu9w)》"
- [buxihuo/OW-YOLO](https://github.com/buxihuo/OW-YOLO) 
: Detect known and unknown objects in the open world（具有区分已知与未知能力的全新检测器））.
- ### Oriented Object Detection
#### 旋转目标检测
- [AlphaRotate](https://github.com/yangxue0827/RotationDetection) 
: "AlphaRotate: A Rotation Detection Benchmark using TensorFlow". (**[arXiv 2021](https://arxiv.org/abs/2111.06677)**)
- [hukaixuan19970627/yolov5_obb](https://github.com/hukaixuan19970627/yolov5_obb) 
: yolov5 + csl_label.(Oriented Object Detection)（Rotation Detection）（Rotated BBox）基于yolov5的旋转目标检测。
- [BossZard/rotation-yolov5](https://github.com/BossZard/rotation-yolov5) 
: rotation detection based on yolov5.
- [acai66/yolov5_rotation](https://github.com/acai66/yolov5_rotation) 
: rotated bbox detection. inspired by [hukaixuan19970627/yolov5_obb](https://github.com/hukaixuan19970627/yolov5_obb), thanks hukaixuan19970627.
- [ming71/rotate-yolov3](https://github.com/ming71/rotate-yolov3) 
: Arbitrary oriented object detection implemented with yolov3 (attached with some tricks).
- [ming71/yolov3-polygon](https://github.com/ming71/yolov3-polygon) 
: Arbitrary-oriented object detection based on yolov3.
- [kunnnnethan/R-YOLOv4](https://github.com/kunnnnethan/R-YOLOv4) 
: This is a PyTorch-based R-YOLOv4 implementation which combines YOLOv4 model and loss function from R3Det for arbitrary oriented object detection.
- [XinzeLee/PolygonObjectDetection](https://github.com/XinzeLee/PolygonObjectDetection) 
: This repository is based on Ultralytics/yolov5, with adjustments to enable polygon prediction boxes.
- [hukaixuan19970627/DOTA_devkit_YOLO](https://github.com/hukaixuan19970627/DOTA_devkit_YOLO) 
: Trans DOTA OBB format(poly format) to YOLO format.
- [hpc203/rotate-yolov5-opencv-onnxrun](https://github.com/hpc203/rotate-yolov5-opencv-onnxrun) 
: 分别使用OpenCV、ONNXRuntime部署yolov5旋转目标检测，包含C++和Python两个版本的程序。
- [hpc203/rotateyolov5-opencv-onnxrun](https://github.com/hpc203/rotateyolov5-opencv-onnxrun) 
: 分别使用OpenCV，ONNXRuntime部署yolov5旋转目标检测，包含C++和Python两个版本的程序。
- [kunnnnethan/R-YOLOv4](https://github.com/kunnnnethan/R-YOLOv4) : This is a PyTorch-based R-YOLOv4 implementation which combines YOLOv4 model and loss function from R3Det for arbitrary oriented object detection.
- [DDGRCF/YOLOX_OBB](https://github.com/DDGRCF/YOLOX_OBB) 
: YOLOX OBB -- YOLOX 旋转框 | 实例分割。 "知乎「刀刀狗」《[YOLOX OBB -- YOLOX 旋转框检测 超详细！！！](https://zhuanlan.zhihu.com/p/430850089)》"。
- ### Face Detection and Recognition
#### 人脸检测与识别
- #### Face Detection
##### 人脸检测
- [YOLO5Face](https://github.com/deepcam-cn/yolov5-face) 
: "YOLO5Face: Why Reinventing a Face Detector". (**[arXiv 2021](https://arxiv.org/abs/2105.12931)**)
- [derronqi/yolov7-face](https://github.com/derronqi/yolov7-face) 
: yolov7 face detection with landmark.
- [derronqi/yolov8-face](https://github.com/derronqi/yolov8-face) 
: yolov8 face detection with landmark.
- [we0091234/yolov7-face-tensorrt](https://github.com/we0091234/yolov7-face-tensorrt) 
: yolov7-face TensorRT.
- [YOLO-FaceV2](https://github.com/Krasjet-Yu/YOLO-FaceV2) 
: "YOLO-FaceV2: A Scale and Occlusion Aware Face Detector ". (**[arXiv 2022](https://arxiv.org/abs/2208.02019)**). "微信公众号「江大白」《[超越Yolo5-Face，Yolo-Facev2开源，各类Trick优化，值得学习！](https://mp.weixin.qq.com/s?__biz=Mzg5NzgyNTU2Mg==&mid=2247498561&idx=1&sn=b7ff0592644ab6bc5b716e07294e1c0a&source=41#wechat_redirect)》"
- [OAID/TengineKit](https://github.com/OAID/TengineKit) 
: TengineKit - Free, Fast, Easy, Real-Time Face Detection & Face Landmarks & Face Attributes & Hand Detection & Hand Landmarks & Body Detection & Body Landmarks & Iris Landmarks & Yolov5 SDK On Mobile.
- [xialuxi/yolov5_face_landmark](https://github.com/xialuxi/yolov5_face_landmark) 
: 基于yolov5的人脸检测，带关键点检测。
- [sthanhng/yoloface](https://github.com/sthanhng/yoloface) 
: Deep learning-based Face detection using the YOLOv3 algorithm.
- [DayBreak-u/yolo-face-with-landmark](https://github.com/DayBreak-u/yolo-face-with-landmark) 
: yoloface大礼包 使用pytroch实现的基于yolov3的轻量级人脸检测（包含关键点）。
- [abars/YoloKerasFaceDetection](https://github.com/abars/YoloKerasFaceDetection) 
: Face Detection and Gender and Age Classification using Keras.
- [dannyblueliu/YOLO-Face-detection](https://github.com/dannyblueliu/YOLO-Face-detection) 
: Face detection based on YOLO darknet.
- [wmylxmj/YOLO-V3-IOU](https://github.com/wmylxmj/YOLO-V3-IOU) 
: YOLO3 动漫人脸检测 (Based on keras and tensorflow) 2019-1-19.
- [pranoyr/head-detection-using-yolo](https://github.com/pranoyr/head-detection-using-yolo) 
: Detection of head using YOLO.
- [grapeot/AnimeHeadDetector](https://github.com/grapeot/AnimeHeadDetector) 
: An object detector for character heads in animes, based on Yolo V3.
- [Chenyang-ZHU/YOLOv3-Based-Face-Detection-Tracking](https://github.com/Chenyang-ZHU/YOLOv3-Based-Face-Detection-Tracking) 
: This is a robot project for television live. System will tracking the host's face, making the face in the middle of the screen.
- [zdfb/Yolov5_face](https://github.com/zdfb/Yolov5_face) 
: 基于pytorch的Yolov5人脸检测。
- [jinfagang/yolov7-face](https://github.com/jinfagang/yolov7-face) 
: Next Gen Face detection based on YOLOv7.
- [Yusepp/YOLOv8-Face](https://github.com/Yusepp/YOLOv8-Face) 
: YOLOv8 for Face Detection.
- #### Face Recognition
##### 人脸识别
- [ChanChiChoi/awesome-Face_Recognition](https://github.com/ChanChiChoi/awesome-Face_Recognition) 
: papers about Face Detection; Face Alignment; Face Recognition && Face Identification && Face Verification && Face Representation; Face Reconstruction; Face Tracking; Face Super-Resolution && Face Deblurring; Face Generation && Face Synthesis; Face Transfer; Face Anti-Spoofing; Face Retrieval.
- [hpc203/10kinds-light-face-detector-align-recognition](https://github.com/hpc203/10kinds-light-face-detector-align-recognition) 
: 10种轻量级人脸检测算法的比拼，其中还包含人脸关键点检测与对齐，人脸特征向量提取和计算距离相似度。
- [ooooxianyu/yoloV5-arcface_forlearn](https://github.com/ooooxianyu/yoloV5-arcface_forlearn) 
: 简单拼接一些源码，实现的人脸识别项目。可供学习参考。具体使用到：yolov5人脸检测、arcface人脸识别。
- [zhouyuchong/face-recognition-deepstream](https://github.com/zhouyuchong/face-recognition-deepstream) 
: Deepstream app use YOLO, retinaface and arcface for face recognition.
- [duckzhao/face_detection_and_recognition_yolov5](https://github.com/duckzhao/face_detection_and_recognition_yolov5) 
: 使用yolov5构建人脸检测模型，使用预训练的Arcface完成人脸特征提取和识别。
- [PhucNDA/FaceID--YOLOV5.ArcFace](https://github.com/PhucNDA/FaceID--YOLOV5.ArcFace) 
: ONNX implementation of YOLOv5 and Siamese Network (ResNet100) with ArcFace loss for Face Detection and Recognition.
- ### Face Mask Detection
#### 口罩检测
- [Bil369/MaskDetect-YOLOv4-PyTorch](https://github.com/Bil369/MaskDetect-YOLOv4-PyTorch) 
: 基于PyTorch&YOLOv4实现的口罩佩戴检测 ⭐ 自建口罩数据集分享。
- [adityap27/face-mask-detector](https://github.com/adityap27/face-mask-detector) 
: 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐅𝐚𝐜𝐞 𝐦𝐚𝐬𝐤 𝐝𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐮𝐬𝐢𝐧𝐠 𝐝𝐞𝐞𝐩𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐀𝐥𝐞𝐫𝐭 𝐬𝐲𝐬𝐭𝐞𝐦 💻🔔.
- [VictorLin000/YOLOv3_mask_detect](https://github.com/VictorLin000/YOLOv3_mask_detect) 
: Face mask detection using YOLOv3 on GoogleColab.
- [amh28/IBM-Data-Science-Capstone-Alejandra-Marquez](https://github.com/amh28/IBM-Data-Science-Capstone-Alejandra-Marquez) 
: Homemade face mask detector fine-tuning a Yolo-v3 network.
- [LorenRd/JetsonYolov4](https://github.com/LorenRd/JetsonYolov4) 
: Face Mask Yolov4 detector - Nvidia Jetson Nano.
- [Backl1ght/yolov4_face_mask_detection](https://github.com/Backl1ght/yolov4_face_mask_detection) 
: 基于yolov4实现口罩佩戴检测，在验证集上做到了0.954的mAP。
- [pritul2/yolov5_FaceMask](https://github.com/pritul2/yolov5_FaceMask) 
: Detecting person with or without face mask. Trained using YOLOv5.
- [NisargPethani/FACE-MASK-DETECTION-USING-YOLO-V3](https://github.com/NisargPethani/FACE-MASK-DETECTION-USING-YOLO-V3) 
: FACE-MASK DETECTION.
- [waittim/mask-detector](https://github.com/waittim/mask-detector) 
: Real-time video streaming mask detection based on Python. Designed to defeat COVID-19.
- [BogdanMarghescu/Face-Mask-Detection-Using-YOLOv4](https://github.com/BogdanMarghescu/Face-Mask-Detection-Using-YOLOv4) 
: Face Mask Detector using YOLOv4.
- [xinghanliuying/yolov5_bus](https://github.com/xinghanliuying/yolov5_bus) 
: 手把手教你使用YOLOV5训练自己的目标检测模型。
- [song-laogou/yolov5-mask-42](https://gitee.com/song-laogou/yolov5-mask-42) : 基于YOLOV5的口罩检测系统-提供教学视频。
- ### Social Distance Detection
#### 社交距离检测
- [Ank-Cha/Social-Distancing-Analyser-COVID-19](https://github.com/Ank-Cha/Social-Distancing-Analyser-COVID-19) 
: Social Distancing Analyser to prevent COVID19.
- [abd-shoumik/Social-distance-detection](https://github.com/abd-shoumik/Social-distance-detection) 
: Social distance detection, a deep learning computer vision project with yolo object detection.
- [ChargedMonk/Social-Distancing-using-YOLOv5](https://github.com/ChargedMonk/Social-Distancing-using-YOLOv5) 
: Classifying people as high risk and low risk based on their distance to other people.
- [JohnBetaCode/Social-Distancing-Analyser](https://github.com/JohnBetaCode/Social-Distancing-Analyser) 
: Social Distancing Analyzer.
- [Ashamaria/Safe-distance-tracker-using-YOLOv3-v3](https://github.com/Ashamaria/Safe-distance-tracker-using-YOLOv3-v3) 
: Safe Distance Tracker.
- ### Autonomous Driving Field Detection
#### 自动驾驶领域检测
- #### Vehicle Detection
##### 车辆检测
- [jason-li-831202/Vehicle-CV-ADAS](https://github.com/jason-li-831202/Vehicle-CV-ADAS) 
: The project can achieve FCWS, LDWS, and LKAS functions solely using only visual sensors. using YOLOv5 / YOLOv5-lite / YOLOv6 / YOLOv7 / YOLOv8 / YOLOv9 / EfficientDet and Ultra-Fast-Lane-Detection-v2.
- [williamhyin/yolov5s_bdd100k](https://github.com/williamhyin/yolov5s_bdd100k) 
: Train a yolo v5 object detection model on Bdd100k dataset.
- [Gaussian_YOLOv3](https://github.com/jwchoi384/Gaussian_YOLOv3) 
: "Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving". (**[ICCV 2019](https://openaccess.thecvf.com/content_ICCV_2019/html/Choi_Gaussian_YOLOv3_An_Accurate_and_Fast_Object_Detector_Using_Localization_ICCV_2019_paper.html)**)
- [streamlit/demo-self-driving](https://github.com/streamlit/demo-self-driving) 
: Streamlit app demonstrating an image browser for the Udacity self-driving-car dataset with realtime object detection using YOLO.
- [JunshengFu/vehicle-detection](https://github.com/JunshengFu/vehicle-detection) 
: Created vehicle detection pipeline with two approaches: (1) deep neural networks (YOLO framework) and (2) support vector machines ( OpenCV + HOG).
- [xslittlegrass/CarND-Vehicle-Detection](https://github.com/xslittlegrass/CarND-Vehicle-Detection) 
: Vehicle detection using YOLO in Keras runs at 21FPS.
- [Kevinnan-teen/Intelligent-Traffic-Based-On-CV](https://github.com/Kevinnan-teen/Intelligent-Traffic-Based-On-CV) 
: 基于计算机视觉的交通路口智能监控系统。
- [subodh-malgonde/vehicle-detection](https://github.com/subodh-malgonde/vehicle-detection) 
: Detect vehicles in a video.
- [CaptainEven/Vehicle-Car-detection-and-multilabel-classification](https://github.com/CaptainEven/Vehicle-Car-detection-and-multilabel-classification) 
: 使用YOLO_v3_tiny和B-CNN实现街头车辆的检测和车辆属性的多标签识别 Using yolo_v3_tiny to do vehicle or car detection and attribute's multilabel classification or recognize。
- [kaylode/vehicle-counting](https://github.com/kaylode/vehicle-counting) 
: Vehicle counting using Pytorch.
- [MaryamBoneh/Vehicle-Detection](https://github.com/MaryamBoneh/Vehicle-Detection) 
: Vehicle Detection Using Deep Learning and YOLO Algorithm.
- [JeffWang0325/Image-Identification-for-Self-Driving-Cars](https://github.com/JeffWang0325/Image-Identification-for-Self-Driving-Cars) 
: This project achieves some functions of image identification for Self-Driving Cars.
- [AnarbekovAlt/Traffic-analysis](https://github.com/AnarbekovAlt/Traffic-analysis) 
: A traffic analysis system is built on the basis of the YOLO network.
- [ruhyadi/yolov5-nodeflux](https://github.com/ruhyadi/yolov5-nodeflux) 
: YOLOv5 Nodeflux Vehicle Detection.
- [Daheer/Driving-Environment-Detector](https://github.com/Daheer/Driving-Environment-Detector) 
: Detecting road objects using YOLO CNN Architecture.
- [georgia-tech-db/eva](https://github.com/georgia-tech-db/eva) 
: Exploratory Video Analytics System.
- [heathhenley/RhodyCarCounter](https://github.com/heathhenley/RhodyCarCounter) 
: An app that uses Yolo to count the cars passing by traffic cams mostly in the Providence, RI area.
- [zehengl/yyc-traffic-cam](https://github.com/zehengl/yyc-traffic-cam) 
: A demo to detect vehicles in traffic cam. [zehengl.github.io/yyc-traffic-cam/](https://zehengl.github.io/yyc-traffic-cam/)
- [ruhyadi/vehicle-detection-yolov8](https://github.com/ruhyadi/vehicle-detection-yolov8) 
: Vehicle Detection with YOLOv8.
- #### License Plate Detection and Recognition
##### 车牌检测与识别
- [zeusees/License-Plate-Detector](https://github.com/zeusees/License-Plate-Detector) 
: License Plate Detection with Yolov5，基于Yolov5车牌检测。
- [TheophileBuy/LicensePlateRecognition](https://github.com/TheophileBuy/LicensePlateRecognition) 
: License Plate Recognition.
- [alitourani/yolo-license-plate-detection](https://github.com/alitourani/yolo-license-plate-detection) 
: A License-Plate detecttion application based on YOLO.
- [HuKai97/YOLOv5-LPRNet-Licence-Recognition](https://github.com/HuKai97/YOLOv5-LPRNet-Licence-Recognition) 
: 使用YOLOv5和LPRNet进行车牌检测+识别（CCPD数据集）。
- [xialuxi/yolov5-car-plate](https://github.com/xialuxi/yolov5-car-plate) 
: 基于yolov5的车牌检测，包含车牌角点检测。
- [kyrielw24/License_Plate_Recognition](https://github.com/kyrielw24/License_Plate_Recognition) 
: 基于Yolo&CNN的车牌识别可视化项目。
- [we0091234/yolov7_plate](https://github.com/we0091234/yolov7_plate) 
: yolov7 车牌检测 车牌识别 中文车牌识别 检测 支持双层车牌 支持13种中文车牌。
- [MuhammadMoinFaisal/Automatic_Number_Plate_Detection_Recognition_YOLOv8](https://github.com/MuhammadMoinFaisal/Automatic_Number_Plate_Detection_Recognition_YOLOv8) 
: Automatic Number Plate Detection YOLOv8.
- #### Lane Detection
##### 车道线检测
- [YOLOP](https://github.com/hustvl/YOLOP) 
: "YOLOP: You Only Look Once for Panoptic Driving Perception". (**[arXiv 2021](https://arxiv.org/abs/2108.11250)**).
- [YOLOPv2](https://github.com/CAIC-AD/YOLOPv2) 
: "YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception". (**[arXiv 2022](https://arxiv.org/abs/2208.11434)**). "微信公众号「集智书童」《[YOLOP v2来啦 | YOLOv7结合YOLOP的多任务版本，超越YOLOP以及HybridNets](https://mp.weixin.qq.com/s/XTD32JCu_YbZjV2Br3KXCA)》"
- [FeiGeChuanShu/YOLOPv2-ncnn](https://github.com/FeiGeChuanShu/YOLOPv2-ncnn) 
: YOLOPv2-ncnn.
- [visualbuffer/copilot](https://github.com/visualbuffer/copilot) 
: Lane and obstacle detection for active assistance during driving.
- [hpc203/YOLOP-opencv-dnn](https://github.com/hpc203/YOLOP-opencv-dnn) 
: 使用OpenCV部署全景驾驶感知网络YOLOP，可同时处理交通目标检测、可驾驶区域分割、车道线检测，三项视觉感知任务。
- [EdVince/YOLOP-NCNN](https://github.com/EdVince/YOLOP-NCNN) 
: YOLOP running in Android by ncnn.
- #### Driving Behavior Detection
##### 驾驶行为检测
- [JingyibySUTsoftware/Yolov5-deepsort-driverDistracted-driving-behavior-detection](https://github.com/JingyibySUTsoftware/Yolov5-deepsort-driverDistracted-driving-behavior-detection) 
: 基于深度学习的驾驶员分心驾驶行为（疲劳+危险行为）预警系统使用YOLOv5+Deepsort实现驾驶员的危险驾驶行为的预警监测。
- [Arrowes/CEAM-YOLOv7](https://github.com/Arrowes/CEAM-YOLOv7) 
: "CEAM-YOLOv7:Improved YOLOv7 Based on Channel Expansion and Attention Mechanism for Driver Distraction Behavior Detection". (**[IEEE Access, 2022](https://ieeexplore.ieee.org/abstract/document/9980374/)**).
- #### Parking Slot Detection
##### 停车位检测
- [visualbuffer/parkingslot](https://github.com/visualbuffer/parkingslot) 
: Automated parking occupancy detection.
- [anil2k/smart-car-parking-yolov5](https://github.com/anil2k/smart-car-parking-yolov5) 
: Detect free parking lot available for cars.
- #### Traffic Light Detection
##### 交通灯检测
- [berktepebag/Traffic-light-detection-with-YOLOv3-BOSCH-traffic-light-dataset](https://github.com/berktepebag/Traffic-light-detection-with-YOLOv3-BOSCH-traffic-light-dataset) 
: Detecting Traffic Lights in Real-time with YOLOv3.
- [mihir-m-gandhi/Adaptive-Traffic-Signal-Timer](https://github.com/mihir-m-gandhi/Adaptive-Traffic-Signal-Timer) 
: This Adaptive Traffic Signal Timer uses live images from the cameras at traffic junctions for real-time traffic density calculation using YOLO object detection and sets the signal timers accordingly.
- [wade0125/Traffic_Light_Detection_Yolo](https://github.com/wade0125/Traffic_Light_Detection_Yolo) 
: Traffic Light Detection Yolo.
- [LIU42/PassingRules](https://github.com/LIU42/PassingRules) 
: 一种基于 YOLOv8 的路口交通信号灯通行规则识别模型及算法.
- #### Traffic Sign Detection
##### 交通标志检测
- [Ai-trainee/Traffic-Sign-Recognition-PyQt5-YOLOv5-GUI](https://github.com/Ai-trainee/Traffic-Sign-Recognition-PyQt5-YOLOv5-GUI) 
: Road Sign Recognition Project Based on YOLOv5. This is a road sign recognition project based on YOLOv5, developed with a PyQt5 interface, YOLOv5 trained model, and MySQL database. 这是一个基于YOLOv5🚀的道路标志识别系统😊，使用了MySQL数据库💽，PyQt5进行界面设计🎨，PyTorch深度学习框架和TensorRT进行加速⚡，同时包含了CSS样式🌈。系统由五个主要模块组成：系统登录模块🔑负责用户登陆；初始化参数模块📋提供YOLOv5模型的初始化参数设置；标志识别模块🔍是系统的核心，负责对道路标志进行识别并将结果导入数据库；数据库模块💾包含基本数据库操作和数据分析两个子模块；图像处理模块🖼️负责单个图像的处理和数据增强。整个系统支持多种数据输入和模型切换，提供了包括mossic和mixup在内的图像增强方法📈。
- [halftop/TT100K_YOLO_Label](https://github.com/halftop/TT100K_YOLO_Label) 
: Tsinghua-Tencent 100K dataset XML and TXT Label.
- [amazingcodeLYL/Traffic_signs_detection_darket](https://github.com/amazingcodeLYL/Traffic_signs_detection_darket) 
: darknet交通标志检测&TT100K数据集。
- [TalkUHulk/yolov3-TT100k](https://github.com/TalkUHulk/yolov3-TT100k) 
: 使用yolov3训练的TT100k(交通标志)模型。
- [TalkUHulk/yolov4-TT100k](https://github.com/TalkUHulk/yolov4-TT100k) 
: 使用yolov4训练的TT100k(交通标志)模型。
- [sarah-antillia/YOLO_Realistic_USA_RoadSigns_160classes](https://github.com/sarah-antillia/YOLO_Realistic_USA_RoadSigns_160classes) 
: USA RoadSigns Dataset 160classes annotated by YOLO format.
- [DickensKP/yolov3-vehicle-pedestrian-trafficsign-detection-system](https://github.com/DickensKP/yolov3-vehicle-pedestrian-trafficsign-detection-system) 
: 基于bubbliiiing的yolov3-pytorch框架，自主训练的车辆、行人、交通标志识别系统.
- [mkrupczak3/Coneslayer](https://github.com/mkrupczak3/Coneslayer) 
: A lightweight neural-network for rapid detection of traffic cones.
- #### Crosswalk Detection
##### 人行横道/斑马线检测
- [CDNet](https://github.com/zhangzhengde0225/CDNet) 
: "CDNet: a real-time and robust crosswalk detection network on Jetson nano based on YOLOv5". (**[Neural Computing and Applications 2022](https://link.springer.com/article/10.1007/s00521-022-07007-9)**). "微信公众号「CVer」《[上海交大提出CDNet：基于改进YOLOv5的斑马线和汽车过线行为检测](https://mp.weixin.qq.com/s/2F3WBtfN_7DkhERMOH8-QA)》"。
- [xN1ckuz/Crosswalks-Detection-using-YoloV5](https://github.com/xN1ckuz/Crosswalks-Detection-using-YoloV5) 
: Crosswalks Detection using YOLO, project for Computer Vision and Machine Perception course at University of Basilicata, Computer Science and Engineering.
- #### Traffic Accidents Detection
##### 交通事故检测
- [khaledsabry97/Argus](https://github.com/khaledsabry97/Argus) 
: "Road Traffic Accidents Detection Based On Crash Estimation". (**[IEEE ICENCO 2021](https://ieeexplore.ieee.org/document/9698968)**)
- #### Road Damage Detection
##### 道路损伤检测
- [adnanmushtaq1996/Yolov4_Road_Damage_Detection](https://github.com/adnanmushtaq1996/Yolov4_Road_Damage_Detection) 
: A Repository to Train a Custom Yolov4 based object detector for road damage detection using the RDD2020 dataset.
- [E-Kozyreva/detection_potholes_yolov8n](https://github.com/E-Kozyreva/detection_potholes_yolov8n) 
: Поиск выбоин на дорогах с использованием YOLOv8 Nano.
- [mounishvatti/pothole_detection_yolov8](https://github.com/mounishvatti/pothole_detection_yolov8) 
: Pothole Detection using Ultralytics YOLOv8
- ### Animal Detection
#### 动物检测
- [SaiSwarup27/Animal-Intrusion-Detection](https://github.com/SaiSwarup27/Animal-Intrusion-Detection) 
: Animal Detection using YOLOv5.
- [xcapt0/animal_recognition](https://github.com/xcapt0/animal_recognition) 
: 🦁 Let the robot recognize the animal instead of you | YOLOv5.
- [PhamDangNguyen/YOLOv5_Animals](https://github.com/PhamDangNguyen/YOLOv5_Animals) 
: YOLOv5 for detection Animals.
- [Sabuj-CSE11/AnimalDetection](https://github.com/Sabuj-CSE11/AnimalDetection) 
: Cat and Dogs detection using YoloV5.
- ### Helmet Detection
#### 头盔/安全帽检测
- [PeterH0323/Smart_Construction](https://github.com/PeterH0323/Smart_Construction) 
: Head Person Helmet Detection on Construction Sites，基于目标检测工地安全帽和禁入危险区域识别系统。
- [Byronnar/tensorflow-serving-yolov3](https://github.com/Byronnar/tensorflow-serving-yolov3) 
: 对原tensorflow-yolov3版本做了许多细节上的改进，增加了TensorFlow-Serving工程部署，训练了多个数据集，包括Visdrone2019, 安全帽等。
- [gengyanlei/reflective-clothes-detect-yolov5](https://github.com/gengyanlei/reflective-clothes-detect-yolov5) 
: reflective-clothes-detect-dataset、helemet detection yolov5、工作服(反光衣)检测数据集、安全帽检测、施工人员穿戴检测。
- [DataXujing/YOLO-V3-Tensorflow](https://github.com/DataXujing/YOLO-V3-Tensorflow) 
: 👷 👷👷 YOLO V3(Tensorflow 1.x) 安全帽 识别 | 提供数据集下载和与预训练模型。
- [rafiuddinkhan/Yolo-Training-GoogleColab](https://github.com/rafiuddinkhan/Yolo-Training-GoogleColab) 
: Helmet Detection using tiny-yolo-v3 by training using your own dataset and testing the results in the google colaboratory.
- [BlcaKHat/yolov3-Helmet-Detection](https://github.com/BlcaKHat/yolov3-Helmet-Detection) 
: Training a YOLOv3 model to detect the presence of helmet for intrusion or traffic monitoring.
- [yumulinfeng1/YOLOv4-Hat-detection](https://github.com/yumulinfeng1/YOLOv4-Hat-detection) 
: 基于YOLOv4的安全帽佩戴检测。
- [FanDady/Helmet-Detection-YoloV5](https://github.com/FanDady/Helmet-Detection-YoloV5) 
: Safety helmet wearing detection on construction site based on YoloV5s-V5.0 including helmet dataset（基于YoloV5-V5.0的工地安全帽检测并且包含开源的安全帽数据集）。
- [RUI-LIU7/Helmet_Detection](https://github.com/RUI-LIU7/Helmet_Detection) 
: 使用yolov5算法实现安全帽以及危险区域的监测，同时接入海康摄像头实现实时监测。
- [ZijianWang1995/PPE_detection](https://github.com/ZijianWang1995/PPE_detection) 
: Real-time PPE detection based on YOLO. Open high-quality dataset. "Fast Personal Protective Equipment Detection for Real Construction Sites Using Deep Learning Approaches". (**[Sensors 2021](https://www.mdpi.com/1424-8220/21/10/3478)**)
- ### Hand Detection
#### 手部检测
- [cansik/yolo-hand-detection](https://github.com/cansik/yolo-hand-detection) 
: A pre-trained YOLO based hand detection network.
- ### Gesture Recognition
#### 手势/手语识别
- [MahmudulAlam/Unified-Gesture-and-Fingertip-Detection](https://github.com/MahmudulAlam/Unified-Gesture-and-Fingertip-Detection) 
: "Unified learning approach for egocentric hand gesture recognition and fingertip detection". (**[Elsevier 2022](https://www.sciencedirect.com/science/article/abs/pii/S0031320321003824)**)
- [insigh1/Interactive_ABCs_with_American_Sign_Language_using_Yolov5](https://github.com/insigh1/Interactive_ABCs_with_American_Sign_Language_using_Yolov5) 
: Interactive ABC's with American Sign Language.
- [Dreaming-future/YOLO-Object-Detection](https://github.com/Dreaming-future/YOLO-Object-Detection) 
: YOLO-Object-Detection 集成多种yolo模型，作为一个模板进行目标检测。
- ### Action Detection
#### 行为检测
- [wufan-tb/yolo_slowfast](https://github.com/wufan-tb/yolo_slowfast) 
: A realtime action detection frame work based on PytorchVideo.
- ### Emotion Recognition
#### 情感识别
- [Tandon-A/emotic](https://github.com/Tandon-A/emotic) 
: "Context based emotion recognition using emotic dataset". (**[arXiv 2020](https://arxiv.org/abs/2003.13401)**)
- ### Human Pose Estimation
#### 人体姿态估计
- [wmcnally/kapao](https://github.com/wmcnally/kapao) 
: KAPAO is a state-of-the-art single-stage human pose estimation model that detects keypoints and poses as objects and fuses the detections to predict human poses. "Rethinking Keypoint Representations: Modeling Keypoints and Poses as Objects for Multi-Person Human Pose Estimation". (**[arXiv 2021](https://arxiv.org/abs/2111.08557)**)
- [TexasInstruments/edgeai-yolov5](https://github.com/TexasInstruments/edgeai-yolov5) 
: "YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss". (**[arXiv 2022](https://arxiv.org/abs/2204.06806)**)
- [TexasInstruments/edgeai-yolox](https://github.com/TexasInstruments/edgeai-yolox) 
: "YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss". (**[arXiv 2022](https://arxiv.org/abs/2204.06806)**)
- [jinfagang/VIBE_yolov5](https://github.com/jinfagang/VIBE_yolov5) 
: Using YOLOv5 as detection on VIBE. "VIBE: Video Inference for Human Body Pose and Shape Estimation". (**[CVPR 2020](https://openaccess.thecvf.com/content_CVPR_2020/html/Kocabas_VIBE_Video_Inference_for_Human_Body_Pose_and_Shape_Estimation_CVPR_2020_paper.html)**)
- [zhuoxiangpang/ism_person_openpose](https://github.com/zhuoxiangpang/ism_person_openpose) 
: yolov5人体检测+openpose姿态检测 实现摔倒检测。
- [pengyang1225/yolov5_person_pose](https://github.com/pengyang1225/yolov5_person_pose) 
: 基于yolov5的person—pose。
- [hpc203/yolov5_pose_opencv](https://github.com/hpc203/yolov5_pose_opencv) 
: 使用OpenCV部署yolov5-pose目标检测+人体姿态估计，包含C++和Python两个版本的程序。支持yolov5s，yolov5m，yolov5l。
- [RizwanMunawar/yolov7-pose-estimation](https://github.com/RizwanMunawar/yolov7-pose-estimation) 
: YOLOv7 Pose estimation using OpenCV, PyTorch.
- [nanmi/yolov7-pose](https://github.com/nanmi/yolov7-pose) 
: pose detection base on yolov7.
- ### Distance Measurement
#### 距离测量
- [davidfrz/yolov5_distance_count](https://github.com/davidfrz/yolov5_distance_count) 
: 通过yolov5实现目标检测+双目摄像头实现距离测量。
- [wenyishengkingkong/realsense-D455-YOLOV5](https://github.com/wenyishengkingkong/realsense-D455-YOLOV5) 
: 利用realsense深度相机实现yolov5目标检测的同时测出距离。
- [Thinkin99/yolov5_d435i_detection](https://github.com/Thinkin99/yolov5_d435i_detection) 
: 使用realsense d435i相机，基于pytorch实现yolov5目标检测，返回检测目标相机坐标系下的位置信息。
- [MUCHWAY/detect_distance_gazebo](https://github.com/MUCHWAY/detect_distance_gazebo) 
: yolov5+camera_distance+gazebo.
- [magisystem0408/yolov5-DeepSort-RealSenseD435i](https://github.com/magisystem0408/yolov5-DeepSort-RealSenseD435i) 
: yolov5+Realsence+DeepSense D435i.
- ### Instance and Semantic Segmentation
#### 实例和语义分割
- [SAM](https://github.com/facebookresearch/segment-anything) 
: The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model. "Segment Anything". (**[arXiv 2023](https://arxiv.org/abs/2304.02643)**).
- [Grounded-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything) 
: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Tag2Text & BLIP & Whisper & ChatBot - Automatically Detect , Segment and Generate Anything with Image, Text, and Audio Inputs. We plan to create a very interesting demo by combining [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO) and [Segment Anything](https://github.com/facebookresearch/segment-anything) which aims to detect and segment Anything with text inputs!
- [Laughing-q/yolov5-q](https://github.com/Laughing-q/yolov5-q) 
: This repo is plan for instance segmentation based on yolov5-6.0 and yolact.
- [TomMao23/multiyolov5](https://github.com/TomMao23/multiyolov5) 
: Multi YOLO V5——Detection and Semantic Segmentation.
- [ArtyZe/yolo_segmentation](https://github.com/ArtyZe/yolo_segmentation) 
: image (semantic segmentation) instance segmentation by darknet or yolo.
- [midasklr/yolov5ds](https://github.com/midasklr/yolov5ds) 
: multi-task yolov5 with detection and segmentation.
- [RizwanMunawar/yolov7-segmentation](https://github.com/RizwanMunawar/yolov7-segmentation) 
: YOLOv7 Instance Segmentation using OpenCV and PyTorch.
- [leandro-svg/Yolov7_Segmentation_Tensorrt](https://github.com/leandro-svg/Yolov7_Segmentation_Tensorrt) 
: The real-time Instance Segmentation Algorithm Yolov7 running on TensoRT and ONNX.
- [akashAD98/YOLOV8_SAM](https://github.com/akashAD98/YOLOV8_SAM) 
: Use yolov8 & SAM model to get segmention for custom model.
- ### 3D Object Detection
#### 三维目标检测
- [ADLab-AutoDrive/BEVFusion](https://github.com/ADLab-AutoDrive/BEVFusion) 
: "BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework". (**[NeurIPS 2022](https://arxiv.org/abs/2205.13790)**).
- [mit-han-lab/bevfusion](https://github.com/mit-han-lab/bevfusion) 
: "BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation". (**[ICRA 2023](https://arxiv.org/abs/2205.13542)**).
- [SAM3D](https://github.com/DYZhang09/SAM3D) 
: "SAM3D: Zero-Shot 3D Object Detection via [Segment Anything](https://github.com/facebookresearch/segment-anything) Model". (**[arXiv 2023](https://arxiv.org/abs/2306.02245)**).
- [maudzung/YOLO3D-YOLOv4-PyTorch](https://github.com/maudzung/YOLO3D-YOLOv4-PyTorch) 
: The PyTorch Implementation based on YOLOv4 of the paper: "YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud". (**[ECCV 2018](https://openaccess.thecvf.com/content_eccv_2018_workshops/w18/html/Ali_YOLO3D_End-to-end_real-time_3D_Oriented_Object_Bounding_Box_Detection_from_ECCVW_2018_paper.html)**)
- [maudzung/Complex-YOLOv4-Pytorch](https://github.com/maudzung/Complex-YOLOv4-Pytorch) 
: The PyTorch Implementation based on YOLOv4 of the paper: "Complex-YOLO: Real-time 3D Object Detection on Point Clouds". (**[arXiv 2018](https://arxiv.org/abs/1803.06199)**)
- [AI-liu/Complex-YOLO](https://github.com/AI-liu/Complex-YOLO) 
: This is an unofficial implementation of "Complex-YOLO: Real-time 3D Object Detection on Point Clouds in pytorch". (**[arXiv 2018](https://arxiv.org/abs/1803.06199)**)
- [ghimiredhikura/Complex-YOLOv3](https://github.com/ghimiredhikura/Complex-YOLOv3) 
: Complete but Unofficial PyTorch Implementation of "Complex-YOLO: Real-time 3D Object Detection on Point Clouds with YoloV3". (**[arXiv 2018](https://arxiv.org/abs/1803.06199)**)
- [ruhyadi/YOLO3D](https://github.com/ruhyadi/YOLO3D) 
: YOLO 3D Object Detection for Autonomous Driving Vehicle. Reference by [skhadem/3D-BoundingBox](https://github.com/skhadem/3D-BoundingBox), "3D Bounding Box Estimation Using Deep Learning and Geometry". (**[CVPR 2017](https://openaccess.thecvf.com/content_cvpr_2017/html/Mousavian_3D_Bounding_Box_CVPR_2017_paper.html)**)
- [ruhyadi/yolo3d-lightning](https://github.com/ruhyadi/yolo3d-lightning) : YOLO for 3D Object Detection.
- [Yuanchu/YOLO3D](https://github.com/Yuanchu/YOLO3D) 
: Implementation of a basic YOLO model for object detection in 3D.
- [EmiyaNing/3D-YOLO](https://github.com/EmiyaNing/3D-YOLO) 
: YOLO v5 for Lidar-based 3D BEV Detection.
- ### SLAM Field Detection
#### SLAM领域检测
- [bijustin/YOLO-DynaSLAM](https://github.com/bijustin/YOLO-DynaSLAM) 
: YOLO Dynamic ORB_SLAM is a visual SLAM system that is robust in dynamic scenarios for RGB-D configuration.
- [BzdTaisa/YoloPlanarSLAM](https://github.com/BzdTaisa/YoloPlanarSLAM) 
: YOLO-Planar-SLAM.
- [saransapmaz/cv-slam-object-determination](https://github.com/saransapmaz/cv-slam-object-determination) 
: Object detection with hector slam and YOLO v3 computer vision algorithm.
- ### Industrial Defect Detection
#### 工业缺陷检测
- [annsonic/Steel_defect](https://github.com/annsonic/Steel_defect) 
: Exercise: Use YOLO to detect hot-rolled steel strip surface defects (NEU-DET dataset).
- [VanillaHours/pcbDefectDetectionYOLO](https://github.com/VanillaHours/pcbDefectDetectionYOLO) 
: PCB defect detection using YOLOv3, on DeepPCB dataset.
- [talisma-cassoma/pcb-components-detection-recognition](https://github.com/talisma-cassoma/pcb-components-detection-recognition) 
: this code shows the train and test of a YOLOV5 convolutional neural network for detection of electronics components.
- [Luckycat518/Yolo-MSAPF](https://github.com/Luckycat518/Yolo-MSAPF) 
: Yolo-MSAPF: Multi-Scale Alignment fusion with Parallel feature Filtering model for high accuracy weld defect detection.
- [JiaLim98/YOLO-PCB](https://github.com/JiaLim98/YOLO-PCB) 
: A Deep Context Learning based PCB Defect Detection Model with Anomalous Trend Alarming System.
- ### SAR Image Detection
#### 合成孔径雷达图像检测
- [humblecoder612/SAR_yolov3](https://github.com/humblecoder612/SAR_yolov3) 
: Best Accruacy:speed ratio SAR Ship detection in the world.
- #### Multispectral Image Fusion Detection
#### 多光谱图像融合检测
- [NVIDIA-AI-IOT/Lidar_AI_Solution](https://github.com/NVIDIA-AI-IOT/Lidar_AI_Solution) 
: This is a highly optimized solution for self-driving 3D-lidar repository. It does a great job of speeding up sparse convolution/CenterPoint/BEVFusion/OSD/Conversion. A project demonstrating Lidar related AI solutions, including three GPU accelerated Lidar/camera DL networks (PointPillars, CenterPoint, BEVFusion) and the related libs (cuPCL, 3D SparseConvolution, YUV2RGB, cuOSD,).
- [SuperYOLO](https://github.com/icey-zhang/SuperYOLO) 
: "SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery". (**[arXiv 2022](https://arxiv.org/abs/2209.13351)**)
- [OrangeSodahub/CRLFnet](https://github.com/OrangeSodahub/CRLFnet) 
: Camera-Radar-Lidar Fusion detection net based on ROS, YOLOv3, OpenPCDet integration.
- [mjoshi07/Visual-Sensor-Fusion](https://github.com/mjoshi07/Visual-Sensor-Fusion) 
: LiDAR Fusion with Vision.
- [DocF/multispectral-object-detection](https://github.com/DocF/multispectral-object-detection) 
: Multispectral Object Detection with Yolov5 and Transformer.
- [MAli-Farooq/Thermal-YOLO](https://github.com/MAli-Farooq/Thermal-YOLO) 
: This study is related to object detection in thermal infrared spectrum using YOLO-V5 framework for ADAS application.
- [Ye-zixiao/Double-YOLO-Kaist](https://github.com/Ye-zixiao/Double-YOLO-Kaist) 
: 一种基于YOLOv3/4的双流混合模态道路行人检测方法🌊💧💦。
- [eralso/yolov5_Visible_Infrared_Vehicle_Detection](https://github.com/eralso/yolov5_Visible_Infrared_Vehicle_Detection) 
: 基于可见光和红外图像的深度学习车辆目标检测。
- [Arrowes/CEAM-YOLOv7](https://github.com/Arrowes/CEAM-YOLOv7) : CEAM-YOLOv7: Improved YOLOv7 Based on Channel Expansion and Attention Mechanism for Driver Distraction Behavior Detection.
- [jere357/yolov5-RGBD](https://github.com/jere357/yolov5-RGBD) 
: "fork" from yolov5 with the possibility of running inferences on RGBD(C) images, work in progress. This repo is not a fork of the original repo bcs i already have 1 fork with a PR pending, this is still messy code and a work in progress.
- ### Safety Monitoring Field Detection
#### 安防监控领域检测
- [gengyanlei/fire-smoke-detect-yolov4](https://github.com/gengyanlei/fire-smoke-detect-yolov4) 
: fire-smoke-detect-yolov4-yolov5 and fire-smoke-detection-dataset 火灾检测，烟雾检测。
- [CVUsers/Smoke-Detect-by-YoloV5](https://github.com/CVUsers/Smoke-Detect-by-YoloV5) 
: Yolov5 real time smoke detection system.
- [CVUsers/Fire-Detect-by-YoloV5](https://github.com/CVUsers/Fire-Detect-by-YoloV5) 
: 火灾检测，浓烟检测，吸烟检测。
- [spacewalk01/Yolov5-Fire-Detection](https://github.com/spacewalk01/Yolov5-Fire-Detection) 
: Train yolov5 to detect fire in an image or video.
- [roflcoopter/viseron](https://github.com/roflcoopter/viseron) 
: Viseron - Self-hosted NVR with object detection.
- [dcmartin/motion-ai](https://github.com/dcmartin/motion-ai) 
: AI assisted motion detection for Home Assistant.
- [Nico31415/Drowning-Detector](https://github.com/Nico31415/Drowning-Detector) 
: Using YOLO object detection, this program will detect if a person is drowning.
- [mc-cat-tty/DoorbellCamDaemon](https://github.com/mc-cat-tty/DoorbellCamDaemon) 
: Part of DoorbellCam project: daemon for people recognition with YOLO from a RTSP video stream.
- [Choe-Ji-Hwan/Fire_Detect_Custom_Yolov5](https://github.com/Choe-Ji-Hwan/Fire_Detect_Custom_Yolov5) 
: 2022-1 Individual Research Assignment: Using YOLOv5 to simply recognize each type of fire.
- [bishal116/FireDetection](https://github.com/bishal116/FireDetection) 
: This project builds fire detecton using YOLO v3 model.
- [Psynosaur/Jetson-SecVision](https://github.com/Psynosaur/Jetson-SecVision) : Person detection for Hikvision DVR with AlarmIO ports, uses TensorRT and yolov4.
- [robmarkcole/fire-detection-from-images](https://github.com/robmarkcole/fire-detection-from-images) 
: Detect fire in images using neural nets.
- [gaiasd/DFireDataset](https://github.com/gaiasd/DFireDataset) 
: D-Fire: an image data set for fire and smoke detection.
- [MuhammadMoinFaisal/FireDetectionYOLOv8](https://github.com/MuhammadMoinFaisal/FireDetectionYOLOv8) 
: Fire Detection using YOLOv8.
- [AI-Expert-04/School_Zone_Eye_Level](https://github.com/AI-Expert-04/School_Zone_Eye_Level) 
: Prevention of accidents in school zones using deep learning.
- [roboflow/supervision](https://github.com/roboflow/supervision) 
: We write your reusable computer vision tools. 💜 [roboflow.github.io/supervision](https://roboflow.github.io/supervision/)
- [AntroSafin/Fire_Detection_YoloV5](https://github.com/AntroSafin/Fire_Detection_YoloV5) 
: This is the YoloV5 fire detection application.
- [harivams-sai/FireDetectionYOLOv8](https://github.com/harivams-sai/FireDetectionYOLOv8) 
: A fire detection model based on YOLOv8 Ultralytics model for object detection. Tech: Python, Computer Vision, Colab Notebook, Fire-detection, YOLOv8.
- [e-candeloro/SAURUSS-Autonomous-Drone-Surveillance](https://github.com/e-candeloro/SAURUSS-Autonomous-Drone-Surveillance) 
: An autonomous drone and sensor based surveillance system that use a Tello Drone, an Arduino, a Raspberry Pi and an Android smartphone.
- [pedbrgs/Fire-Detection](https://github.com/pedbrgs/Fire-Detection) 
: Fire and smoke detection using spatial and temporal patterns.
- ### Anti-UAV Field Detection
#### 反无人机领域检测
- [Anti-UAV](https://github.com/ZhaoJ9014/Anti-UAV) 
: 🔥🔥Official Repository for Anti-UAV🔥🔥. "Evidential Detection and Tracking Collaboration: New Problem, Benchmark and Algorithm for Robust Anti-UAV System". (**[arXiv 2023](https://arxiv.org/abs/2306.15767)**)
- ### Medical Field Detection
#### 医学领域检测
- [DataXujing/YOLO-v5](https://github.com/DataXujing/YOLO-v5) 
: YOLO v5在医疗领域中消化内镜目标检测的应用。
- [Jafar-Abdollahi/Automated-detection-of-COVID-19-cases-using-deep-neural-networks-with-CTS-images](https://github.com/Jafar-Abdollahi/Automated-detection-of-COVID-19-cases-using-deep-neural-networks-with-CTS-images) 
: In this project, a new model for automatic detection of covid-19 using raw chest X-ray images is presented.
- [fahriwps/breast-cancer-detection](https://github.com/fahriwps/breast-cancer-detection) 
: Breast cancer mass detection using YOLO object detection algorithm and GUI.
- [niehusst/YOLO-Cancer-Detection](https://github.com/niehusst/YOLO-Cancer-Detection) 
: An implementation of the YOLO algorithm trained to spot tumors in DICOM images.
- [safakgunes/Blood-Cancer-Detection-YOLOV5](https://github.com/safakgunes/Blood-Cancer-Detection-YOLOV5) 
: Blood Cancer Detection with YOLOV5.
- [shchiang0708/YOLOv2_skinCancer](https://github.com/shchiang0708/YOLOv2_skinCancer) 
: YOLOv2_skinCancer.
- [avral1810/parkinsongait](https://github.com/avral1810/parkinsongait) 
: Parkinson’s Disease.
- [sierprinsky/YoloV5_blood_cells](https://github.com/sierprinsky/YoloV5_blood_cells) : The main idea of this project is to detect blood cells using YOLOV5 over a public roboflow dataset.
- [LuozyCS/skin_disease_detection_yolov5](https://github.com/LuozyCS/skin_disease_detection_yolov5) 
: skin_disease_detection_yolov5.
- [Moqixis/object_detection_yolov5_deepsort](https://github.com/Moqixis/object_detection_yolov5_deepsort) 
: 基于yolov5+deepsort的息肉目标检测。
- [mdciri/YOLOv7-Bone-Fracture-Detection](https://github.com/mdciri/YOLOv7-Bone-Fracture-Detection) 
: YOLOv7 to detect bone fractures on X-ray images.
- [MIRACLE-Center/YOLO_Universal_Anatomical_Landmark_Detection](https://github.com/MIRACLE-Center/YOLO_Universal_Anatomical_Landmark_Detection) 
: [MICCAI 2021] [You Only Learn Once: Universal Anatomical Landmark Detection](https://arxiv.org/abs/2103.04657)
- [fahriwps/breast-cancer-detection](https://github.com/fahriwps/breast-cancer-detection) : Breast cancer mass detection using YOLO object detection algorithm and GUI.
- [mkang315/CST-YOLO](https://github.com/mkang315/CST-YOLO) 
: Official implementation of "CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin Transformer".
- [mkang315/BGF-YOLO](https://github.com/mkang315/BGF-YOLO) 
: [MICCAI'24] Official implementation of "BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection".
- ### Chemistry Field Detection
#### 化学领域检测
- [xuguodong1999/COCR](https://github.com/xuguodong1999/COCR) 
: COCR is designed to convert an image of hand-writing chemical structure to graph of that molecule.
- ### Agricultural Field Detection
#### 农业领域检测
- [liao1fan/MGA-YOLO-for-apple-leaf-disease-detection](https://github.com/liao1fan/MGA-YOLO-for-apple-leaf-disease-detection) 
: MGA-YOLO: A Lightweight One-Stage Network for Apple Leaf Disease Detection.
- [tanmaypandey7/wheat-detection](https://github.com/tanmaypandey7/wheat-detection) 
: Detecting wheat heads using YOLOv5.
- [WoodratTradeCo/crop-rows-detection](https://github.com/WoodratTradeCo/crop-rows-detection) 
: It is an real-time crop rows detection method using YOLOv5.
- [denghv/Vegetables_Fruit_Detection](https://github.com/denghv/Vegetables_Fruit_Detection) 
: Using YOLOv10 to detect vegetables & fruit.
- ### Sports Field Detection
#### 体育领域检测
- [tomer-erez/pingpong-referee](https://github.com/tomer-erez/pingpong-referee) 
: using the YOlO algorithm for an automated pingpong referee.
- ### Adverse Weather Conditions
#### 恶劣天气情况
- [LLVIP](https://github.com/bupt-ai-cz/LLVIP) 
: "LLVIP: A Visible-infrared Paired Dataset for Low-light Vision". (**[ICCV 2021](https://openaccess.thecvf.com/content/ICCV2021W/RLQ/html/Jia_LLVIP_A_Visible-Infrared_Paired_Dataset_for_Low-Light_Vision_ICCVW_2021_paper.html)**)
- [Image-Adaptive YOLO](https://github.com/wenyyu/Image-Adaptive-YOLO) 
: "Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions". (**[AAAI 2022](https://arxiv.org/abs/2112.08088)**). "计算机视觉研究院：《[图像自适应YOLO：模糊环境下的目标检测（附源代码）](https://mp.weixin.qq.com/s/QdM6Dx990VhN97MRIP74XA)》"
- ### Adversarial Attack and Defense
#### 对抗攻击与防御
- [EAVISE/adversarial-yolo](https://gitlab.com/EAVISE/adversarial-yolo) : "Fooling automated surveillance cameras: adversarial patches to attack person detection". (**[CVPR 2019](https://openaccess.thecvf.com/content_CVPRW_2019/html/CV-COPS/Thys_Fooling_Automated_Surveillance_Cameras_Adversarial_Patches_to_Attack_Person_Detection_CVPRW_2019_paper.html)**)
- [git-disl/TOG](https://github.com/git-disl/TOG) 
: "Adversarial Objectness Gradient Attacks on Real-time Object Detection Systems". (**[IEEE TPS-ISA 2020](https://ieeexplore.ieee.org/abstract/document/9325397)**) | "Understanding Object Detection Through an Adversarial Lens". (**[ESORICS 2020](https://link.springer.com/chapter/10.1007/978-3-030-59013-0_23)**)
- [VITA-Group/3D_Adversarial_Logo](https://github.com/VITA-Group/3D_Adversarial_Logo) 
: 3D adversarial logo attack on different3D object meshes to fool a YOLOV2 detector. "Can 3D Adversarial Logos Clock Humans?". (**[arXiv 2020](https://arxiv.org/abs/2006.14655)**)
- [ASGuard-UCI/MSF-ADV](https://github.com/ASGuard-UCI/MSF-ADV) 
: MSF-ADV is a novel physical-world adversarial attack method, which can fool the Multi Sensor Fusion (MSF) based autonomous driving (AD) perception in the victim autonomous vehicle (AV) to fail in detecting a front obstacle and thus crash into it. "Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks". (**[IEEE S&P 2021](https://www.computer.org/csdl/proceedings-article/sp/2021/893400b302/1t0x9btzenu)**)
- [veralauee/DPatch](https://github.com/veralauee/DPatch) 
: "DPatch: An Adversarial Patch Attack on Object Detectors". (**[arXiv 2018](https://arxiv.org/abs/1806.02299)**)
- [Shudeng/GPAttack](https://github.com/Shudeng/GPAttack) 
: Grid Patch Attack for Object Detection.
- [Wu-Shudeng/DPAttack](https://github.com/Wu-Shudeng/DPAttack) 
: "DPAttack: Diffused Patch Attacks against Universal Object Detection". (**[arXiv 2020](https://arxiv.org/abs/2010.11679)**)
- [FenHua/DetDak](https://github.com/FenHua/DetDak) 
: Patch adversarial attack; object detection; CIKM2020 安全AI挑战者计划第四期：通用目标检测的对抗攻击。 "Object Hider: Adversarial Patch Attack Against Object Detectors". (**[arXiv 2020](https://arxiv.org/abs/2010.14974)**)
- [THUrssq/Tianchi04](https://github.com/THUrssq/Tianchi04) 
: This is NO.4 solution for "CIKM-2020 Alibaba-Tsinghua Adversarial Challenge on Object Detection". "Sparse Adversarial Attack to Object Detection". (**[arXiv 2020](https://arxiv.org/abs/2012.13692)**)
- [mesunhlf/UPC-tf](https://github.com/mesunhlf/UPC-tf) 
: "Universal Physical Camouflage Attacks on Object Detectors". (**[CVPR 2020](https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Universal_Physical_Camouflage_Attacks_on_Object_Detectors_CVPR_2020_paper.html)**)
- [alex96295/YOLOv3_adversarial_defense](https://github.com/alex96295/YOLOv3_adversarial_defense) 
: YOLOv3_adversarial_defense.
- [alex96295/YOLO_adversarial_attacks](https://github.com/alex96295/YOLO_adversarial_attacks) 
: YOLO_adversarial_attacks.
- [alex96295/Adversarial-Patch-Attacks-TRAINING-YOLO-SSD-Pytorch](https://github.com/alex96295/Adversarial-Patch-Attacks-TRAINING-YOLO-SSD-Pytorch) 
: This repository has the code needed to train 'Adversarial Patch Attacks' on YOLO and SSD models for object detection in Pytorch.
- [FranBesq/attack-yolo](https://github.com/FranBesq/attack-yolo) 
: Developing adversarial attacks on YOLO algorithm for computer vision.
- [Rushi314/GPR-Object-Detection](https://github.com/Rushi314/GPR-Object-Detection) 
: Detecting Objects in Ground Penetrating Radars Scans.
- [realtxy/pso-adversarial-yolo_v3](https://github.com/realtxy/pso-adversarial-yolo_v3) 
: pso-adversarial-yolo_v3.
- [sowgali/ObjCAM](https://github.com/sowgali/ObjCAM) 
: Visualizations for adversarial attacks in object detectors like YOLO.
- [andrewpatrickdu/adversarial-yolov3-cowc](https://github.com/andrewpatrickdu/adversarial-yolov3-cowc) 
: "Physical Adversarial Attacks on an Aerial Imagery Object Detector". (**[WACV 2022](https://openaccess.thecvf.com/content/WACV2022/html/Du_Physical_Adversarial_Attacks_on_an_Aerial_Imagery_Object_Detector_WACV_2022_paper.html)**)
- [IQTLabs/camolo](https://github.com/IQTLabs/camolo) 
: Camouflage YOLO - (CAMOLO) trains adversarial patches to confuse the YOLO family of object detectors.
- [AdvTexture](https://github.com/WhoTHU/Adversarial_Texture) 
: "Adversarial Texture for Fooling Person Detectors in the Physical World". (**[CVPR 2022](https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Adversarial_Texture_for_Fooling_Person_Detectors_in_the_Physical_World_CVPR_2022_paper.html)**). "知乎「WhoTH」《[CVPR2022 Oral 物理对抗样本 如何做一件“隐形衣”](https://zhuanlan.zhihu.com/p/499854846)》"。
- [SamSamhuns/yolov5_adversarial](https://github.com/SamSamhuns/yolov5_adversarial) 
: Generate adversarial patches against YOLOv5 🚀
- ### Camouflaged Detection
#### 伪装目标检测
- [Ap1rate/yolov8-SIM](https://github.com/Ap1rate/yolov8-SIM) 
: Link to Journal of Ecological Informatics paper ' Camouflaged Detection: Optimization-Based Computer Vision for Alligator sinensis with Low Detectability in Complex Wild Environments '.
- ### Game Field Detection
#### 游戏领域检测
- [SunOner/sunone_aimbot](https://github.com/SunOner/sunone_aimbot) 
: 🌲Aim-bot based on AI for all FPS games. [boosty.to/sunone](https://boosty.to/sunone)
- [Passer1072/RookieAI_yolov8](https://github.com/Passer1072/RookieAI_yolov8) 
: 基于yolov8实现的AI自瞄项目 AI self-aiming project based on yolov8.
- [petercunha/Pine](https://github.com/petercunha/Pine) 
: 🌲 Aimbot powered by real-time object detection with neural networks, GPU accelerated with Nvidia. Optimized for use with CS:GO.
- [chaoyu1999/FPSAutomaticAiming](https://github.com/chaoyu1999/FPSAutomaticAiming) 
: 基于yolov5的FPS类游戏AI自瞄AI。
- [Lu-tju/CSGO_AI](https://github.com/Lu-tju/CSGO_AI) 
: 基于YOLOv3的csgo自瞄。
- [kir486680/csgo_aim](https://github.com/kir486680/csgo_aim) 
: Aim assist for CSGO with python and yolo.
- [c925777075/yolov5-dnf](https://github.com/c925777075/yolov5-dnf) 
: yolov5-DNF.
- [davidhoung2/APEX-yolov5-aim-assist](https://github.com/davidhoung2/APEX-yolov5-aim-assist) 
: using yolov5 to help you aim enemies.
- [Brednan/CSGO-Aimbot](https://github.com/Brednan/CSGO-Aimbot) 
: Aimbot for the FPS game CSGO. It uses YOLOv5 to detect enemy players on my screen, then moves my cursor to the location.
- [2319590263/yolov5-csgo](https://github.com/2319590263/yolov5-csgo) 
: 基于yolov5实现的csgo自瞄。
- [SCRN-VRC/YOLOv4-Tiny-in-UnityCG-HLSL](https://github.com/SCRN-VRC/YOLOv4-Tiny-in-UnityCG-HLSL) 
: A modern object detector inside fragment shaders.
- [qcjxs-hn/yolov5-csgo](https://github.com/qcjxs-hn/yolov5-csgo) 
: 这是一个根据教程写的csgo-ai和我自己训练的模型，还有数据集。
- [Sequoia](https://github.com/IgaoGuru/Sequoia) 
: A neural network for CounterStrike:GlobalOffensive character detection and classification. Built on a custom-made dataset (csgo-data-collector).
- [ItGarbager/aimcf_yolov5](https://github.com/ItGarbager/aimcf_yolov5) 
: 使用yolov5算法实现cf角色头部预测。
- [jiaran-takeme/Target-Detection-for-CSGO-by-YOLOv5](https://github.com/jiaran-takeme/Target-Detection-for-CSGO-by-YOLOv5) 
: Target Detection for CSGO by YOLOv5.
- [Lucid1ty/Yolov5ForCSGO](https://github.com/Lucid1ty/Yolov5ForCSGO) 
: CSGO character detection and auto aim.
- [leo4048111/Yolov5-LabelMaker-For-CSGO](https://github.com/leo4048111/Yolov5-LabelMaker-For-CSGO) 
: A simple tool for making CSGO dataset in YOLO format.
- [soloist-v/AutoStrike](https://github.com/soloist-v/AutoStrike) 
: 使用yolov5自动瞄准，支持fps游戏 鼠标移动控制需要自行调整。
- [slyautomation/osrs_yolov5](https://github.com/slyautomation/osrs_yolov5) 
: Yolov5 Object Detection In OSRS using Python code, Detecting Cows - Botting.
- [HarunoWindy/yolo-games-weights](https://github.com/HarunoWindy/yolo-games-weights) 
: YOLOv5 vision deep-learning on detect games UI (current support: onmyoji) YOLOv5深度学习识别游戏UI(目前支持：阴阳师).
- [mrathena/python.yolo.csgo.autoaim.helper](https://github.com/mrathena/python.yolo.csgo.autoaim.helper) 
: Python Yolo v5 6.2 Csgo.
- [Aa-bN/AimYolo](https://github.com/Aa-bN/AimYolo) 
: AI外挂——基于YOLOv5的射击类游戏瞄准辅助。An AI plug-in - targeting aid for shooting games based on YOLOv5.
- [suixin1424/cf-yolo-trt](https://github.com/suixin1424/cf-yolo-trt) 
: 基于yolov5-trt的穿越火线ai自瞄。
- [DuGuYifei/Yolov5_FPS_AICheatPrinciple](https://github.com/DuGuYifei/Yolov5_FPS_AICheatPrinciple) 
: The AI cheating principle of fps game. (This is only used for learning).
- [MistyAI/MistyFN](https://github.com/MistyAI/MistyFN) 
: Aimbot and Triggerbot for Fortnite based on artificial intelligence.
- [suixin1424/crossfire-yolo-TensorRT](https://github.com/suixin1424/crossfire-yolo-TensorRT) 
: crossfire-yolo-TensorRT. 基于yolo-trt的穿越火线ai自瞄。
- [EthanH3514/AL_Yolo](https://github.com/EthanH3514/AL_Yolo) 
: 基于Yolov5的Apex Legend游戏 AI 辅瞄外挂。
- [SunOner/yolov8_aimbot](https://github.com/SunOner/yolov8_aimbot) 
: Aim-bot based on AI for all FPS games.
- [bigQY/calabiyau-cheat](https://github.com/bigQY/calabiyau-cheat) 
: 基于yolov10的卡拉彼丘自瞄。
- ### Automatic Annotation Tools
#### 自动标注工具
- [Label Studio](https://github.com/HumanSignal/label-studio) 
: Label Studio is a multi-type data labeling and annotation tool with standardized output format. [labelstud.io](https://labelstud.io/)
- [AnyLabeling](https://github.com/vietanhdev/anylabeling) 
: 🌟 AnyLabeling 🌟. Effortless AI-assisted data labeling with AI support from YOLO, Segment Anything, MobileSAM!! [anylabeling.nrl.ai](https://anylabeling.nrl.ai/)
- [X-AnyLabeling](https://github.com/CVHub520/X-AnyLabeling) 
: 💫 X-AnyLabeling 💫. X-AnyLabeling：一款多 SOTA 模型集成的高级自动标注工具！ Effortless data labeling with AI support from Segment Anything and other awesome models.
- [Label Anything](https://github.com/open-mmlab/playground/tree/main/label_anything) 
: OpenMMLab PlayGround: Semi-Automated Annotation with Label-Studio and SAM.
- [LabelImg](https://github.com/heartexlabs/labelImg) 
: 🖍️ LabelImg is a graphical image annotation tool and label object bounding boxes in images.
- [labelme](https://github.com/wkentaro/labelme) 
: Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
- [DarkLabel](https://github.com/darkpgmr/DarkLabel) 
: Video/Image Labeling and Annotation Tool.
- [AlexeyAB/Yolo_mark](https://github.com/AlexeyAB/Yolo_mark) 
: GUI for marking bounded boxes of objects in images for training neural network Yolo v3 and v2.
- [Cartucho/OpenLabeling](https://github.com/Cartucho/OpenLabeling) 
: Label images and video for Computer Vision applications.
- [CVAT](https://github.com/cvat-ai/cvat) 
: Computer Vision Annotation Tool (CVAT). Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.
- [VoTT](https://github.com/Microsoft/VoTT) 
: Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos.
- [WangRongsheng/KDAT](https://github.com/WangRongsheng/KDAT) 
: 一个专为视觉方向目标检测全流程的标注工具集，全称：Kill Object Detection Annotation Tools。
- [Rectlabel-support](https://github.com/ryouchinsa/Rectlabel-support) 
: RectLabel - An image annotation tool to label images for bounding box object detection and segmentation.
- [cnyvfang/labelGo-Yolov5AutoLabelImg](https://github.com/cnyvfang/labelGo-Yolov5AutoLabelImg) 
: 💕YOLOV5 semi-automatic annotation tool (Based on labelImg)💕一个基于labelImg及YOLOV5的图形化半自动标注工具。
- [CVUsers/Auto_maker](https://github.com/CVUsers/Auto_maker) 
: 深度学习数据自动标注器开源 目标检测和图像分类（高精度高效率）。
- [MyVision](https://github.com/OvidijusParsiunas/myvision) 
: Computer vision based ML training data generation tool 🚀
- [wufan-tb/AutoLabelImg](https://github.com/wufan-tb/AutoLabelImg) 
: auto-labelimg based on yolov5, with many other useful tools. AutoLabelImg 多功能自动标注工具。
- [MrZander/YoloMarkNet](https://github.com/MrZander/YoloMarkNet) 
: Darknet YOLOv2/3 annotation tool written in C#/WPF.
- [mahxn0/Yolov3_ForTextLabel](https://github.com/mahxn0/Yolov3_ForTextLabel) 
: 基于yolov3的目标/自然场景文字自动标注工具。
- [MNConnor/YoloV5-AI-Label](https://github.com/MNConnor/YoloV5-AI-Label) 
: YoloV5 AI Assisted Labeling.
- [LILINOpenGitHub/Labeling-Tool](https://github.com/LILINOpenGitHub/Labeling-Tool) 
: Free YOLO AI labeling tool. YOLO AI labeling tool is a Windows app for labeling YOLO dataset.
- [whs0523003/YOLOv5_6.1_autolabel](https://github.com/whs0523003/YOLOv5_6.1_autolabel) 
: YOLOv5_6.1 自动标记目标框。
- [2vin/PyYAT](https://github.com/2vin/PyYAT) 
: Semi-Automatic Yolo Annotation Tool In Python.
- [AlturosDestinations/Alturos.ImageAnnotation](https://github.com/AlturosDestinations/Alturos.ImageAnnotation) 
: A collaborative tool for labeling image data for yolo.
- [stephanecharette/DarkMark](https://github.com/stephanecharette/DarkMark) 
: Marking up images for use with Darknet.
- [2vin/yolo_annotation_tool](https://github.com/2vin/yolo_annotation_tool) 
: Annotation tool for YOLO in opencv.
- [sanfooh/quick_yolo2_label_tool](https://github.com/sanfooh/quick_yolo2_label_tool) 
: yolo快速标注工具 quick yolo2 label tool.
- [folkien/yaya](https://github.com/folkien/yaya) 
: YAYA - Yet annother YOLO annoter for images (in QT5). Support yolo format, image modifications, labeling and detecting with previously trained detector.
- [pylabel-project/pylabel](https://github.com/pylabel-project/pylabel) 
: Python library for computer vision labeling tasks. The core functionality is to translate bounding box annotations between different formats-for example, from coco to yolo.
- [opendatalab/labelU](https://github.com/opendatalab/labelU) 
: Uniform, Unlimited, Universal and Unbelievable Annotation Toolbox.
- ### Feature Map Visualization
#### 特征图可视化
- [pooya-mohammadi/yolov5-gradcam](https://github.com/pooya-mohammadi/yolov5-gradcam) 
: Visualizing Yolov5's layers using GradCam.
- [TorchCAM](https://github.com/frgfm/torch-cam) 
: Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM).
- [Him-wen/OD_Heatmap](https://github.com/Him-wen/OD_Heatmap) 
: Heatmap visualization of the YOLO model using the Grad-CAM heatmap visualization method can Intuitively show which regions in the image contribute the most to the category classification.
- ### Object Detection Evaluation Metrics
#### 目标检测性能评价指标
- [rafaelpadilla/review_object_detection_metrics](https://github.com/rafaelpadilla/review_object_detection_metrics) 
: Object Detection Metrics. 14 object detection metrics: mean Average Precision (mAP), Average Recall (AR), Spatio-Temporal Tube Average Precision (STT-AP). This project supports different bounding box formats as in COCO, PASCAL, Imagenet, etc. "A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit". (**[Electronics 2021](https://www.mdpi.com/2079-9292/10/3/279)**)
- [rafaelpadilla/Object-Detection-Metrics](https://github.com/rafaelpadilla/Object-Detection-Metrics) 
: Most popular metrics used to evaluate object detection algorithms. "A Survey on Performance Metrics for Object-Detection Algorithms". (**[IWSSIP 2020](https://ieeexplore.ieee.org/abstract/document/9145130)**)
- [Cartucho/mAP](https://github.com/Cartucho/mAP) 
: mean Average Precision - This code evaluates the performance of your neural net for object recognition.
- [Lightning-AI/metrics](https://github.com/Lightning-AI/metrics) 
: Machine learning metrics for distributed, scalable PyTorch applications.
- [open-mmlab/mmeval](https://github.com/open-mmlab/mmeval) 
: MMEval is a machine learning evaluation library that supports efficient and accurate distributed evaluation on a variety of machine learning frameworks.
- [laclouis5/globox](https://github.com/laclouis5/globox) 
: A package to read and convert object detection databases (COCO, YOLO, PascalVOC, LabelMe, CVAT, OpenImage, ...) and evaluate them with COCO and PascalVOC metrics.
- ### GUI
#### 图形用户界面
- #### Swift-Related
- [ultralytics/yolo-ios-app](https://github.com/ultralytics/yolo-ios-app) 
: Ultralytics YOLO iOS App source code for running YOLOv8 in your own iOS apps 🌟. [ultralytics.com/yolo](https://ultralytics.com/yolo)
- #### Flutter-Related
- [ultralytics/yolo-flutter-app](https://github.com/ultralytics/yolo-flutter-app) 
: A Flutter plugin for Ultralytics YOLO computer vision models. [ultralytics.com](https://ultralytics.com/)
- [hiennguyen92/flutter_realtime_object_detection](https://github.com/hiennguyen92/flutter_realtime_object_detection) 
: Flutter App real-time object detection with Tensorflow Lite.
- #### Streamlit-Related
- [wjnwjn59/YOLOv10_Streamlit_Demo](https://github.com/wjnwjn59/YOLOv10_Streamlit_Demo) 
: A simple object detection web demo using YOLOv10 and Streamlit.
- [rampal-punia/yolov8-streamlit-detection-tracking](https://github.com/rampal-punia/yolov8-streamlit-detection-tracking) 
: Object detection and tracking algorithm implemented for Real-Time video streams and static images.
- [JackDance/YOLOv8-streamlit-app](https://github.com/JackDance/YOLOv8-streamlit-app) 
: 🔥🔥🔥 Use streamlit framework to increase yolov8 front-end page interaction function. "知乎「Mr.Luyao」《[深度学习/机器学习项目的前端展示利器--Streamlit](https://zhuanlan.zhihu.com/p/630029493)》"。
- [streamlit/demo-self-driving](https://github.com/streamlit/demo-self-driving) : Streamlit app demonstrating an image browser for the Udacity self-driving-car dataset with realtime object detection using YOLO.
- [xugaoxiang/yolov5-streamlit](https://github.com/xugaoxiang/yolov5-streamlit) 
: Deploy YOLOv5 detection with Streamlit.
- [Kedreamix/YoloGesture](https://github.com/Kedreamix/YoloGesture) 
: 基于计算机视觉手势识别控制系统YoLoGesture (利用YOLO实现)，利用yolo进行手势识别的控制系统，最后利用streamlit进行了部署。
- #### Gradio-Related
- [zyds/yolov5-code](https://github.com/zyds/yolov5-code) : 手把手带你实战 YOLOv5。
- [KdaiP/yolov8-deepsort-tracking](https://github.com/KdaiP/yolov8-deepsort-tracking) 
: opencv+yolov8+deepsort行人检测与跟踪,以及可选的WebUI界面（基于gradio）。
- [pengxiang1998/YOLOv8](https://github.com/pengxiang1998/YOLOv8) 
: 基于Gradio搭建的YOLOv8目标检测推理部署。
- #### QT-Related
- [Ai-trainee/Traffic-Sign-Recognition-PyQt5-YOLOv5-GUI](https://github.com/Ai-trainee/Traffic-Sign-Recognition-PyQt5-YOLOv5-GUI) : Road Sign Recognition Project Based on YOLOv5. This is a road sign recognition project based on YOLOv5, developed with a PyQt5 interface, YOLOv5 trained model, and MySQL database. 这是一个基于YOLOv5🚀的道路标志识别系统😊，使用了MySQL数据库💽，PyQt5进行界面设计🎨，PyTorch深度学习框架和TensorRT进行加速⚡，同时包含了CSS样式🌈。系统由五个主要模块组成：系统登录模块🔑负责用户登陆；初始化参数模块📋提供YOLOv5模型的初始化参数设置；标志识别模块🔍是系统的核心，负责对道路标志进行识别并将结果导入数据库；数据库模块💾包含基本数据库操作和数据分析两个子模块；图像处理模块🖼️负责单个图像的处理和数据增强。整个系统支持多种数据输入和模型切换，提供了包括mossic和mixup在内的图像增强方法📈。
- [parker-int64/yolov5-RGBD](https://github.com/parker-int64/yolov5-RGBD) 
: Qt QML based yolov5 + RGBD camera program.
- [Aimol-l/qml_with_yolov7](https://github.com/Aimol-l/qml_with_yolov7) 
: 用YOLOV7+ByteTrack的方法识别视频/视频流，用QML绘制GUI，并带有统计信息。
- [xietx1995/YOLO-QT-Camera-Tool](https://github.com/xietx1995/YOLO-QT-Camera-Tool) 
: Detecting objects from camera or local video files vi qt and yolo.
- [Javacr/PyQt5-YOLOv5](https://github.com/Javacr/PyQt5-YOLOv5) 
: YOLOv5检测界面-PyQt5实现。
- [zstar1003/yolov5_pyqt5](https://github.com/zstar1003/yolov5_pyqt5) 
: 这是一个使用pyqt5搭建YOLOv5目标检测可视化程序。
- [scutlrr/Yolov4-QtGUI](https://github.com/scutlrr/Yolov4-QtGUI) 
: Yolov4-QtGUI是基于[QtGuiDemo](https://github.com/jmu201521121021/QtGuiDemo)项目开发的可视化目标检测界面，可以简便选择本地图片、摄像头来展示图像处理算法的结果。
- [xugaoxiang/yolov5-pyqt5](https://github.com/xugaoxiang/yolov5-pyqt5) 
: 给yolov5加个gui界面，使用pyqt5，yolov5是5.0版本。
- [mxy493/YOLOv5-Qt](https://github.com/mxy493/YOLOv5-Qt) 
: 基于YOLOv5的GUI程序，支持选择要使用的权重文件，设置是否使用GPU，设置置信度阈值等参数。
- [BonesCat/YoloV5_PyQt5](https://github.com/BonesCat/YoloV5_PyQt5) 
: Add gui for YoloV5 using PyQt5.
- [LuckyBoy1798/yolov5-pyqt](https://github.com/LuckyBoy1798/yolov5-pyqt) 
: 基于yolov5+pyqt的甲骨文图形化检测工具。
- [PySimpleGUI/PySimpleGUI-YOLO](https://github.com/PySimpleGUI/PySimpleGUI-YOLO) 
: A YOLO Artificial Intelligence algorithm demonstration using PySimpleGUI.
- [prabindh/qt5-opencv3-darknet](https://github.com/prabindh/qt5-opencv3-darknet) 
: Qt5 + Darknet/Yolo + OpenCV3.
- [GinkgoX/YOLOv3GUI_Pytorch_PyQt5](https://github.com/GinkgoX/YOLOv3GUI_Pytorch_PyQt5) 
: This is a GUI project for Deep Learning Object Detection based on YOLOv3 model.
- [FatemeZamanian/Yolov5-Fruit-Detector](https://github.com/FatemeZamanian/Yolov5-Fruit-Detector) 
: A program to recognize fruits on pictures or videos using yolov5.
- [BioMeasure/PyQt5_YoLoV5_DeepSort](https://github.com/BioMeasure/PyQt5_YoLoV5_DeepSort) 
: This is a PyQt5 GUI program, which is based on YoloV5 and DeepSort to track person.
- [DongLizhong/YOLO_SORT_QT](https://github.com/DongLizhong/YOLO_SORT_QT) 
: This code uses the opencv dnn module to load the darknet model for detection and add SORT for multi-object tracking(MOT).
- [Whu-wxy/yolov5_deepsort_ncnn_qt](https://github.com/Whu-wxy/yolov5_deepsort_ncnn_qt) 
: 用ncnn调用yolov5和deep sort模型，opencv读取视频。
- [jeswanthgalla/PyQt4_GUI_darknet_yolov4](https://github.com/jeswanthgalla/PyQt4_GUI_darknet_yolov4) 
: GUI App using PyQt4. Multithreading to process multiple camera streams and using darknet yolov4 model for object detection.
- [barleo01/yoloobjectdetector](https://github.com/barleo01/yoloobjectdetector) 
: The pupose of this application is to capture video from a camera, apply a YOLO Object detector and display it on a simple Qt Gui.
- [Eagle104fred/PyQt5-Yolov5](https://github.com/Eagle104fred/PyQt5-Yolov5) 
: 把YOLOv5的视频显示到pyqt5ui上。
- [cnyvfang/YOLOv5-GUI](https://github.com/cnyvfang/YOLOv5-GUI) : Qt-GUI implementation of the YOLOv5 algorithm (ver.6 and ver.5). YOLOv5算法(ver.6及ver.5)的Qt-GUI实现。
- [WeNN-Artificial-Intelligence/PyQT-Object-Detection-App](https://github.com/WeNN-Artificial-Intelligence/PyQT-Object-Detection-App) 
: Real-time object detection app with Python and PyQt framework.
- [Powercube7/YOLOv5-GUI](https://github.com/Powercube7/YOLOv5-GUI) 
: A simple GUI made for creating jobs in YOLOv5.
- [cdmstrong/yolov5-pyqt-moke](https://github.com/cdmstrong/yolov5-pyqt-moke) 
: 利用yolov5和pyqt做可视化检测。
- [GHigher12/Pyqt5_yolov5_unet_centernet](https://github.com/GHigher12/Pyqt5_yolov5_unet_centernet) 
: 集yolov5、centernet、unet算法的pyqt5界面，可实现图片目标检测和语义分割。
- [chenanga/qt5_yolov5_2.0](https://github.com/chenanga/qt5_yolov5_2.0) 
: Pyqt搭建YOLOV5目标检测界面-第一次优化后的版本。
- [xun-xh/yolov5-onnx-pyqt-exe](https://github.com/xun-xh/yolov5-onnx-pyqt-exe) 
: 基于Yolov5 + PyQt5 + onnxruntime的目标检测部署。
- [LPC1616/pyqt-yolox-modbus](https://github.com/LPC1616/pyqt-yolox-modbus) 
: qt界面+yolox识别算法+modbus通信。
- [zawawiAI/yolo_gpt](https://github.com/zawawiAI/yolo_gpt) 
: This is a GUI application that integrates YOLOv8 object recognition with OpenAI's GPT-3 language generation model.
- [LSH9832/yolov5_training_tool](https://github.com/LSH9832/yolov5_training_tool) 
: 本工具使用PYQT5编写界面。通过使用该工具可以快速部署相应数据集并训练，目前仍在不断更新中，较大的缺点是目前只支持PascalVOC格式的xml标签文件，所以其它格式的标签文件需要先转换为PascalVOC的格式，且目前仅适用于Linux系统且仅在Ubuntu16.04-20.04试运行。
- [Egrt/YOLO_PyQt5](https://github.com/Egrt/YOLO_PyQt5) 
: 使用Pyqt5搭建YOLO系列多线程目标检测系统。
- [smartwj/yolov5_pyqt5](https://github.com/smartwj/yolov5_pyqt5) 
: 基于yolov5的pyqt5目标检测图形上位机工具。
- [LitChi-bit/YOLOv5-6.0-GUI](https://github.com/LitChi-bit/YOLOv5-6.0-GUI) 
: Qt-GUI implementation of the YOLOv5 algorithm (ver.6).
- [BraunGe/YOLOv5-GUI](https://github.com/BraunGe/YOLOv5-GUI) 
: A GUI for YOLOv5, support all the 11 inference formats that YOLOv5 supports.
- [PetervanLunteren/EcoAssist](https://github.com/PetervanLunteren/EcoAssist) 
: A no-code platform to train and deploy YOLOv5 object detection models.
- [SwimmingLiu/yolov7-Pyside6](https://github.com/SwimmingLiu/yolov7-Pyside6) 
: PySide6 implementation of YOLOv7 GUI.
- #### PySide-Related
- [JSwimmingLiu/YOLOSHOW](https://github.com/SwimmingLiu/YOLOSHOW) 
: YOLO SHOW - YOLOv10 / YOLOv9 / YOLOv8 / YOLOv7 / YOLOv5 / RTDETR GUI based on Pyside6.[swimmingliu.cn/posts/diary/yoloshow](https://swimmingliu.cn/posts/diary/yoloshow)
- [Jai-wei/YOLOv8-PySide6-GUI](https://github.com/Jai-wei/YOLOv8-PySide6-GUI) 
: YoloSide - YOLOv8 GUI By PySide6.
- ### Other Applications
#### 其它应用
- [Ikomia-dev/IkomiaApi](https://github.com/Ikomia-dev/IkomiaApi) 
: State-of-the-art algorithms in Computer Vision with a few lines of code.
- [penny4860/Yolo-digit-detector](https://github.com/penny4860/Yolo-digit-detector) 
: Implemented digit detector in natural scene using resnet50 and Yolo-v2. I used SVHN as the training set, and implemented it using tensorflow and keras.
- [chineseocr/table-detect](https://github.com/chineseocr/table-detect) 
: table detect(yolo) , table line(unet) （表格检测/表格单元格定位）。
- [thisiszhou/SexyYolo](https://github.com/thisiszhou/SexyYolo) 
: An implementation of Yolov3 with Tensorflow1.x, which could detect COCO and sexy or porn person simultaneously.
- [javirk/Person_remover](https://github.com/javirk/Person_remover) 
: People removal in images using Pix2Pix and YOLO.
- [foschmitz/yolo-python-rtsp](https://github.com/foschmitz/yolo-python-rtsp) 
: Object detection using deep learning with Yolo, OpenCV and Python via Real Time Streaming Protocol (RTSP).
- [ismail-mebsout/Parsing-PDFs-using-YOLOV3](https://github.com/ismail-mebsout/Parsing-PDFs-using-YOLOV3) 
: Parsing pdf tables using YOLOV3.
- [008karan/PAN_OCR](https://github.com/008karan/PAN_OCR) 
: Building OCR using YOLO and Tesseract.
- [zeyad-mansour/lunar](https://github.com/zeyad-mansour/lunar) 
: Lunar is a neural network aimbot that uses real-time object detection accelerated with CUDA on Nvidia GPUs.
- [lannguyen0910/food-recognition](https://github.com/lannguyen0910/food-recognition) 
: 🍔🍟🍗 Food analysis baseline with Theseus. Integrate object detection, image classification and multi-class semantic segmentation. 🍞🍖🍕
- [killnice/yolov5-D435i](https://github.com/killnice/yolov5-D435i) 
: using yolov5 and realsense D435i.
- [SahilChachra/Video-Analytics-Dashboard](https://github.com/SahilChachra/Video-Analytics-Dashboard) 
: Video Analytics dashboard built using YoloV5 and Streamlit.
- [isLinXu/YOLOv5_Efficient](https://github.com/isLinXu/YOLOv5_Efficient) 
: Use yolov5 efficiently(高效地使用Yolo v5).
- [HRan2004/Yolo-ArbV2](https://github.com/HRan2004/Yolo-ArbV2) 
: Yolo-ArbV2 在完全保持YOLOv5功能情况下，实现可选多边形信息输出。
- [Badw0lf613/wmreading_system](https://github.com/Badw0lf613/wmreading_system) 
: 基于YOLOv5的水表读数系统。
- [zgcr/SimpleAICV-pytorch-ImageNet-COCO-training](https://github.com/zgcr/SimpleAICV-pytorch-ImageNet-COCO-training) 
: SimpleAICV:pytorch training example on ImageNet(ILSVRC2012)/COCO2017/VOC2007+2012 datasets.Include ResNet/DarkNet/RetinaNet/FCOS/CenterNet/TTFNet/YOLOv3/YOLOv4/YOLOv5/YOLOX.
- [ErenKaymakci/Real-Time-QR-Detection-and-Decoding](https://github.com/ErenKaymakci/Real-Time-QR-Detection-and-Decoding) 
: This repo explain how qr codes works, qr detection and decoding.
- [LUMAIS/AntDet_YOLOv5](https://github.com/LUMAIS/AntDet_YOLOv5) 
: Ants and their Activiteis (Trophallaxis) Detection using YOLOv5 based on PyTorch.
- [Jiseong-Ok/OCR-Yolov5-SwinIR-SVTR](https://github.com/Jiseong-Ok/OCR-Yolov5-SwinIR-SVTR) 
: OCR(Korean).
- [QIN2DIM/hcaptcha-challenger](https://github.com/QIN2DIM/hcaptcha-challenger) 
: 🥂 Gracefully face hCaptcha challenge with YOLOv6(ONNX) embedded solution.
- [bobjiangps/vision](https://github.com/bobjiangps/vision) 
: UI auto test framework based on YOLO to recognize elements, less code, less maintenance, cross platform, cross project / 基于YOLO的UI层自动化测试框架, 可识别控件类型，减少代码和维护，一定程度上跨平台跨项目。
- [RizwanMunawar/yolov7-object-cropping](https://github.com/RizwanMunawar/yolov7-object-cropping) 
: YOLOv7 Object Cropping Using OpenCV.
- [RizwanMunawar/yolov7-object-blurring](https://github.com/RizwanMunawar/yolov7-object-blurring) 
: YOLOv7 Object Blurring Using PyTorch and OpenCV.
- [pacocp/YOLOF](https://github.com/pacocp/YOLOF) 
: 📹 YOLO meets Optical Flow.
- [FabianPlum/OmniTrax](https://github.com/FabianPlum/OmniTrax) 
: Deep learning-based multi animal tracking and pose estimation Blender Add-on.
- [aweihao/ExDark2Yolo](https://github.com/aweihao/ExDark2Yolo) 
: Convert ExDark annotated format data to YOLO format data. / 将ExDark标注格式的数据转换成YOLO格式的数据。
- [ozankaraali/yolov3-recaptcha](https://github.com/ozankaraali/yolov3-recaptcha) 
: Solve Recaptcha with YoloV3. A proof of concept Recaptcha solver using YOLOv3 on Tensorflow 2.0 and Selenium. This tutorial shows that with a better trained object detection weight file, ReCaptcha can be easily solved.
- [jyp-studio/Invoice_detection](https://github.com/jyp-studio/Invoice_detection) 
: This is an AI model for detecting and recognizing invoice information by yolov5 and OCR.
- [vmc-7645/YOLOv8-retail](https://github.com/vmc-7645/YOLOv8-retail) 
: Detect retail products via the YOLOv8 object recognition engine.
- [TAber-W/RM_4-points_yolov5](https://github.com/TAber-W/RM_4-points_yolov5) 
: Robomaster 基于yoloface和MobileNet修改的四点模型.
- [eternal-echo/picking](https://github.com/eternal-echo/picking) 
: 基于YOLO v5视觉分拣零件系统设计。
- [swordswind/yolo_ocr_api_server](https://github.com/swordswind/yolo_ocr_api_server) 
: YOLOv10&EasyOCR融合图像识别API服务器。
## Blogs
- [知乎「江大白」| 微信公众号「江大白」](https://www.zhihu.com/people/nan-yang-8-13)
- [2020-05-27，深入浅出Yolo系列之Yolov3&Yolov4&Yolov5&Yolox核心基础知识完整讲解](https://zhuanlan.zhihu.com/p/143747206)
- [2020-08-10，深入浅出Yolo系列之Yolov5核心基础知识完整讲解](https://zhuanlan.zhihu.com/p/172121380)
- [2021-08-09，深入浅出Yolox之自有数据集训练超详细教程](https://zhuanlan.zhihu.com/p/397499216)
- [2021-08-11，深入浅出Yolo系列之Yolox核心基础完整讲解](https://zhuanlan.zhihu.com/p/397993315)
- [2022-01-30，深入浅出0基础入门AI及目标检测详细学习路径](https://zhuanlan.zhihu.com/p/463221190)
- [2022-01-30，深入浅出Yolov5之自有数据集训练超详细教程](https://zhuanlan.zhihu.com/p/463176500)
- [2022-11-03，实践教程 | 在yolov5上验证的一些想法尝试](https://mp.weixin.qq.com/s/HqXJov5fWIlgKhMp2_Ca7g)
- [2022-12-17，YOLOv6精度深度优化，感知量化的重参再设计](https://mp.weixin.qq.com/s/lm77Fe4e6e_cx_gJYhp8QA)
- [2022-12-28，Repvgg重参数化，YOLO检测算法涨点实践！](https://mp.weixin.qq.com/s/QZnpo24537fhGeFj7-MR_Q)
- [2023-01-16，YOLOv8自有数据集训练，及多任务使用详细教程](https://mp.weixin.qq.com/s/zhoFAKvFOHh0T1R2fvwZxQ)
- [2023-01-28，YOLOv8+DeepSORT原理讲解及实现（附源码）](https://mp.weixin.qq.com/s/rDpbzIG95TmgpJQH71QY8g)
- [2023-02-23，深入浅出TensorRT中ONNX模型解析过程](https://mp.weixin.qq.com/s/C3O3QeSUnu4LUBxHZtur7A)
- [2023-02-24，模型部署 | TensorRT加速PyTorch实战部署教程，值得收藏学习！](https://mp.weixin.qq.com/s/AdnfJ48mnwFejTtHN4v70w)
- [2023-02-25，YOLOv8+ByteTrack，作者开源多目标跟踪算法](https://mp.weixin.qq.com/s/DZcVdwFZP3TKaTk0n98oeg)
- [2023-02-27，基于YOLOv5的半监督目标检测，算法进阶之路，阿里团队新作！（附论文及源码）](https://mp.weixin.qq.com/s/9qpuLCvgaQjc_JOdZchxjQ)
- [2023-03-18，Efficient Teacher，针对YOLOv5的半监督目标检测算法（附论文及源码）](https://mp.weixin.qq.com/s/3YnNAx_2PFqpxLUZZWoYAg)
- [2023-03-20，onnx模型转换，op不支持时的心得经验分享](https://mp.weixin.qq.com/s/qkktjhALMKgRwSSiq6n5bA)
- [2023-03-24，深度学习模型训练中，GPU和显存分析](https://mp.weixin.qq.com/s/xyCNXUBE2rTjTUnK6bBm7g)
- [2023-03-25，PyTorch模型训练，并行加速方法梳理汇总](https://mp.weixin.qq.com/s/54FaTRh8dUXwI4JqO9LAsQ)
- [2023-03-27，基于YOLO的铝型材表面缺陷识别 ](https://mp.weixin.qq.com/s/sTL6aATIDOh8RpicU2B9tA)
- [2023-03-31，小目标检测精度优化方式，CEASA模块，即插即用（附论文及源码）](https://mp.weixin.qq.com/s/fXV3rdB_YtSVap0FtK_AeQ)
- [2023-04-01，GPU 利用率低常见原因分析及优化](https://mp.weixin.qq.com/s/LCJZqnNB6C15EEMPB1X-hQ)
- [2023-04-03，小目标检测算法，Yolov5优化升级 ，即插即用，值得尝试！](https://mp.weixin.qq.com/s/KEdsJO1z19sq7rTtwyC4Rg)
- [2023-04-22，CUDA卷积算子，手写详细实现流程](https://mp.weixin.qq.com/s/3rQQ31LWxvDli_1uwGsHIw)
- [2023-04-28，深入浅出PyTorch模型，int8量化及原理流程](https://mp.weixin.qq.com/s/pij3APMt_wtyS6St89lbdQ)
- [2023-04-29，AI视觉项目，图像标注工具梳理汇总](https://mp.weixin.qq.com/s/SvgTQfKqGlI5DsrsmfKUhA)
- [2023-05-08，Label-Studio X SAM，半自动化标注神器（附源码）](https://mp.weixin.qq.com/s/f-sD8ukV3Nm28_-yHi44BA)
- [2023-05-09，深入浅出多目标跟踪技术的研究与探索](https://mp.weixin.qq.com/s/aYam5aQXJTZ1ysubEfewYA)
- [2023-05-10，超强目标检测器RT-DETR，保姆级部署教程，从入门到精通（附论文及源码）](https://mp.weixin.qq.com/s/NfUWJ5cBTXvuB45l1hnSfw)
- [2023-05-13，YOLOCS目标检测算法，YOLOv5的Backbone/Neck/Head全面改进](https://mp.weixin.qq.com/s/exo2JkLluChvLDSif2JvMQ)
- [2023-05-17，一文看尽深度学习各种注意力机制，学习推荐！](https://mp.weixin.qq.com/s/PkzzElN1uk2Yzu1DsYnOdQ)
- [2023-05-26，一文读懂PyTorch显存管理机制，推荐学习！](https://mp.weixin.qq.com/s/a9LK35lLE4yfQkqvBp6ujQ)
- [2023-06-05，两万字长文，目标检测入门看这篇就够了，推荐收藏！](https://mp.weixin.qq.com/s/EBc1JrR5n4BlWGBx8kuiXw)
- [2023-06-07，手把手带你，自己设计实现一个深度学习框架（附代码实现）](https://mp.weixin.qq.com/s/-8A_XaOwHyg653UyRbArQQ)
- [2023-06-12，MMDetection目标检测框架详解，及训练自有数据集教程](https://mp.weixin.qq.com/s/U3irSW9UTKt0gY0HCV9slQ)
- [2023-06-19，万字长文，彻底搞懂YOLOv8网络结构及代码实战！](https://mp.weixin.qq.com/s/vXIx7dBRxgxnvh5BoIRQZw)
- [2023-06-27，TensorRT模型部署，添加自己插件的落地方式](https://mp.weixin.qq.com/s/E-Iebdd4Es5UK-TrBUJcjA)
- [2023-06-29，YOLOv7+Transformer部署，TensorRT应用实战（附代码）](https://mp.weixin.qq.com/s/znxT8nsfkq0s5NHRnAxYaw)
- [2023-07-06，万字长文，基于PyTorch的多种卷积神经网络BackBone代码实现](https://mp.weixin.qq.com/s/TQ88Oex6YTKAkUZL3kLu3A)
- [2023-07-21，万字长文，YOLOv5手势识别训练转换及模型部署！（附代码）](https://mp.weixin.qq.com/s/1yvJIObEs9H4C9Qd3tb9kA)
- [2023-08-03，TensorRT模型INT8量化，Python代码部署实现](https://mp.weixin.qq.com/s/Phu7UmPKuSrUOhCQDV2xEQ)
- [2023-08-12，目标检测算法，检测框位置优化总结](https://mp.weixin.qq.com/s/_JDPP7Yq8E4bXxZtWlOy6Q)
- [2023-09-01，基于Yolo算法的AI数钢筋，整体解决方案汇总](https://mp.weixin.qq.com/s/plWUuEVkbK-nDycqVDFU8A)
- [2024-01-26，深入浅出，YOLOv8算法使用指南](https://mp.weixin.qq.com/s/9naZZ7wXugppelcmPHGVlQ)
- [2024-02-23，目标检测YOLOv9算法，重磅开源！（附论文及源码）](https://mp.weixin.qq.com/s/RVG-9h8zKsWACMr6dDRpUQ)
- [2024-04-04，CPU推理1ms的Backbone开源，精度速度碾压MobileNet/ShuffleNet等轻量模型！](https://mp.weixin.qq.com/s/FC9KtCPpwEraYuj4qnw_oQ)
- [2024-04-12，深入浅出，PyTorch模型int8量化原理拆解](https://mp.weixin.qq.com/s/j2QS3LdudrrlyZYQkVrl5Q)
- [2024-06-18，Mamba-YOLO开源，超越 YOLO ，创新SSM 技术，提升目标检测性能！（附论文及源码）](https://mp.weixin.qq.com/s/UREcCHvyl7yIEv_si9KOjQ)
- [2024-07-13，YOLOv5、YOLOv8与YOLOv10，性能分析与边缘部署梳理，YOLO算法进化史！](https://mp.weixin.qq.com/s/wTwjDESVipFg2Tnh9Mgp6A)
- [知乎「迪迦奥特曼」](https://www.zhihu.com/people/nemofeng95)
- [2022-08-12，从百度飞桨YOLOSeries库看各个YOLO模型](https://zhuanlan.zhihu.com/p/550057480)
- [2022-09-21，YOLO内卷时期该如何选模型？](https://zhuanlan.zhihu.com/p/566469003)
- [知乎「PoemAI」](https://www.zhihu.com/people/LEYM2)
- [2022-07-10，YOLO家族进化史（v1-v7）](https://zhuanlan.zhihu.com/p/539932517)
- [知乎「科技猛兽」](https://www.zhihu.com/people/wang-jia-hao-53-3)
- [2020-08-14，你一定从未看过如此通俗易懂的YOLO系列(从v1到v5)模型解读 (上)](https://zhuanlan.zhihu.com/p/183261974)
- [2020-08-21，你一定从未看过如此通俗易懂的YOLO系列(从v1到v5)模型解读 (中)](https://zhuanlan.zhihu.com/p/183781646)
- [2020-08-17，你一定从未看过如此通俗易懂的YOLO系列(从v1到v5)模型解读 (下)](https://zhuanlan.zhihu.com/p/186014243)
- [知乎「CV技术指南」| 微信公众号「CV技术指南」](https://www.zhihu.com/people/cvji-zhu-zhi-nan)
- [2021-08-26，目标检测mAP的计算 & COCO的评价指标](https://mp.weixin.qq.com/s/gpr7JZMRgp8B5RxhVzt_mQ)
- [2022-04-07，YOLO系列梳理（一）YOLOv1-YOLOv3](https://zhuanlan.zhihu.com/p/494572914)
- [2022-04-15，YOLO系列梳理与复习（二）YOLOv4 ](https://mp.weixin.qq.com/s/2lndImcah5QJJJiEujGOsA)
- [2022-04-24，YOLO系列梳理（三）YOLOv5](https://zhuanlan.zhihu.com/p/503971609)
- [2022-06-26，YOLO系列梳理（九）初尝新鲜出炉的YOLOv6](https://zhuanlan.zhihu.com/p/534090250)
- [2022-07-19，YOLO系列梳理（十）YOLO官方重回江湖 并带来了YOLOv7](https://zhuanlan.zhihu.com/p/543574708)
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- 微信公众号「WeThinkln」
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- 微信公众号「GiantPandaCV」
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- 微信公众号「PandaCVer」
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- 微信公众号「人工智能AI算法工程师」
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- [2022-09-24，YOLOv5、v7改进之二十八：ICLR 2022涨点神器——即插即用的动态卷积ODConv](https://mp.weixin.qq.com/s/-wH_N4-pXY08XdbJ-Iu8zA)
- [2022-10-08，YOLOv5、YOLOv7改进之二十九：v2.0版本的Swin Transformer 融入](https://mp.weixin.qq.com/s/9g-JMK44YQDd3feTBwCYjA)
- [2022-10-13，YOLOv5、YOLOv7改进之三十：引入10月4号发表最新的Transformer视觉模型MOAT结构](https://mp.weixin.qq.com/s/Y2kOLVbU5ZnNzPIoiv4voA)
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- [2022-10-19，YOLOv5、v7改进之三十五：引入NAMAttention注意力机制](https://mp.weixin.qq.com/s/rFe2pex6-YsUpRj8K-pw3g)
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- [2022-11-04，改进的YOLO：AF-FPN替换金字塔模块提升目标检测精度](https://mp.weixin.qq.com/s/JVr1C9nPTYlHS4aei-Zqrg)
- [2022-12-31，Micro-YOLO：探索目标检测压缩模型的有效方法（附论文下载）](https://mp.weixin.qq.com/s/0_sF3U232i0PEw1NHE2Efw)
- [2023-02-25，使用ONNXRuntime部署阿里达摩院开源DAMO-YOLO目标检测，一共包含27个onnx模型(代码开源)](https://mp.weixin.qq.com/s/cQo7HMcWcbZgk7XIzj1q2A)
- [2023-04-03，CVPR 2023 论文分类汇总：一个专为计算机视觉领域研究者打造的学术资源宝库](https://mp.weixin.qq.com/s/g8yUdF0SP-81VpVfFjTqNw)
- [2023-04-07，Micro-YOLO：探索目标检测压缩模型的有效方法（附论文下载）](https://mp.weixin.qq.com/s/xMq10ZZQnFyXaob0H-Z1qw)
- [2023-04-07，实用教程详解：模型部署，用DNN模块部署YOLO目标检测（附源代码）](https://mp.weixin.qq.com/s/ny98FTagPQB1-GnHKFu2MA)
- [2023-04-20，全自动实时移动端AI框架 | YOLO-v4目标检测实时手机端实现](https://mp.weixin.qq.com/s/FPG44PhAxNi7cy_ALcNXmA)
- [2023-04-22，CVPR目标检测新框架：不再是YOLO，而是只需要一层特征（干货满满，建议收藏）](https://mp.weixin.qq.com/s/5sTxdjhKIPpQ-rCsWfe80A)
- [2023-04-25，GPT-CV：基于Yolov5的半监督目标检测](https://mp.weixin.qq.com/s/wK-5i30X06SfLgASlRdqJw)
- [2023-04-25，EdgeYOLO：边缘设备上实时运行的目标检测器及Pytorch实现](https://mp.weixin.qq.com/s/zEFjvUKnrm5Iwa6e9Fy29Q)
- [2023-04-26，改进的YOLO：AF-FPN替换金字塔模块提升目标检测精度](https://mp.weixin.qq.com/s/xocZNuIOCgGynjZxX_xKgw)
- [2023-06-22，RestoreDet：低分辨率图像中目标检测](https://mp.weixin.qq.com/s/FqBq9gy-NKfp3W2qgKHb5w)
- [2023-07-12，GPT理解的CV：基于Yolov5的半监督目标检测](https://mp.weixin.qq.com/s/N4x0_Bu078g1zSMIDPwzZg)
- [2023-07-12，YoloV8与ChatGPT互通，这功能是真的强大！](https://mp.weixin.qq.com/s/ODIFRyvfbZOiEORLdWGc_A)
- [2023-07-24，YOLO-S预告：一种用于小目标检测的轻量级、精确的类YOLO网络](https://mp.weixin.qq.com/s/-G2TpQOOhLyYDw5wPODBkw)
- [2023-08-20，Yolo框架优化：黑夜中也可以实时目标检测](https://mp.weixin.qq.com/s/e0EJVHKW7nkfkMVurMgR2Q)
- [2023-09-04，CRAS-YOLO：多类别船舶检测与分类模型](https://mp.weixin.qq.com/s/ztdYjDbWzpx2LnWTiVWdrQ)
- [2023-09-04，Drone-YOLO：一种有效的无人机图像目标检测](https://mp.weixin.qq.com/s/X4HGQhWaxy1bQssrQIYBmQ)
- [2023-09-05，BFD-YOLO：基于YOLOv7的建筑外墙缺陷检测](https://mp.weixin.qq.com/s/BaqXo4uTeqoY5FhD2jVuxA)
- [2024-05-26，Yolov10：详解、部署、应用一站式齐全！](https://mp.weixin.qq.com/s/damt3VWade0we1MSCe9_QA)
- 微信公众号「新机器视觉」
- [2023-03-22，YOLO系列的演进，从v1到v7](https://mp.weixin.qq.com/s/0ALtok0vleMif-5_rgCycQ)
- [2023-03-23，YOLO系列的演进，从v1到v7（二）](https://mp.weixin.qq.com/s/_aVWQ-NxGwZthA_D_drTRw)
- [2023-03-24，YOLO系列的演进，从v1到v7（三）](https://mp.weixin.qq.com/s/Ngz7SYEtQ8jsejKG0IknXg)
- [2023-05-20，机器视觉和模式识别库汇总](https://mp.weixin.qq.com/s/UaqBSCWnGbLLCuy8cvJpkQ)
- 微信公众号「OpenMMLab」
- [2022-10-20，社区协作，简洁易用，快来开箱新一代 YOLO 系列开源库](https://mp.weixin.qq.com/s/ZK1hzp6QJarS1xiqkBWcrg)
- [2023-03-28，建议收藏！超实用的 YOLO 训练&测试技巧合集](https://mp.weixin.qq.com/s/iF2Upd2ThMBlWPim8Gj13g)
- [2023-01-12，YOLOv8 深度详解！一文看懂，快速上手](https://mp.weixin.qq.com/s/_RNmB3KtYEt7UuDsCOJ3rQ)
- [2023-04-04，显著提升模型精度！以 MMYOLO 为例 ，巧用 MMRazor 轻量级骨干网络](https://mp.weixin.qq.com/s/ilCMYZmG_XpvJ_ysB1cgkw)
- 微信公众号「自动驾驶之心」
- [2022-10-26，手把手教学！TensorRT部署实战：YOLOv5的ONNX模型部署](https://mp.weixin.qq.com/s/M47rwwbU0FRrgd-Xg9c7ww)
- [2022-11-12，SSDA-YOLO：用于跨域目标检测的半监督域自适应YOLO方法](https://mp.weixin.qq.com/s/FFRsxSaTeGvs1ssKGCD6lg)
- [2022-11-30，达摩院 | DAMO-YOLO：兼顾速度与精度的新目标检测框架](https://mp.weixin.qq.com/s/QYsCzgMhW9Mfsa6CYolVuQ)
- [2022-12-23，通用小目标Trick | 深度学习检测小目标常用方法盘点](https://mp.weixin.qq.com/s/WRVjub3ePxWoCBQXKhS__w)
- [2023-01-12，纯量产经验 | 谈谈目标检测中正负样本的问题](https://mp.weixin.qq.com/s/esGe2o3_pPXUlrysZoCQKQ)
- [2023-05-15，最新！自动驾驶中用于目标检测和语义分割的Radar-Camera融合综述](https://mp.weixin.qq.com/s/EHTXisVDv7SV4UEbo7sdbQ)
- [2023-05-19，25FPS！英伟达首发BEVFusion部署源代码，边缘端实时运行！！！](https://mp.weixin.qq.com/s/79DskdwwSghyldvQF43l6A)
- [2023-05-21，保姆级开源教程 | 手把手教你部署FreeYOLO](https://mp.weixin.qq.com/s/AhPaSVl2Gh8zWtJ74IUyzw)
- [2023-05-29，最新SOTA！BEVFusion4D：BEVFusion升级版3D检测时空新框架！](https://mp.weixin.qq.com/s/i3lLadD3_Q5RX5D0JUocPQ)
- [2023-06-04，万字长文 | Transformer在BEV、2D/3D检测上的应用、量化与加速！](https://mp.weixin.qq.com/s/sEWfs2C62cuThZBXSM0fZA)
- [2023-06-15，全搞定！基于TensorRT的CNN/Transformer/检测/BEV模型四大部署代码+CUDA加速！](https://mp.weixin.qq.com/s/WjBvj6hCWEYs7IL9DlrK2Q)
- [2023-08-23，模型部署，今年的香饽饽！TensorRT详细入门指北](https://mp.weixin.qq.com/s/KsPb80tf_zxPyP0xu8ZmHA)
- [2024-01-10，YOLO进军BEV感知！YOLO+BEV在实时检测上的尝试](https://mp.weixin.qq.com/s/8pceyAzzGvwKNnRE9OJEOA)
- 微信公众号「CVHub」
- [2023-01-07，现代目标检测故事 | 40+种网络架构大盘点！从基础架构ResNet到最强检测器Yolov7再到最新部署神器GhostNetV2](https://mp.weixin.qq.com/s/22rRzyZj93-Y4msYwa_LKQ)
- [2023-02-19，阿里团队新作 | 探讨 YOLOv5 的高效进阶之路！](https://mp.weixin.qq.com/s/B0yHtFMTO5gwt0B-ra18QA)
- [2023-05-05，超强目标检测器 RT-DETR | Python/C++ 保姆级部署教程，从入门到精通](https://mp.weixin.qq.com/s/W56LHZbZEqqoCPFVf612FA)
- [2023-06-04，中科院一区顶刊 TCSVT 2023 | DIAL-Filters: 显著提升模糊夜视场景下的检测和分割性能！](https://mp.weixin.qq.com/s/qPbxjDuPOFSD2zsWAGmLQw)
- [2023-07-12，北航新作 | Q-YOLO: 基于 TensorRT 和 OpenVIVO 的目标检测量化实战方案](https://mp.weixin.qq.com/s/Us7IiYXFtUoQJ6btpcG1lw)
- [2023-07-30，大连理工联合阿里达摩院发布HQTrack | 高精度视频多目标跟踪大模型](https://mp.weixin.qq.com/s/Jl2mr7tszulZX19Fx4ZNgw)
- [2024-09-30，Ultrylytics 官宣: YOLO11 全新发布！](https://mp.weixin.qq.com/s/IfOCnuvFCTIzKIQEFWFLdA)
- 微信公众号「人工智能感知信息处理算法研究院」
- [2023-06-15，改进YOLOV5小目标检测之VisDrone2019数据集](https://mp.weixin.qq.com/s/GJza38BBYTl6XAWiiEzpHA)
- [2023-06-16，改进YOLOV5小目标检测之数据预处理之一](https://mp.weixin.qq.com/s/BXueTqerYFtGg9MOhJ7YYA)
- [2023-06-17，改进YOLOV5小目标检测之数据预处理之二](https://mp.weixin.qq.com/s/NblhcYo-JWZuJkMS5015sw)
- [2023-06-22，改进YOLOV5小目标检测消融实验之一](https://mp.weixin.qq.com/s/3_03EmF0wo4hmbes5o37NQ)
- [2023-06-23，改进YOLOV5小目标检测消融实验之二](https://mp.weixin.qq.com/s/iEEGkLFICJT03kXWQwR_sA)
- [2023-07-04，基于改进YOLOv5和可变形卷积的水下群体目标检测概述之一](https://mp.weixin.qq.com/s/ZIH6Y1d6yeUV-zE6AnEvuQ)
- [2023-07-05，基于改进YOLOv5和可变形卷积的水下群体目标检测概述之二](https://mp.weixin.qq.com/s/ptkTsyG2_mOFb6lGUCSkVA)
- [2023-07-07，YOLOV5算法改进之自适应阈值模块](https://mp.weixin.qq.com/s/XSBtVbtcQTrMf13E_HEeWw)
- [2023-07-10，改进YOLOV5算法之不同数据集测试](https://mp.weixin.qq.com/s/-0ZsO9D4o4UXuIy_a2gt0w)
- [2023-07-11，改进YOLOV5算法与同类算法的比较](https://mp.weixin.qq.com/s/KIxhlNBuTnCLnqzKqD_GPA)
- [2023-07-12，改进YOLOV5自适应阈值模块实验分析 ](https://mp.weixin.qq.com/s/WffWRa6MzaRN4oMF3BvOWg)
- [2023-07-15，KAYOLO网络模型](https://mp.weixin.qq.com/s/rYrdJPHYE57Kc8QzVDxUfg)
- [2023-07-19，Yolov8n-IOU损失函数的改进](https://mp.weixin.qq.com/s/x1WRIC9MNQWMTup9XHkwWg)
- [2023-07-26，YOLOV7算法原理](https://mp.weixin.qq.com/s/KnLwHIWqespSxO0v82cJ3A)
- [2023-07-30，Flask 部署 YOLOV5](https://mp.weixin.qq.com/s/9dwrXEAi5tht4-tNyZ4tYw)
- [2023-08-13，目标检测算法的应用](https://mp.weixin.qq.com/s/cX1WlVJqDNePZW18Jlf_Kg)
- 微信公众号「OneFlow」
- [2022-12-13，YOLOv5全面解析教程①：网络结构逐行代码解读](https://mp.weixin.qq.com/s/qfZIKgBdHNwPDp5ng0Y_Qw)
- [2022-12-22，YOLOv5全面解析教程②：如何制作训练效果更好的数据集](https://mp.weixin.qq.com/s/t4Ppf2qokpClRwCN52zF-g)
- [2023-02-02，YOLOv5全面解析教程③：更快更好的边界框回归损失](https://mp.weixin.qq.com/s/LIOnJqJj_GrpakKbLeWEDQ)
- [2023-02-17，YOLOv5全面解析教程④：目标检测模型精确度评估](https://mp.weixin.qq.com/s/nvfAU6TwTDoZhF8zFpCaOw)
- [2023-02-24，YOLOv5全面解析教程⑤：计算mAP用到的Numpy函数详解](https://mp.weixin.qq.com/s/ag7PkcRRSTppEG0GOysqpg)
- [2023-03-09，YOLOv5全面解析教程⑥：模型训练流程详解](https://mp.weixin.qq.com/s/RriWDozw7ZHTBg7Rr38dNw)
- [2023-05-23，YOLOv5全面解析教程⑦：使用模型融合提升mAP和mAR](https://mp.weixin.qq.com/s/6PjD5k5o1GQO8v7jIydZ_w)
- [2023-05-23，YOLOv5全面解析教程⑧：将训练好的YOLOv5权重导为其它框架格式](https://mp.weixin.qq.com/s/4yiN7JZrvAvMi4m5eusbMw)
- 微信公众号「AIWalker」
- [2023-03-29，ChatGPT是如何看待YOLO系列算法的贡献呢？~哈哈~ ](https://mp.weixin.qq.com/s/E-TNeTKK5EV70zAenRVbwQ)
- [2023-05-07，YOLO-NAS | YOLO新高度，引入NAS，出于YOLOv8而优于YOLOv8](https://mp.weixin.qq.com/s/FsWSRguAn2WZKtmPhMbc6g)
- [2023-05-16，全网唯一复现！手机端 1ms 级延迟的主干网模型 MobileOne](https://mp.weixin.qq.com/s/Wk1sHIQKUe01PqMnpzcCfQ)
- [2023-08-15，南开大学提出YOLO-MS | 超越YOLOv8与RTMDet，即插即用打破性能瓶颈](https://mp.weixin.qq.com/s/FfG9vNM_a2k_zflWfuimsw)
- [2024-02-19，U版YOLO-World来了，YOLOv8再度升级，三行代码上手YOLO-World](https://mp.weixin.qq.com/s/yepStVzyrOE4MsgFFuwo0Q)
- [2024-02-23，YOLOv9来了，可编程梯度信息与广义高效层聚合网络 助力全新检测SOTA前沿](https://mp.weixin.qq.com/s/tFavH5_Sqtnq1_NMRt_AUg)
- 微信公众号「董董灿是个攻城狮」
- [2023-03-20，万字长文解析Resnet50的算法原理](https://mp.weixin.qq.com/s/pA86udkaFzCogi2Qw8vBEA)
- [2023-04-17，万字长文入门神经网络硬件加速](https://mp.weixin.qq.com/s/3aNVGIPf5pLzEv67KI8M5w)
- [2023-04-19，CUDA卷积算子手写详细实现](https://mp.weixin.qq.com/s/VlrglazJE54Xnm3tjM0uCg)
- 微信公众号「计算机视觉漫谈」
- [2020-02-22，YOLO v3实战之钢筋数量AI识别（一）](https://mp.weixin.qq.com/s/EElv2Tc73JKS8jpejEGB1w)
- [2020-03-07，YOLO v3实战之钢筋智能识别改进方案分享（二）](https://mp.weixin.qq.com/s/lOeRqD2orcLw5FR496r4uw)
- 微信公众号「智造情报局」
- [2022-11-07，项目实操：基于yolov5的PCB表面缺陷检测【附完整代码】](https://mp.weixin.qq.com/s/IzMabvYts2BEa5IvAwUfrg)
- 微信公众号「学姐带你玩AI」
- [2022-11-21，YOLOv5+Tesseract-OCR 实现车牌号文本识别【实战】](https://mp.weixin.qq.com/s/52Woexamu697tozevSiyQQ)
- 微信公众号「量子位」
- [2023-01-12，YOLOv8已至，精度大涨！教你如何在自定义数据集上训练它](https://mp.weixin.qq.com/s/_ccYfjWm6CsH_vxpACUWEA)
- 微信公众号「笑傲算法江湖」
- [2023-02-08，代码实战：YOLOv5实现钢材表面缺陷检测](https://mp.weixin.qq.com/s/i_bF6_77MxKqEy7-y7LQdQ)
- 微信公众号「OpenCV中文网」
- [2023-04-07，YOLOv8 全家桶再迎新成员！新增Pose Estimation模型!](https://mp.weixin.qq.com/s/wF93AAVnGsQtHdB-DkSTPQ)
- 微信公众号「深度学习与计算机视觉」
- [2023-03-28，使用 YOLO 进行目标检测：如何提取人物图像](https://mp.weixin.qq.com/s/vthdOoy3etZmybMLaGzoFg)
- 微信公众号「机器学习算法工程师」
- [2023-04-19，惊呆了！基于Transformer的检测模型RT-DETR竟然比YOLO还快！](https://mp.weixin.qq.com/s/wgBaZ-CTB7B4nvYnobMDvw)
- 微信公众号「计算机视觉与机器学习」
- [2023-04-19，RT-DETR | 吊打YOLO系列的 DETR部署教程来啦，优雅而简洁！](https://mp.weixin.qq.com/s/oflfbPkhj3ka2ExK7ZZ0VA)
- [2023-05-16，超强目标检测器 RT-DETR | Python/C++ 保姆级部署教程，从入门到精通](https://mp.weixin.qq.com/s/XwmQILnaLtWPfo-dysLeAA)
- 微信公众号「人工智能前沿讲习」
- [2023-04-19，【源头活水】CVPR 2023 | AbSViT：拥有自上而下注意力机制的视觉Transformer](https://mp.weixin.qq.com/s/FtVd37tOXMfu92eDSvdvbg)
- 微信公众号「AI科技与算法编程」
- [2023-04-11, YOLOv8 AS-One：目标检测AS-One 来了！（YOLO就是名副其实的卷王之王）](https://mp.weixin.qq.com/s/ofokLwCwgN1GNTqy3NuYmg)
- 微信公众号「深度学习与NLP」
- [2023-04-24，[万字干货]-如何给模型加入先验知识？](https://mp.weixin.qq.com/s/RmM9ay4arJWBoNP11Bfbsw)
- 微信公众号「OpenCV与AI深度学习」
- [2023-04-23，基于 YOLOv8 的自定义数据集训练](https://mp.weixin.qq.com/s/NrT7aFurdz5IRr3bCFsHQA)
- [2023-06-19，一文彻底搞懂YOLOv8【网络结构+代码+实操】](https://mp.weixin.qq.com/s/HldcdtBXzh5YawcS0Bb4KQ)
- [2023-07-04，保姆教程 | YOLOv5在建筑工地中安全帽佩戴检测的应用](https://mp.weixin.qq.com/s/g6jEP5Y2R_DhrI30DBol5Q)
- [2024-06-05，实战 | YOLOv10 自定义数据集训练实现车牌检测 (数据集+训练+预测 保姆级教程)](https://mp.weixin.qq.com/s/3WSmGP7xdQJc-5YdQXBPFg)
- [2024-06-21，YOLOv10在PyTorch和OpenVINO中推理对比](https://mp.weixin.qq.com/s/xZ4HlfBPXFbf8OPxmXwbrQ)
- [2024-07-08，实战 | YOLOv8使用TensorRT加速推理教程（步骤 + 代码）](https://mp.weixin.qq.com/s/VcUifHycY9aw99d3WD1h1w)
- [2024-07-10，OpenCV使用CUDA加速资料汇总(pdf+视频+源码)](https://mp.weixin.qq.com/s/o-AECBLDucxVLr1Q0yxZ_g)
- [2024-09-30，YOLOv11来了：将重新定义AI的可能性](https://mp.weixin.qq.com/s/S_yjuxHb8PD3B472mvizfg)
- 微信公众号「嵌入式视觉」
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- 微信公众号「机器学习算法那些事」
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- 微信公众号「人工智能技术与咨询」
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- 微信公众号「StrongerTang」
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- 微信公众号「北京大学王选计算机研究所」
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- 微信公众号「计算机视觉深度学习和自动驾驶」
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- 微信公众号「内推君SIR」
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- 微信公众号「古月居」
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- 微信公众号「Streamlit」
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- 微信公众号「FightingCV」
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- 微信公众号「AILab笔记」
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- 微信公众号「CVer」
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- 微信公众号「我爱计算机视觉」
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- 微信公众号「英特尔物联网」
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- 微信公众号「数据科学与AI」
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- 微信公众号「郭小喵玩AI」
- [2023-06-22，Win10环境下OpenVINO部署YOLOv5模型：从理论到实践](https://mp.weixin.qq.com/s/v4y-vjsUrlow5EaP_VrF0A)
- [2023-09-04，超详细 | 使用Yolov8训练自己的数据集](https://mp.weixin.qq.com/s/KdoZnQArI95eWvqHMeqO0A)
- 微信公众号「郭小喵玩AI」
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- 微信公众号「AI视界引擎」
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- 微信公众号「小白玩转Python」
- [2023-12-22，基于 YOLOv8 的疲劳状态检测 | 附源码](https://mp.weixin.qq.com/s/L_-Ii5QvnGgJwo5WYSUcVg)
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- 微信公众号「机器之心」
- [2024-02-23，目标检测新SOTA：YOLOv9问世，新架构让传统卷积重焕生机](https://mp.weixin.qq.com/s/HFyADfWKkyw0TivsqH6kXA)
- 微信公众号「码科智能」
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- 微信公众号「自动驾驶Daily」
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- 微信公众号「3D视觉工坊」
- [2024-02-23，YOLOv9震撼来袭！使用可编程梯度信息学习你想学习的内容！](https://mp.weixin.qq.com/s/Fbd-jarVO4LyjlhdxgmnsA)
- [2024-06-18，YOLO跌落神坛？Mamba YOLO干翻YOLO系列模型！](https://mp.weixin.qq.com/s/MeFXqAyU_OAGnh7qEIb3yQ)
- 微信公众号「DeepDriving」
- [2023-07-21，AI模型部署 | TensorRT模型INT8量化的Python实现](https://mp.weixin.qq.com/s/IQTCUs8CcfgHxJCyV6cm3w)
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- 微信公众号「CSharp与边缘模型部署」
- [2024-06-04，使用 TensorRT C++ API 调用GPU加速部署 YOLOv10 实现 500FPS 推理速度——快到飞起！！](https://mp.weixin.qq.com/s/yijeZtkRhbQxuSE1AsyUhA)
- 微信公众号「BestSongC」
- [2024-05-24，基于YOLO系列算法（YOLOv5、YOLOv6、YOLOv8以及YOLOv9）和Streamlit框架的行人头盔检测系统](https://mp.weixin.qq.com/s/STAVjII8kAk3MMPbB9vJfQ)
- [2024-05-29，基于YOLO系列算法YOLOv5、YOLOv6、YOLOv8以及YOLOv9和Streamlit框架的工人头盔和安全背心检测系统](https://mp.weixin.qq.com/s/Cuhwf2exVWFmoyZPQE_HmQ)
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- [2024-06-11，基于YOLO系列算法（YOLOv5、YOLOv6、YOLOv8)以及YOLOv9）和Streamlit框架的五类动物目标检测系统](https://mp.weixin.qq.com/s/cEhgZYp7rUBFz2BukacQQg)
- 微信公众号「人工智能学习指南」
- [2024-05-28，用自己的数据集实测YOLOv10效果！](https://mp.weixin.qq.com/s/JlGvYGvPa5NyxjEXHLO6uA)
- 微信公众号「跨模态AGI」
- [2024-06-12，YOLO-NAS：开启实时目标检测新纪元](https://mp.weixin.qq.com/s/ASahqSAHoMFvRvBlnq5OzQ)
- [2024-07-02，YOLOv10：实时目标检测的新星，引领AI视觉识别新纪元](https://mp.weixin.qq.com/s/JMPBJfMhUHg0472javPlpg)
- [2024-07-05，揭秘YOLO-World：颠覆传统，开启实时开放词汇检测新时代](https://mp.weixin.qq.com/s/WGMNf4u8bA-t534avapJQw)
- 微信公众号「魔方AI空间」
- [2024-05-26，CV再放大招 | YOLOv10：毫秒级实时端到端目标检测开源模型](https://mp.weixin.qq.com/s/yQlkvlYnCz1H3JWTXCKc_A)
- [2024-07-03，2万字长文｜YOLOv10的起源：YOLO系列的十年全面综述【YOLOv1-YOLOv10】(建议收藏)](https://mp.weixin.qq.com/s/1DYhy-flED1HwUX8YCQUPg)
- 微信公众号「槿墨AI」
- [2024-06-05，清华接棒YOLOv10开源，卷出毫秒级实时检测！](https://mp.weixin.qq.com/s/2DkhzmEllF5tom9FWHPz3g)
- [2024-06-12，【超全解读】Drone-YOLO：无人机图像中的实时目标检测](https://mp.weixin.qq.com/s/XQh4MxPwfsWzDnUoiW4jww)
- 微信公众号「计算机视觉工坊」
- [2024-06-21，目标检测的极限在哪里？LW-DETR：干翻YOLOv10！](https://mp.weixin.qq.com/s/64HnNUs7r133hTFsmgWpEA)
- 微信公众号「遥感与深度学习」
- [2024-06-28，论文赏读 | 结合YOLOv9和Mamba的遥感小目标检测](https://mp.weixin.qq.com/s/FqFDS2Ih0uA9nuWMpbBDEA)
- [2024-07-11，论文赏读 | Mamba YOLO: 基于SSM的YOLO 用于目标检测](https://mp.weixin.qq.com/s/Td-hwr-4UkIUheF7RJAi7Q)
- 微信公众号「算法美食屋」
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- 微信公众号「人工智能学起来」
- [2024-06-25，小目标检测重大进展！速度提升10倍，GPU内存占用少73.4％](https://mp.weixin.qq.com/s/xbUKj3mHOyou5KpCi-04xw)
## Videos
- bilibili「我是傅傅猪」
- [2022-12-14，自制深度学习推理框架](https://www.bilibili.com/video/BV1HV4y1A7H8)
- [2023-06-02，从零自制深度学习推理框架](https://www.bilibili.com/video/BV118411f7yM/)
## Star History
