🚀🚀🚀 YOLO is a great real-time one-stage object detection framework. This repository lists some awesome public YOLO object detection projects and datasets.

• Awesome-YOLO-Object-Detection
  • Summary
    • Famous YOLO
    • Extensional Frameworks
    • Awesome List
    • Paper and Code Overview
      • Paper Review
      • Code Review
    • Learning Resources
  • Other Versions of YOLO
    • PyTorch Implementation
    • C Implementation
    • CPP Implementation
    • ROS Implementation
    • Mojo Implementation
    • Rust Implementation
    • Go Implementation
    • CSharp Implementation
    • Tensorflow and Keras Implementation
    • PaddlePaddle Implementation
    • Caffe Implementation
    • MXNet Implementation
    • Web Implementation
    • Others
  • Lighter and Deployment Frameworks
    • High-performance Inference Engine
      • ONNX
      • TensorRT
      • DeepStream
      • OpenVINO
      • NCNN
      • MNN
      • Other Engine
    • NPU and FPGA Hardware Deployment
      • RK3588
      • FPGA
      • Other Hardware
    • Pruning Knoweldge-Distillation Quantization
      • Pruning
      • Quantization
      • Knoweldge-Distillation
    • Lightweight Backbones and FPN
  • Object Detection Applications
    • Open World Object Detection
    • Few-shot Object Detection
    • Small Object Detection
    • Multimodal Image Detection
    • Video Object Detection
    • Object Tracking
      • Multi-Object Tracking
      • Dynamic Object Tracking
    • Deep Reinforcement Learning
    • Motion Control Field
    • Super-Resolution Field
    • Spiking Neural Network
    • Attention and Transformer
    • Oriented Object Detection
    • Face Detection and Recognition
      • Face Detection
      • Face Recognition
    • Face Mask Detection
    • Social Distance Detection
    • Autonomous Driving Field Detection
      • Vehicle Detection
      • License Plate Detection and Recognition
      • Lane Detection
      • Driving Behavior Detection
      • Parking Slot Detection
      • Traffic Light Detection
      • Traffic Sign Detection
      • Crosswalk Detection
      • Traffic Accidents Detection
      • Road Damage Detection
    • Animal Detection
    • Helmet Detection
    • Hand Detection
    • Gesture Recognition
    • Action Detection
    • Emotion Recognition
    • Human Pose Estimation
    • Distance Measurement
    • Instance and Semantic Segmentation
    • 3D Object Detection
    • SLAM Field Detection
    • Industrial Defect Detection
    • SAR Image Detection
    • Safety Monitoring Field Detection
    • Anti-UAV Field Detection
    • Medical Field Detection
    • Chemistry Field Detection
    • Agricultural Field Detection
    • Sports Field Detection
    • Aerial Imagery Detection
    • Adverse Weather Conditions
    • Adversarial Attack and Defense
    • Camouflaged Detection
    • Game Field Detection
    • Automatic Annotation Tools
    • Feature Map Visualization
    • Object Detection Evaluation Metrics
    • GUI
      • Swift-Related
      • Flutter-Related
      • Streamlit-Related
      • Gradio-Related
      • QT-Related
      • PySide-Related
    • Other Applications
  • Object Detection Datasets
    • Datasets Share Platform
    • Datasets Tools
      • Data Annotation
      • Data Augmentation
      • Data Management
    • General Detection and Recognition Datasets
      • General Object Detection Datasets
      • General Object Recognition Datasets
    • Autonomous Driving Datasets
      • Diverse Autonomous Driving Datasets
      • Traffic Sign Detection Datasets
      • License Plate Detection and Recognition Datasets
    • Adverse Weather Datasets
    • Person Detection Datasets
    • Anti-UAV Datasets
    • Optical Aerial Imagery Datasets
    • Low-light Image Datasets
    • Infrared Image Datasets
    • SAR Image Datasets
    • Sonar Image Datasets
    • Multimodal Image Datasets
    • 3D Object Detection Datasets
    • Vehicle-to-Everything Field Datasets
    • Super-Resolution Field Datasets
    • Face Detection and Recognition Datasets
      • Face Detection Datasets
      • Face Recognition Datasets
  • Blogs
  • Videos

• Famous YOLO

  • YOLOv1 (Darknet ) : "You Only Look Once: Unified, Real-Time Object Detection". (CVPR 2016)

  • YOLOv2 (Darknet ) : "YOLO9000: Better, Faster, Stronger". (CVPR 2017)

  • YOLOv3 (Darknet ) : "YOLOv3: An Incremental Improvement". (arXiv 2018)

  • YOLOv4 (WongKinYiu/PyTorch_YOLOv4 ) : "YOLOv4: Optimal Speed and Accuracy of Object Detection". (arXiv 2020)

  • Scaled-YOLOv4 (WongKinYiu/ScaledYOLOv4 ) : "Scaled-YOLOv4: Scaling Cross Stage Partial Network". (CVPR 2021)

  • YOLOv5 : YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite. docs.ultralytics.com. YOLOv5 🚀 is the world's most loved vision AI, representing Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.

  • YOLOv6 : "YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications". (arXiv 2022).

  • YOLOv7 : "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors". (CVPR 2023).

  • YOLOv8 : NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite. docs.ultralytics.com

  • YOLOv9 : "YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information". (arXiv 2024)

  • MultimediaTechLab/YOLO : YOLO: Official Implementation of YOLOv9, YOLOv7, YOLO-RD. Welcome to the official implementation of YOLOv7 and YOLOv9, YOLO-RD. This repository will contains the complete codebase, pre-trained models, and detailed instructions for training and deploying YOLOv9.

  • YOLOv10 : "YOLOv10: Real-Time End-to-End Object Detection". (arXiv 2024)

  • YOLOv11 : NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite. Ultralytics YOLOv11 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

  • YOLOv12 : "YOLOv12: Attention-Centric Real-Time Object Detectors". (arXiv 2025)

  • YOLO-World | YOLO-World-v2 : "YOLO-World: Real-Time Open-Vocabulary Object Detection". (CVPR 2024). www.yoloworld.cc

  • YOLOE : "YOLOE: Real-Time Seeing Anything". (arXiv 2025).

• Extensional Frameworks

  • Qwen2.5-VL : Qwen2-VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud. "Qwen2.5-VL Technical Report". (arXiv 2025). 2025-01-26，Qwen2.5 VL! Qwen2.5 VL! Qwen2.5 VL!. "Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution". (arXiv 2024). "Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond". (arXiv 2023).

  • Visual-RFT : "Visual-RFT: Visual Reinforcement Fine-Tuning". (arXiv 2025).

  • VLM-R1 : VLM-R1: A stable and generalizable R1-style Large Vision-Language Model. Solve Visual Understanding with Reinforced VLMs. 2025-03-20，Improving Object Detection through Reinforcement Learning with VLM-R1.

  • Florence-2 : "Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks". (CVPR 2024).

  • maestro : VLM fine-tuning for everyone. maestro is a streamlined tool to accelerate the fine-tuning of multimodal models. By encapsulating best practices from our core modules, maestro handles configuration, data loading, reproducibility, and training loop setup. It currently offers ready-to-use recipes for popular vision-language models such as Florence-2, PaliGemma 2, and Qwen2.5-VL. maestro.roboflow.com

  • Autodistill : Images to inference with no labeling (use foundation models to train supervised models). Autodistill uses big, slower foundation models to train small, faster supervised models. Using autodistill, you can go from unlabeled images to inference on a custom model running at the edge with no human intervention in between. docs.autodistill.com

  • EdgeYOLO : 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). "EdgeYOLO: An Edge-Real-Time Object Detector". (arXiv 2023)

  • YOLOX : "YOLOX: Exceeding YOLO Series in 2021". (arXiv 2021)

  • YOLOR : "You Only Learn One Representation: Unified Network for Multiple Tasks". (arXiv 2021)

  • YOLOF : "You Only Look One-level Feature". (CVPR 2021).

  • YOLOS : "You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection". (NeurIPS 2021)

  • 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)

  • YOLO-NAS : Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS. www.supergradients.com. YOLO-NAS and YOLO-NAS-POSE architectures are out! The new YOLO-NAS delivers state-of-the-art performance with the unparalleled accuracy-speed performance, outperforming other models such as YOLOv5, YOLOv6, YOLOv7 and YOLOv8.

  • LeYOLO : "LeYOLO, New Scalable and Efficient CNN Architecture for Object Detection". (arXiv 2024)

  • DynamicDet : "DynamicDet: A Unified Dynamic Architecture for Object Detection". (CVPR 2023)

  • DINO : "DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection". (ICLR 2023).

  • GroundingDINO : "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection". (ECCV 2024).

  • RT-DETR | RT-DETRv2 : "DETRs Beat YOLOs on Real-time Object Detection". (CVPR 2024). "RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer". (arXiv 2024).

  • EasyCV : An all-in-one toolkit for computer vision. "YOLOX-PAI: An Improved YOLOX, Stronger and Faster than YOLOv6". (arXiv 2022).

  • YOLACT & YOLACT++ : You Only Look At CoefficienTs. (ICCV 2019, IEEE TPAMI 2020)

  • Alpha-IoU : "Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression". (NeurIPS 2021)

  • CIoU : Complete-IoU (CIoU) Loss and Cluster-NMS for Object Detection and Instance Segmentation (YOLACT). (AAAI 2020, IEEE TCYB 2021)

  • 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)

  • doubleZ0108/Data-Augmentation : General Data Augmentation Algorithms for Object Detection(esp. Yolo).

• Awesome List

  • awesome-yolo-object-detection : 🚀🚀🚀 A collection of some awesome public YOLO object detection series projects and the related object detection datasets.

  • srebroa/awesome-yolo : 🚀 ⭐ The list of the most popular YOLO algorithms - awesome YOLO.

  • Bubble-water/YOLO-Summary : YOLO-Summary.

  • WZMIAOMIAO/deep-learning-for-image-processing : deep learning for image processing including classification and object-detection etc.

  • hoya012/deep_learning_object_detection : A paper list of object detection using deep learning.

  • amusi/awesome-object-detection : Awesome Object Detection.

  • wenhwu/awesome-remote-sensing-change-detection : List of datasets, codes, and contests related to remote sensing change detection.

  • ZHOUYI1023/awesome-radar-perception : A curated list of radar datasets, detection, tracking and fusion.

  • lartpang/awesome-segmentation-saliency-dataset : A collection of some datasets for segmentation / saliency detection. Welcome to PR...😄

  • TianhaoFu/Awesome-3D-Object-Detection : Papers, code and datasets about deep learning for 3D Object Detection.

  • xahidbuffon/Awesome_Underwater_Datasets : Pointers to large-scale underwater datasets and relevant resources.

  • M-3LAB/awesome-industrial-anomaly-detection : Paper list and datasets for industrial image anomaly detection.

  • ZhangXiwuu/Awesome_visual_place_recognition_datasets : A curated list of Visual Place Recognition (VPR)/ loop closure detection (LCD) datasets.

  • ari-dasci/OD-WeaponDetection : Datasets for weapon detection based on image classification and object detection tasks.

  • DLLXW/objectDetectionDatasets : 目标检测数据集制作:VOC,COCO,YOLO等常用数据集格式的制作和互相转换脚本。

  • kuanhungchen/awesome-tiny-object-detection : 🕶 A curated list of Tiny Object Detection papers and related resources.

• Paper and Code Overview

  • Paper Review

    • 52CV/CV-Surveys : 计算机视觉相关综述。包括目标检测、跟踪........

    • 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)

    • "A Review of Yolo Algorithm Developments". (Procedia Computer Science 2022)

  • Code Review

    • iscyy/ultralyticsPro : 🔥🔥🔥 专注于YOLO11，YOLOv8、YOLOv10、RT-DETR、YOLOv7、YOLOv5改进模型，Support to improve backbone, neck, head, loss, IoU, NMS and other modules🚀

    • MMDetection : OpenMMLab Detection Toolbox and Benchmark. mmdetection.readthedocs.io. (arXiv 2019)

    • MMYOLO : OpenMMLab YOLO series toolbox and benchmark. Implemented RTMDet, RTMDet-Rotated,YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOX, PPYOLOE, etc. mmyolo.readthedocs.io/zh_CN/dev/

    • iscyy/yoloair : 🔥🔥🔥 专注于YOLO改进模型，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。

    • iscyy/yoloair2 : ☁️💡🎈专注于改进YOLOv7，Support to improve Backbone, Neck, Head, Loss, IoU, NMS and other modules.

    • 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！》"

    • 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 : 🚀 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 : 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

    • 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》"

    • PaddlePaddle/PaddleYOLO : 🚀🚀🚀 YOLO series of PaddlePaddle implementation, PP-YOLOE+, YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOX, YOLOv5u, YOLOv7u, RTMDet and so on. 🚀🚀🚀

    • 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 : Cver4s：Computer vision algorithm code base.

    • 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

  • zjhellofss/KuiperLLama : 《动手自制大模型推理框架》。KuiperLLama 动手自制大模型推理框架，支持LLama2/3和Qwen2.5。校招、秋招、春招、实习好项目，带你从零动手实现支持LLama2/3和Qwen2.5的大模型推理框架。

  • zjhellofss/KuiperInfer : 校招、秋招、春招、实习好项目！带你从零实现一个高性能的深度学习推理库，支持大模型 llama2 、Unet、Yolov5、Resnet等模型的推理。Implement a high-performance deep learning inference library step by step。

  • zjhellofss/kuiperdatawhale : 从零自制深度学习推理框架。

  • 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

  • yjh0410/PyTorch_YOLO_Tutorial : YOLO Tutorial.

  • HuKai97/yolov5-5.x-annotations : 一个基于yolov5-5.0的中文注释版本！

  • crkk-feng/yolov5-annotations : A Chinese annotated version of yolov5-5.0.

  • XiaoJiNu/yolov5-v6-chinese-comment : yolov5-v6版本注释。

  • 1131624548/About-YOLOv5-7-0 : YOLOv5代码注释。

  • zyds/yolov5-code : 手把手带你实战 YOLOv5。

• PyTorch Implementation

  • ultralytics/yolov3 : YOLOv3 in PyTorch > ONNX > CoreML > TFLite.

  • eriklindernoren/PyTorch-YOLOv3 : Minimal PyTorch implementation of YOLOv3.

  • Tianxiaomo/pytorch-YOLOv4 : PyTorch ,ONNX and TensorRT implementation of YOLOv4.

  • ayooshkathuria/pytorch-yolo-v3 : A PyTorch implementation of the YOLO v3 object detection algorithm.

  • WongKinYiu/PyTorch_YOLOv4 : PyTorch implementation of YOLOv4.

  • argusswift/YOLOv4-pytorch : This is a pytorch repository of YOLOv4, attentive YOLOv4 and mobilenet YOLOv4 with PASCAL VOC and COCO.

  • longcw/yolo2-pytorch : YOLOv2 in PyTorch.

  • 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 : 这是一个YoloV5-pytorch的源码，可以用于训练自己的模型。

  • 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 : 这是一个YoloV4-tiny-pytorch的源码，可以用于训练自己的模型。

  • bubbliiiing/yolov3-pytorch : 这是一个yolo3-pytorch的源码，可以用于训练自己的模型。

  • 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 : 这是一个yolov7的库，可以用于训练自己的数据集。

  • 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 : Full implementation of YOLOv3 in PyTorch.

  • ruiminshen/yolo2-pytorch : PyTorch implementation of the YOLO (You Only Look Once) v2.

  • DeNA/PyTorch_YOLOv3 : Implementation of YOLOv3 in PyTorch.

  • abeardear/pytorch-YOLO-v1 : an experiment for yolo-v1, including training and testing.

  • wuzhihao7788/yolodet-pytorch : reproduce the YOLO series of papers in pytorch, including YOLOv4, PP-YOLO, YOLOv5，YOLOv3, etc.

  • uvipen/Yolo-v2-pytorch : YOLO for object detection tasks.

  • Peterisfar/YOLOV3 : yolov3 by pytorch.

  • 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 : Pytorch and ncnn implementation of PPYOLOE、YOLOX、PPYOLO、PPYOLOv2、SOLOv2 an so on.

  • pjh5672/YOLOv1 : YOLOv1 implementation using PyTorch.

  • pjh5672/YOLOv2 : YOLOv2 implementation using PyTorch.

  • pjh5672/YOLOv3 : YOLOv3 implementation using PyTorch.

  • Iywie/pl_YOLO : YOLOv7, YOLOX and YOLOv5 are working right now.

  • 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 : This a clean and easy-to-use implementation of YOLOv7 in PyTorch, made with ❤️ by Theos AI.

• C Implementation

  • ggml : Tensor library for machine learning. Written in C.

  • rockcarry/ffcnn : ffcnn is a cnn neural network inference framework, written in 600 lines C language.

  • ar7775/Object-Detection-System-Yolo : Object Detection System.

  • lstuma/YOLO_utils : A few utilities for the YOLO project implemented in C for extra speed.

  • RajneeshKumar12/yolo-detection-app : Yolo app for object detection.

  • Deyht/CIANNA : CIANNA - Convolutional Interactive Artificial Neural Networks by/for Astrophysicists.

• CPP Implementation

  • walktree/libtorch-yolov3 : A Libtorch implementation of the YOLO v3 object detection algorithm, written with pure C++.

  • yasenh/libtorch-yolov5 : A LibTorch inference implementation of the yolov5.

  • Nebula4869/YOLOv5-LibTorch : Real time object detection with deployment of YOLOv5 through LibTorch C++ API.

  • ncdhz/YoloV5-LibTorch : 一个 C++ 版本的 YoloV5 封装库.

  • Rane2021/yolov5_train_cpp_inference : yolov5训练和c++推理代码，效果出色。

  • 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 : 使用opencv模块部署yolov5-6.0版本。

  • UNeedCryDear/yolov5-seg-opencv-onnxruntime-cpp : yolov5 segmentation with onnxruntime and opencv.

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

  • UNeedCryDear/yolov8-opencv-onnxruntime-cpp : detection and instance segmentation of yolov8,use onnxruntime and opencv.

• ROS Implementation

  • mgonzs13/yolov8_ros : Ultralytics YOLOv8, YOLOv9, YOLOv10, YOLOv11 for ROS 2.

  • leggedrobotics/darknet_ros : Real-Time Object Detection for ROS.

  • engcang/ros-yolo-sort : YOLO and SORT, and ROS versions of them.

  • chrisgundling/YoloLight : Tiny-YOLO-v2 ROS Node for Traffic Light Detection.

  • Ar-Ray-code/YOLOX-ROS : YOLOX + ROS2 object detection package.

  • Ar-Ray-code/YOLOv5-ROS : YOLOv5 + ROS2 object detection package.

  • Tossy0423/yolov4-for-darknet_ros : This is the environment in which YOLO V4 is ported to darknet_ros.

  • qianmin/yolov5_ROS : run YOLOv5 in ROS，ROS使用YOLOv5。

  • ailllist/yolov5_ROS : yolov5 for ros, not webcam.

  • 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 : Connect Yolov5 detection module and DeepSort tracking module via ROS.

  • U07157135/ROS2-with-YOLOv5 : 在無人機上以ROS2技術實現YOLOv5物件偵測。

  • lukazso/yolov6-ros : ROS package for YOLOv6.

  • 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 : ROS package for official YOLOv7.

  • phuoc101/yolov7_ros : ROS package for official YOLOv7.

  • ConfusionTechnologies/ros-yolov5-node : For ROS2, uses ONNX GPU Runtime to inference YOLOv5.

  • Ar-Ray-code/darknet_ros_fp16 : darknet + ROS2 Humble + OpenCV4 + CUDA 11（cuDNN, Jetson Orin）.

  • 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 : Integration of "Yolov7 StrongSort" with ROS for real time object tracking.

  • af-doom/yolov8_ros_tensorrt- : This is a YOLOv8 project based on ROS implementation, where YOLOv8 uses Tensorrt acceleration.

  • 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 : Inference with YOLOv5, OpenCV 4.5.4 DNN, C++, ROS and Python.

  • mgonzs13/yolov8_ros : ROS 2 wrap for Ultralytics YOLOv8 to perform object detection.

  • 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 : EdgeYOLO + ROS2 object detection package.

  • 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 : ROS/ROS2 package for Ultralytics YOLOv8 real-time object detection.

• Mojo Implementation

  • taalhaataahir0102/Mojo-Yolo : Mojo-Yolo.

• Rust Implementation

  • Candle : Minimalist ML framework for Rust.

  • Tokenizers : 💥 Fast State-of-the-Art Tokenizers optimized for Research and Production. huggingface.co/docs/tokenizers

  • Safetensors : Simple, safe way to store and distribute tensors. huggingface.co/docs/safetensors

  • Burn : Burn - A Flexible and Comprehensive Deep Learning Framework in Rust. burn-rs.github.io/

  • TensorFlow Rust : Rust language bindings for TensorFlow.

  • tch-rs : Rust bindings for the C++ api of PyTorch.

  • dfdx : Deep learning in Rust, with shape checked tensors and neural networks.

  • tract : Sonos' Neural Network inference engine. Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference

  • ort : A Rust wrapper for ONNX Runtime. docs.rs/ort

  • usls : A Rust library integrated with ONNXRuntime, providing a collection of Computer Vison and Vision-Language models.

  • ptaxom/pnn : pnn is 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 : YOLOv5 Inference with ONNX & OpenCV in Rust.

  • masc-it/yolov5-api-rust : Rust API to run predictions with YoloV5 models.

  • AndreyGermanov/yolov8_onnx_rust : YOLOv8 inference using Rust.

  • igor-yusupov/rusty-yolo : rusty-yolo.

  • gsuyemoto/yolo-rust : Run YOLO computer vision model using Rust and OpenCV and/or Torch.

  • alianse777/darknet-rust : A Rust wrapper for Darknet, an open source neural network framework written in C and CUDA. pjreddie.com/darknet/

  • 12101111/yolo-rs : Yolov3 & Yolov4 with TVM and rust.

  • TKGgunter/yolov4_tiny_rs : A rust implementation of yolov4_tiny algorithm.

  • flixstn/You-Only-Look-Once : A Rust implementation of Yolo for object detection and tracking.

  • lenna-project/yolo-plugin : Yolo Object Detection Plugin for Lenna.

  • laclouis5/globox-rs : Object detection toolbox for parsing, converting and evaluating bounding box annotations.

  • 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 : go-darknet: Go bindings for Darknet (Yolo V4, Yolo V7-tiny, Yolo V3).

    • 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 : Go implementation of the yolo v3 object detection system.

    • wimspaargaren/yolov5 : Go implementation of the yolo v5 object detection system.

    • genert/real_time_object_detection_go : Real Time Object Detection with OpenCV, Go, and Yolo v4.

  • CSharp Implementation

    • ML.NET : ML.NET is an open source and cross-platform machine learning framework for .NET.

    • TorchSharp : A .NET library that provides access to the library that powers PyTorch.

    • TensorFlow.NET : .NET Standard bindings for Google's TensorFlow for developing, training and deploying Machine Learning models in C# and F#.

    • DlibDotNet : Dlib .NET wrapper written in C++ and C# for Windows, MacOS, Linux and iOS.

    • DiffSharp : DiffSharp: Differentiable Functional Programming.

    • 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

    • techwingslab/yolov5-net : YOLOv5 object detection with C#, ML.NET, ONNX.

    • sstainba/Yolov8.Net : A .net 6 implementation to use Yolov5 and Yolov8 models via the ONNX Runtime.

    • Alturos.Yolo : C# Yolo Darknet Wrapper (real-time object detection).

    • ivilson/Yolov7net : Yolov7 Detector for .Net 6.

    • 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》"

    • keijiro/TinyYOLOv2Barracuda : Tiny YOLOv2 on Unity Barracuda.

    • derenlei/Unity_Detection2AR : Localize 2D image object detection in 3D Scene with Yolo in Unity Barracuda and ARFoundation.

    • died/YOLO3-With-OpenCvSharp4 : Demo of implement YOLO v3 with OpenCvSharp v4 on C#.

    • mbaske/yolo-unity : YOLO In-Game Object Detection for Unity (Windows).

    • BobLd/YOLOv4MLNet : Use the YOLO v4 and v5 (ONNX) models for object detection in C# using ML.Net.

    • keijiro/YoloV4TinyBarracuda : YoloV4TinyBarracuda is an implementation of the YOLOv4-tiny object detection model on the Unity Barracuda neural network inference library.

    • zhang8043/YoloWrapper : C#封装YOLOv4算法进行目标检测。

    • maalik0786/FastYolo : Fast Yolo for fast initializing, object detection and tracking.

    • Uehwan/CSharp-Yolo-Video : C# Yolo for Video.

    • HTTP123-A/HumanDetection_Yolov5NET : YOLOv5 object detection with ML.NET, ONNX.

    • Celine-Hsieh/Hand_Gesture_Training--yolov4 : Recognize the gestures' features using the YOLOv4 algorithm.

    • lin-tea/YOLOv5DetectionWithCSharp : YOLOv5s inference In C# and Training In Python.

    • 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 : Yolo Object Detection Application for RTSP streams.

    • TimothyMeadows/Yolo6.NetCore : You Only Look Once (v6) for .NET Core LTS.

    • mwetzko/EasyYoloDarknet : EasyYoloDarknet.

    • mwetzko/EasyYoloDarknet : Windows optimized Yolo / Darknet Compile, Train and Detect.

    • 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 : High performance object detector based on YOLO series.

    • thisistherealdiana/YOLO_project : YOLO project made by Diana Kereselidze.

    • oujunke/Yolo5Net : Yolo5实现于TensorFlow.Net.

    • wojciechp6/YOLO-UnityBarracuda : Object detection app build on Unity Barracuda and YOLOv2 Tiny.

    • RaminAbbaszadi/YoloWrapper-WPF : WPF (C#) Yolo Darknet Wrapper.

    • fengyhack/YoloWpf : GUI demo for Object Detection with YOLO and OpenCVSharp.

    • hanzhuang111/Yolov5Wpf : 使用ML.NET部署YOLOV5 的ONNX模型。

    • 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 : YOLO object detection using webcam in winforms.

    • 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 : yolov5 annotation viewer.

    • developer-ken/YoloPredictorMLDotNet : YoloPredictorMLDotNet.

    • LionelC-Kyo/CSharp_YoloV5_Torch : Run Yolo V5 in C# By Torch.

    • 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 : Use the YOLO v3 (ONNX) model for object detection in C# using ML.Net.

    • zgabi/Yolo.Net : zgabi/Yolo.Net

    • aliardan/RoadMarkingDetection : Road markings detection using yolov5 model based on ONNX.

    • TimothyMeadows/Yolo5.NetCore : You Only Look Once (v5) for .NET Core LTS.

    • AD-HO/YOLOv5-ML.NET : Inferencing Yolov5 ONNX model using ML.NET and ONNX Runtime.

    • ToxicSkill/YOLOV7-Webcam-inference : Simple WPF program for webcam inference with yoloV7 models.

    • aliardan/RoadMarkingDetection : Road markings detection using yolov5 model based on ONNX.

    • rabbitsun2/csharp_and_microsoft_ml_and_yolo_v5_sample : C#, Microsoft ML, Yolo v5, Microsoft ML.DNN, OpenCVSharp4 연계 프로젝트.

    • hsysfan/YOLOv5-Seg-OnnxRuntime : YOLOv5 Segmenation Implementation in C# and OnnxRuntime.

    • 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 : 🔥 TensorFlow Code for technical report: "YOLOv3: An Incremental Improvement".

    • zzh8829/yolov3-tf2 : YoloV3 Implemented in Tensorflow 2.0.

    • 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 : tensorflow implementation of 'YOLO : Real-Time Object Detection'.

    • llSourcell/YOLO_Object_Detection : This is the code for "YOLO Object Detection" by Siraj Raval on Youtube.

    • wizyoung/YOLOv3_TensorFlow : Complete YOLO v3 TensorFlow implementation. Support training on your own dataset.

    • theAIGuysCode/yolov4-deepsort : Object tracking implemented with YOLOv4, DeepSort, and TensorFlow.

    • mystic123/tensorflow-yolo-v3 : Implementation of YOLO v3 object detector in Tensorflow (TF-Slim).

    • hizhangp/yolo_tensorflow : Tensorflow implementation of YOLO, including training and test phase.

    • nilboy/tensorflow-yolo : tensorflow implementation of 'YOLO : Real-Time Object Detection'(train and test).

    • qqwweee/keras-yolo3 : A Keras implementation of YOLOv3 (Tensorflow backend).

    • allanzelener/YAD2K : YAD2K: Yet Another Darknet 2 Keras.

    • experiencor/keras-yolo2 : YOLOv2 in Keras and Applications.

    • experiencor/keras-yolo3 : Training and Detecting Objects with YOLO3.

    • SpikeKing/keras-yolo3-detection : YOLO v3 物体检测算法。

    • xiaochus/YOLOv3 : Keras implementation of yolo v3 object detection.

    • 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 : 这是一个YoloV4-keras的源码，可以用于训练自己的模型。

    • 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 : 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 : YOLOv3 implementation in TensorFlow 2.3.1.

    • miemie2013/Keras-YOLOv4 : PPYOLO AND YOLOv4.

    • Ma-Dan/keras-yolo4 : A Keras implementation of YOLOv4 (Tensorflow backend).

    • miranthajayatilake/YOLOw-Keras : YOLOv2 Object Detection w/ Keras (in just 20 lines of code).

    • maiminh1996/YOLOv3-tensorflow : Re-implement YOLOv3 with TensorFlow.

    • Stick-To/Object-Detection-Tensorflow : Object Detection API Tensorflow.

    • avBuffer/Yolov5_tf : Yolov5/Yolov4/ Yolov3/ Yolo_tiny in tensorflow.

    • ruiminshen/yolo-tf : TensorFlow implementation of the YOLO (You Only Look Once).

    • xiao9616/yolo4_tensorflow2 : yolo 4th edition implemented by tensorflow2.0.

    • sicara/tf2-yolov4 : A TensorFlow 2.0 implementation of YOLOv4: Optimal Speed and Accuracy of Object Detection.

    • LongxingTan/Yolov5 : Efficient implementation of YOLOV5 in TensorFlow2.

    • 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 : 🚀 基于TensorFlow.js的YOLOv5实时目标检测项目。

    • Burf/TFDetection : A Detection Toolbox for Tensorflow2.

    • taipingeric/yolo-v4-tf.keras : A simple tf.keras implementation of YOLO v4.

    • 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 : Object Detection toolkit based on PaddlePaddle. "PP-YOLO: An Effective and Efficient Implementation of Object Detector". (arXiv 2020)

    • nemonameless/PaddleDetection_YOLOv5 : YOLOv5 of PaddleDetection, Paddle implementation of YOLOv5.

    • nemonameless/PaddleDetection_YOLOX : Paddle YOLOX, 51.8% on COCO val by YOLOX-x, 44.6% on YOLOX-ConvNeXt-s.

    • nemonameless/PaddleDetection_YOLOset : Paddle YOLO set: YOLOv3, PPYOLO, PPYOLOE, YOLOX, YOLOv5, YOLOv7 and so on.

    • miemie2013/Paddle-YOLOv4 : Paddle-YOLOv4.

    • Sharpiless/PaddleDetection-Yolov5 : 基于Paddlepaddle复现yolov5，支持PaddleDetection接口。

    • Nioolek/PPYOLOE_pytorch : An unofficial implementation of Pytorch version PP-YOLOE,based on Megvii YOLOX training code.

  • Caffe Implementation

    • ChenYingpeng/caffe-yolov3 : A real-time object detection framework of Yolov3/v4 based on caffe.

    • ChenYingpeng/darknet2caffe : Convert darknet weights to caffemodel.

    • eric612/Caffe-YOLOv3-Windows : A windows caffe implementation of YOLO detection network.

    • Harick1/caffe-yolo : Caffe for YOLO.

    • choasup/caffe-yolo9000 : Caffe for YOLOv2 & YOLO9000.

    • gklz1982/caffe-yolov2 : caffe-yolov2.

  • MXNet Implementation

    • Gluon CV Toolkit : GluonCV provides implementations of the state-of-the-art (SOTA) deep learning models in computer vision.

    • zhreshold/mxnet-yolo : YOLO: You only look once real-time object detector.

  • Web Implementation

    • ModelDepot/tfjs-yolo-tiny : In-Browser Object Detection using Tiny YOLO on Tensorflow.js.

    • justadudewhohacks/tfjs-tiny-yolov2 : Tiny YOLO v2 object detection with tensorflow.js.

    • reu2018DL/YOLO-LITE : YOLO-LITE is a web implementation of YOLOv2-tiny.

    • mobimeo/node-yolo : Node bindings for YOLO/Darknet image recognition library.

    • 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 : YOLO v3 and Tiny YOLO v1, v2, v3 with Tensorflow.js.

    • zqingr/tfjs-yolov3 : A Tensorflow js implementation of YOLOv3 and YOLOv3-tiny.

    • bennetthardwick/darknet.js : A NodeJS wrapper of pjreddie's darknet / yolo.

    • nihui/ncnn-webassembly-yolov5 : Deploy YOLOv5 in your web browser with ncnn and webassembly.

    • muhk01/Yolov5-on-Flask : Running YOLOv5 through web browser using Flask microframework.

    • 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 : Repo for Web Application for Vehicle detection from Satellite Imagery using YOLOv5 model.

    • Devmawi/BlazorObjectDetection-Sample : A sample for demonstrating online execution of an onnx model by a Blazor app.

    • Hyuto/yolov5-onnxruntime-web : YOLOv5 right in your browser with onnxruntime-web.

  • Others

    • jinfagang/yolov7_d2 : 🔥🔥🔥🔥 (Earlier YOLOv7 not official one) YOLO with Transformers and Instance Segmentation, with TensorRT acceleration! 🔥🔥🔥

    • yang-0201/YOLOv6_pro : Make it easier for yolov6 to change the network structure.

    • 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.

    • fcakyon/yolov5-pip : Packaged version of ultralytics/yolov5.

    • kadirnar/yolov6-pip : Packaged version of yolov6 model.

    • kadirnar/yolov7-pip : Packaged version of yolov7 model.

    • kadirnar/torchyolo : PyTorch implementation of YOLOv5, YOLOv6, YOLOv7, YOLOX.

    • CvPytorch : CvPytorch is an open source COMPUTER VISION toolbox based on PyTorch.

    • Holocron : PyTorch implementations of recent Computer Vision tricks (ReXNet, RepVGG, Unet3p, YOLOv4, CIoU loss, AdaBelief, PolyLoss).

    • DL-Practise/YoloAll : YoloAll is a collection of yolo all versions. you you use YoloAll to test yolov3/yolov5/yolox/yolo_fastest.

    • msnh2012/Msnhnet : (yolov3 yolov4 yolov5 unet ...)A mini pytorch inference framework which inspired from darknet.

    • xinghanliuying/yolov5-trick : 基于yolov5的改进库。

    • BMW-InnovationLab/BMW-YOLOv4-Training-Automation : YOLOv4-v3 Training Automation API for Linux.

    • AntonMu/TrainYourOwnYOLO : Train a state-of-the-art yolov3 object detector from scratch!

    • madhawav/YOLO3-4-Py : A Python wrapper on Darknet. Compatible with YOLO V3.

    • 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 : Machine Learning and AI Mobile Application.

    • kadirnar/Minimal-Yolov6 : Minimal-Yolov6.

    • 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 : 🔥🔥🔥 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 : 🔥 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 : 🔥 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 : yolov5-darknet support yaml && cfg.

    • Code-keys/yolo-darknet : YOLO-family complemented by darknet. yolov5 yolov7 et al ...

    • pooya-mohammadi/deep_utils : A toolkit full of handy functions including most used models and utilities for deep-learning practitioners!

    • yl-jiang/YOLOSeries : YOLO Series.

    • yjh0410/FreeYOLO : FreeYOLO is inspired by many other excellent works, such as YOLOv7 and YOLOX.

    • open-yolo/yolov7 : Improved and packaged version of WongKinYiu/yolov7.

    • iloveai8086/YOLOC : 🚀YOLOC is Combining different modules to build an different Object detection model.

    • miemie2013/miemiedetection : Pytorch and ncnn implementation of PPYOLOE、YOLOX、PPYOLO、PPYOLOv2、SOLOv2 an so on.

    • RyanCCC/YOLOSeries : YOLO算法的实现。

    • HuKai97/YOLOX-Annotations : 一个YOLOX的中文注释版本，供大家参考学习！

    • isLinXu/YOLOv8_Efficient : 🚀Simple and efficient use for Ultralytics yolov8🚀

    • z1069614715/objectdetection_script : 一些关于目标检测的脚本的改进思路代码。

• High-performance Inference Engine

  高性能推理引擎

  • ONNX

    • ONNX Runtime : ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator. onnxruntime.ai

    • ONNX : Open Neural Network Exchange. Open standard for machine learning interoperability. onnx.ai

    • ONNXMLTools : ONNXMLTools enables you to convert models from different machine learning toolkits into ONNX. onnx.ai

    • xboot/libonnx : A lightweight, portable pure C99 onnx inference engine for embedded devices with hardware acceleration support.

    • kraiskil/onnx2c : Open Neural Network Exchange to C compiler. Onnx2c is a ONNX 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 : Sonos' Neural Network inference engine. Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference

    • ort : A Rust wrapper for ONNX Runtime. docs.rs/ort

    • onnxruntime-rs : This is an attempt at a Rust wrapper for Microsoft's ONNX Runtime (version 1.8).

    • Wonnx : Wonnx is a GPU-accelerated ONNX inference run-time written 100% in Rust, ready for the web.

    • altius : Small ONNX inference runtime written in Rust.

    • Hyuto/yolo-nas-onnx : Inference YOLO-NAS ONNX model. hyuto.github.io/yolo-nas-onnx/

    • DanielSarmiento04/yolov10cpp : Implementation of yolo v10 in c++ std 17 over opencv and onnxruntime.

  • TensorRT

    • 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

    • 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

    • NVIDIA/TensorRT-Model-Optimizer : TensorRT Model Optimizer is a unified library of state-of-the-art model optimization techniques such as quantization, pruning, distillation, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM or TensorRT to optimize inference speed on NVIDIA GPUs. nvidia.github.io/TensorRT-Model-Optimizer

    • kalfazed/tensorrt_starter : This repository give a guidline to learn CUDA and TensorRT from the beginning.

    • wang-xinyu/tensorrtx : TensorRTx aims to implement popular deep learning networks with tensorrt network definition APIs.

    • laugh12321/TensorRT-YOLO : 🚀 Easier & Faster YOLO Deployment Toolkit for NVIDIA 🛠️. 🚀 TensorRT-YOLO is an easy-to-use, extremely efficient inference deployment tool for the YOLO series designed specifically for NVIDIA devices. The project not only integrates TensorRT plugins to enhance post-processing but also utilizes CUDA kernels and CUDA graphs to accelerate inference. 🚀 TensorRT-YOLO 是一款专为 NVIDIA 设备设计的易用灵活、极致高效的YOLO系列推理部署工具。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。

    • 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.

    • shouxieai/tensorRT_Pro : C++ library based on tensorrt integration.

    • shouxieai/infer : A new tensorrt integrate. Easy to integrate many tasks.

    • Melody-Zhou/tensorRT_Pro-YOLOv8 : This repository is based on 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！！！🚀🚀🚀

    • FeiYull/TensorRT-Alpha : 🔥🔥🔥TensorRT for YOLOv8、YOLOv8-Pose、YOLOv8-Seg、YOLOv8-Cls、YOLOv7、YOLOv6、YOLOv5、YOLONAS......🚀🚀🚀CUDA IS ALL YOU NEED.🍎🍎🍎

    • zhiqwang/yolort : yolort is a runtime stack for yolov5 on specialized accelerators such as tensorrt, libtorch, onnxruntime, tvm and ncnn. zhiqwang.com/yolort

    • 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。

    • 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 : C++ and Python implementations of YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOv9, YOLOv10, YOLOv11 inference.

    • triple-Mu/YOLOv8-TensorRT : YOLOv8 using TensorRT accelerate !

    • emptysoal/TensorRT-YOLOv8-ByteTrack : An object tracking project with YOLOv8 and ByteTrack, speed up by C++ and TensorRT.

    • Linaom1214/TensorRT-For-YOLO-Series : tensorrt for yolo series (YOLOv10,YOLOv9,YOLOv8,YOLOv7,YOLOv6,YOLOX,YOLOv5), nms plugin support.

    • spacewalk01/yolov11-tensorrt : C++ implementation of YOLOv11 using TensorRT API.

    • 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 : Based on tensorrt v8.0+, deploy detect, pose, segment, tracking of YOLOv8 with C++ and python api.

    • hamdiboukamcha/yolov10-tensorrt : YOLOv10 C++ TensorRT : Real-Time End-to-End Object Detection.

    • VIDIA-AI-IOT/torch2trt : An easy to use PyTorch to TensorRT converter.

    • 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 : ⚡️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 : 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 : 🔥🔥🔥🔥🔥🔥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 : Yolov5 TensorRT Implementations.

    • lewes6369/TensorRT-Yolov3 : TensorRT for Yolov3.

    • CaoWGG/TensorRT-YOLOv4 :tensorrt5, yolov4, yolov3,yolov3-tniy,yolov3-tniy-prn.

    • isarsoft/yolov4-triton-tensorrt : YOLOv4 on Triton Inference Server with TensorRT.

    • TrojanXu/yolov5-tensorrt : A tensorrt implementation of yolov5.

    • 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 : 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 : YOLOv5 in TensorRT.

    • Monday-Leo/YOLOv7_Tensorrt : A simple implementation of Tensorrt YOLOv7.

    • ibaiGorordo/ONNX-YOLOv6-Object-Detection : Python scripts performing object detection using the YOLOv6 model in ONNX.

    • ibaiGorordo/ONNX-YOLOv7-Object-Detection : Python scripts performing object detection using the YOLOv7 model in ONNX.

    • triple-Mu/yolov7 : End2end TensorRT YOLOv7.

    • hewen0901/yolov7_trt : yolov7目标检测算法的c++ tensorrt部署代码。

    • tsutof/tiny_yolov2_onnx_cam : Tiny YOLO v2 Inference Application with NVIDIA TensorRT.

    • Monday-Leo/Yolov5_Tensorrt_Win10 : A simple implementation of tensorrt yolov5 python/c++🔥

    • Wulingtian/yolov5_tensorrt_int8 : TensorRT int8 量化部署 yolov5s 模型，实测3.3ms一帧！

    • Wulingtian/yolov5_tensorrt_int8_tools : tensorrt int8 量化yolov5 onnx模型。

    • 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 : Python scripts performing object detection using the YOLOv8 model in ONNX.

    • we0091234/yolov8-tensorrt : yolov8 tensorrt 加速.

    • FeiYull/yolov8-tensorrt : YOLOv8的TensorRT+CUDA加速部署，代码可在Win、Linux下运行。

    • 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 : 🐰 一套代码同时支持YOLOV5, V6, V7, V8 TRT推理 ™️ PYTHON ✈️

    • Psynosaur/Jetson-SecVision : Person detection for Hikvision DVR with AlarmIO ports, uses TensorRT and yolov4.

    • tatsuya-fukuoka/yolov7-onnx-infer : Inference with yolov7's onnx model.

    • 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 : 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 : TensorRT for YOLOv10 with CUDA.

    • PrinceP/tensorrt-cpp-for-onnx : Tensorrt codebase to inference in c++ for all major neural arch using onnx.

    • hamdiboukamcha/Yolo-V10-cpp-TensorRT : The YOLOv10 C++ TensorRT Project in C++ and optimized using NVIDIA TensorRT.

    • DataXujing/YOLOv12-TensorRT : YOLOv12 TensorRT 端到端模型加速推理和INT8量化实现。

  • DeepStream

    • NVIDIA-AI-IOT/deepstream_reference_apps : Reference Apps using DeepStream 6.1.

    • NVIDIA-AI-IOT/deepstream_python_apps : DeepStream SDK Python bindings and sample applications.

    • NVIDIA-AI-IOT/deepstream_python_apps : This repository provides YOLOV5 GPU optimization sample.

    • 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 : Describe how to use yolov5 in Deepstream 5.0.

    • 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 : YOLOv8 by Ultralytics in DeepStream 6.1.

    • bharath5673/Deepstream : yolov2 ,yolov5 ,yolov6 ,yolov7 ,yolov7,yolovR ,yolovX on deepstream.

    • Savant : Python Computer Vision & Video Analytics Framework With Batteries Included. savant-ai.io

    • Savant : Plug-and-Play Custom Parsers for AI Models in NVIDIA DeepStream SDK. Supported YOLOv11 model.

  • OpenVINO

    • 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 : YoloV3/tiny-YoloV3 + RaspberryPi3/Ubuntu LaptopPC + NCS/NCS2 + USB Camera + Python + OpenVINO.

    • 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 : 用c++实现了yolov5使用openvino的部署。

    • dlod-openvino/yolov5_infer : Do the YOLOv5 model inference by OpenCV/OpenVINO based on onnx model format.

    • snail0614/yolov5.6_openvino_cpp : yolov5.6.1 OpenVINO的C++实现。

    • shungfu/openvino_yolov5v7 : YOLOv5 YOLOv7 INT8 quantization using OpenVINO.

    • dacquaviva/yolov5-openvino-cpp-python : Example of using ultralytics YOLOv5 with Openvino in C++ and Python.

    • rlggyp/YOLOv10-OpenVINO-CPP-Inference : YOLOv10 C++ implementation using OpenVINO for efficient and accurate real-time object detection.

  • NCNN

    • NCNN : ncnn is a high-performance neural network inference framework optimized for the mobile platform.

    • Baiyuetribe/ncnn-models : awesome AI models with NCNN, and how they were converted ✨✨✨

    • Qengineering/YoloV10-ncnn-Raspberry-Pi-4 : YoloV10 for a bare Raspberry Pi 4 or 5.

    • 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 : Real-time object detection on Android using the YOLO network with TensorFlow.

    • nihui/ncnn-android-yolov5 : The YOLOv5 object detection android example.

    • szaza/android-yolo-v2 : Android YOLO real time object detection sample application with Tensorflow mobile.

    • FeiGeChuanShu/ncnn-android-yolox : Real time yolox Android demo by ncnn.

    • xiangweizeng/darknet2ncnn : Darknet2ncnn converts the darknet model to the ncnn model.

    • sunnyden/YOLOV5_NCNN_Android : YOLOv5 C++ Implementation on Android using NCNN framework.

    • duangenquan/YoloV2NCS : This project shows how to run tiny yolo v2 with movidius stick.

    • lp6m/yolov5s_android : Run yolov5s on Android device!

    • KoheiKanagu/ncnn_yolox_flutter : This is a plugin to run YOLOX on ncnn.

    • cyrillkuettel/ncnn-android-yolov5 : This is a sample ncnn android project, it depends on ncnn library and opencv.

    • 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 : YoloV3 Raspberry Pi 4.

    • Qengineering/YoloV4-ncnn-Raspberry-Pi-4 : YoloV4 on a bare Raspberry Pi 4 with ncnn framework.

    • Qengineering/YoloV5-ncnn-Raspberry-Pi-4 : YoloV5 for a bare Raspberry Pi 4.

    • Qengineering/YoloV6-ncnn-Raspberry-Pi-4 : YoloV6 for a bare Raspberry Pi using ncnn.

    • Qengineering/YoloV7-ncnn-Raspberry-Pi-4 : YoloV7 for a bare Raspberry Pi using ncnn.

    • Qengineering/YoloV8-ncnn-Raspberry-Pi-4 : YoloV8 for a bare Raspberry Pi 4.

    • FeiGeChuanShu/ncnn-android-yolov8 : Real time yolov8 Android demo by ncnn.

    • FLamefiREz/yolov10-android-ncnn : yolov10-android-ncnn.

  • MNN

    • MNN : MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba. (MLSys 2020)

    • apxlwl/MNN-yolov3 : MNN demo of Strongeryolo, including channel pruning, android support...

  • Other Engine

    • TVM : Open deep learning compiler stack for cpu, gpu and specialized accelerators.

    • 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)

    • Tengine : Tengine is a lite, high performance, modular inference engine for embedded device.

    • 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 : 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 : 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 : Tiny YOLO for iOS implemented using CoreML but also using the new MPS graph API.

    • 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 : 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 : Lynxi-model-zoo.

• NPU and FPGA Hardware Deployment

  NPU 和 FPGA 硬件部署

  • RK3588

    • Qengineering/YoloV8-NPU : YoloV8 for RK3566/68/88 NPU (Rock 5, Orange Pi 5, Radxa Zero 3).

    • aemior/yolov8n_rk3588 : This repository contains a demonstration of Yolv8n running on an RK3588 device.

    • cqu20160901/yolov8n_rknn_Cplusplus_dfl : yolov8 瑞芯微 rknn 板端 C++部署，使用平台 rk3588，全网最简单、运行最快的部署方式。

    • cqu20160901/yolov8seg_rknn_Cplusplus : yolov8seg 瑞芯微 rknn 板端 C++部署，使用平台 rk3588。

    • Ley-WL/ultralytics-rknn : 基于ultralytics-yolov8, 将其检测/分类/分割/姿态等任务移植到rk3588上。

    • 455670288/rknn-yolov8s-multi-thread-inference : yolov8s在rk3588的推理部署，并使用多线程池并行npu推理加速。

    • 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 : 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 : 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 : 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 : 启动多线程, relu激活, 3588的yolo部署, 帧率150以上.

    • crab2rab/RKNN-YOLOV5-BatchInference-MultiThreading : RKNN-YOLOV5-BatchInference-MultiThreadingYOLOV5多张图片多线程C++推理。

    • Qengineering/YoloV10-NPU : YoloV10 NPU for the RK3566/68/88.

    • cqu20160901/yolov10_rknn_Cplusplus : yolov10 瑞芯微 rknn 板端 C++部署，使用平台 rk3588。

    • Zhou-sx/yolov5_Deepsort_rknn : Track vehicles and persons on rk3588 / rk3399pro.

    • Applied-Deep-Learning-Lab/Yolov5_RK3588 : Yolov5_RK3588.

    • 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）。

    • icetd/RkYoloRtspServer : simple yolov5 rtspserver for rk3588.

  • FPGA

    • Xilinx/Vitis-AI : 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 are available to help you get familiar with the unfied programming APIs. Vitis-AI-Library provides an easy-to-use and unified interface by encapsulating many efficient and high-quality neural networks.

    • tensil-ai/tensil : Open source machine learning accelerators. www.tensil.ai

    • 19801201/SpinalHDL_CNN_Accelerator : CNN accelerator implemented with Spinal HDL.

    • dhm2013724/yolov2_xilinx_fpga : YOLOv2 Accelerator in Xilinx's Zynq-7000 Soc(PYNQ-z2, Zedboard and ZCU102). (硕士论文 2019, 电子技术应用 2019, 计算机科学与探索 2019)

    • Yu-Zhewen/Tiny_YOLO_v3_ZYNQ : Implement Tiny YOLO v3 on ZYNQ. "A Parameterisable FPGA-Tailored Architecture for YOLOv3-Tiny". (ARC 2020)

    • 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 v9.5.0 (it needs to use the model saving method of Pytorch V1.4).

    • 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)

    • 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 : Custom YOLOv4 for apple recognition (clean/damaged) on Alveo U280 accelerator card using Vitis AI framework.

    • mkshuvo2/ZCU104_YOLOv3_Post_Processing : Tensor outputs form Vitis AI Runner Class for YOLOv3.

    • puffdrum/v4tiny_pt_quant : quantization for yolo with xilinx/vitis-ai-pytorch.

    • chanshann/LITE_YOLOV3_TINY_VITISAI : LITE_YOLOV3_TINY_VITISAI.

    • LukiBa/zybo_yolo : YOLO example implementation using Intuitus CNN accelerator on ZYBO ZYNQ-7000 FPGA board.

    • matsuda-slab/YOLO_ZYNQ_MASTER : Implementation of YOLOv3-tiny on FPGA.

    • FerberZhang/Yolov2-FPGA-CNN- : A demo for accelerating YOLOv2 in xilinx's fpga PYNQ.

    • ChainZeeLi/FPGA_DPU : This project is to implement YOLO v3 on Xilinx FPGA with DPU.

    • xbdxwyh/yolov3_fpga_project : yolov3_fpga_project.

    • ZLkanyo009/Yolo-compression-and-deployment-in-FPGA : 基于FPGA量化的人脸口罩检测。

    • xiying-boy/yolov3-AX7350 : 基于HLS_YOLOV3的驱动文件。

    • himewel/yolowell : A set of hardware architectures to build a co-design of convolutional neural networks inference at FPGA devices.

    • 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 : Converts pytorch yolo format weights to C header files for bare-metal (FPGA implementation).

    • 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 : fpga_accelerator_yolov3tiny.

    • 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 : 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 : K210 PCB YOLO.

    • SEASKY-Master/Yolo-for-k210 : Yolo-for-k210.

    • TonyZ1Min/yolo-for-k210 : keras-yolo-for-k210.

    • vseasky/yolo-for-k210 : Yolo-for-k210.

    • InnoIPA/dpu-sc : dpu-sc presented how to create quick demos to run AI inference(YOLOv4-Tiny, LPRNet) on DPU with MPSoC.

    • 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 : We have built more defect detection solutions with YOLOv4-tiny on EXMU-X261.

  • Other Hardware

    • guichristmann/edge-tpu-tiny-yolo : Run Tiny YOLO-v3 on Google's Edge TPU USB Accelerator.

    • 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 : This project deploys a yolo fastest model in the form of tflite on raspberry 3b+.

    • mahxn0/Hisi3559A_Yolov5 : 基于hisi3559a的yolov5训练部署全流程。

    • ZhenxinYUAN/YOLO_hi3516Deploy : Deploy Yolo series algorithms on Hisilicon platform hi3516, including yolov3, yolov5, yolox, etc.

    • jveitchmichaelis/edgetpu-yolo : Minimal-dependency Yolov5 export and inference demonstration for the Google Coral EdgeTPU.

    • xiaqing10/Hisi_YoLoV5 : 海思nnie跑yolov5。

    • 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 : This repository will demostrate how to deploy a offical YOLOv7 pre-trained model with OpenVINO runtime api.

    • littledeep/YOLOv5-RK3399Pro : PyTorch-->ONNX-->RKNN.

    • jnulzl/YOLOV5_RK1126 : yolov5 rk1126 cpp code.

    • 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 : an edge-real-time anchor-free object detector with decent performance.

    • liuyuan000/Rv1126_YOLOv5-Lite : YOLOv5-Lite在Rv1126部署。

• Pruning Knoweldge-Distillation Quantization

  • Pruning

    剪枝

    • Torch-Pruning : Towards Any Structural Pruning; LLMs / SAM / Diffusion / Transformers / YOLOv8 / CNNs. "Towards Any Structural Pruning". (CVPR 2023)

    • 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). "Woodfisher: Efficient second-order approximation for neural network compression". (NeurIPS 2020)

    • SparseZoo : Neural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes.

    • 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 : "SlimYOLOv3: Narrower, Faster and Better for UAV Real-Time Applications". (arXiv 2019)

    • uyzhang/yolov5_prune : Yolov5 pruning on COCO Dataset.

    • midasklr/yolov5prune : yolov5模型剪枝。

    • ZJU-lishuang/yolov5_prune : yolov5 prune，Support V2, V3, V4 and V6 versions of yolov5.

    • 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 : mobilev2-yolov5s剪枝、蒸馏，支持ncnn，tensorRT部署。ultra-light but better performence！

    • 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 : yolov3 yolov4 channel and layer pruning, Knowledge Distillation 层剪枝，通道剪枝，知识蒸馏。

    • 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 : YOLO ModelCompression MultidatasetTraining.

    • talebolano/yolov3-network-slimming : yolov3 network slimming剪枝的一种实现。

    • Bigtuo/YOLOX-Lite : 将YOLOv5-Lite代码中的head更换为YOLOX head。

    • YINYIPENG-EN/Pruning_for_YOLOV5_pytorch : Pruning_for_YOLOV5_pytorch.

    • 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 : 基于SFP和FPGM的yolov5的软剪枝实现。

  • Quantization

    量化

    • 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》"。 "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）》"。

    • dog-qiuqiu/Yolo-Fastest : Yolo-Fastest：超超超快的开源ARM实时目标检测算法。 Zenodo 2021. "知乎「马雪浩」《Yolo-Fastest：超超超快的开源ARM实时目标检测算法》"。

    • 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》"。

    • YOLObile : "YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design". (AAAI 2021)

    • 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 : 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 : 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 : 🍅🍅🍅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 : 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 : 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 : implementation of Distilling Object Detectors with Fine-grained Feature Imitation on yolov5. "Distilling Object Detectors with Fine-grained Feature Imitation". (CVPR 2019)

    • 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 : yolov5 5.0 version distillation || yolov5 5.0版本知识蒸馏，yolov5l >> yolov5s。

    • 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 : Knowledge-Distillation-yolov5 基于yolov5的知识蒸馏。

    • 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。

    • Sharpiless/Yolov3-MobileNet-Distillation : 在Yolov3-MobileNet上进行模型蒸馏训练。

    • SsisyphusTao/Object-Detection-Knowledge-Distillation : An Object Detection Knowledge Distillation framework powered by pytorch, now having SSD and yolov5.

• Lightweight Backbones and FPN

  轻量级骨干网络和特征金字塔网络

  • 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 : 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 : This repo is mainly for replacing PANet with BiFPN in YOLOv5.

  • dog-qiuqiu/MobileNet-Yolo : MobileNetV2-YoloV3-Nano: 0.5BFlops 3MB HUAWEI P40: 6ms/img, YoloFace-500k:0.1Bflops 420KB🔥🔥🔥.

  • eric612/MobileNet-YOLO : A caffe implementation of MobileNet-YOLO detection network.

  • eric612/Mobilenet-YOLO-Pytorch : Include mobilenet series (v1,v2,v3...) and yolo series (yolov3,yolov4,...) .

  • Adamdad/keras-YOLOv3-mobilenet : A Keras implementation of YOLOv3 (Tensorflow backend) inspired by allanzelener/YAD2K.

  • fsx950223/mobilenetv2-yolov3 : yolov3 with mobilenetv2 and efficientnet.

  • liux0614/yolo_nano : Unofficial implementation of yolo nano.

  • lingtengqiu/Yolo_Nano : Pytorch implementation of yolo_Nano for pedestrian detection.

  • 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 : 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 : YOLOv5的轻量化改进(蜂巢检测项目)。

  • YOLO-ReT : "YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUs". (WACV 2022)

• Open World Object Detection

  开放世界目标检测

  • YOLO-World : "YOLO-World: Real-Time Open-Vocabulary Object Detection". (CVPR 2024). www.yoloworld.cc

  • Florence-2 : "Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks". (CVPR 2024).

  • maestro : VLM fine-tuning for everyone. maestro is a streamlined tool to accelerate the fine-tuning of multimodal models. By encapsulating best practices from our core modules, maestro handles configuration, data loading, reproducibility, and training loop setup. It currently offers ready-to-use recipes for popular vision-language models such as Florence-2, PaliGemma 2, and Qwen2.5-VL. maestro.roboflow.com

  • Autodistill : Images to inference with no labeling (use foundation models to train supervised models). Autodistill uses big, slower foundation models to train small, faster supervised models. Using autodistill, you can go from unlabeled images to inference on a custom model running at the edge with no human intervention in between. docs.autodistill.com

  • DOSOD : "A Light-Weight Framework for Open-Set Object Detection with Decoupled Feature Alignment in Joint Space". (arXiv 2024).

  • DINO : "DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection". (ICLR 2023).

  • GroundingDINO : "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection". (ECCV 2024).

  • UniDetector : "Detecting Everything in the Open World: Towards Universal Object Detection". (CVPR 2023).

  • 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））.

• Few-shot Object Detection

  少样本目标检测

  • Autodistill : Images to inference with no labeling (use foundation models to train supervised models). Autodistill uses big, slower foundation models to train small, faster supervised models. Using autodistill, you can go from unlabeled images to inference on a custom model running at the edge with no human intervention in between. docs.autodistill.com

  • bingykang/Fewshot_Detection : "Few-shot Object Detection via Feature Reweighting". (ICCV 2019).

  • SSDA-YOLO : Codes for my paper "SSDA-YOLO: Semi-supervised Domain Adaptive YOLO for Cross-Domain Object Detection". (Computer Vision and Image Understanding, 2023).

  • OneTeacher : "Towards End-to-end Semi-supervised Learning for One-stage Object Detection". (arXiv 2023).

  • Efficient Teacher : "Efficient Teacher: Semi-Supervised Object Detection for YOLOv5". (arXiv 2023).

• Small Object Detection

  小目标检测

  • 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 : "Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection". (arXiv 2022, Zenodo 2021).

  • Slim-neck by GSConv : "Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles". (arXiv 2022)

  • hustvl/TinyDet : "TinyDet: accurately detecting small objects within 1 GFLOPs". (Science China Information Sciences, 2023)

  • QueryDet : "QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection". (CVPR 2022)

  • RFLA : "RFLA: Gaussian Receptive Field based Label Assignment for Tiny Object Detection". (ECCV 2022). "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》"

  • YOLT : "You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery". (arXiv 2018). "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》"

  • SIMRDWN : "Satellite Imagery Multiscale Rapid Detection with Windowed Networks". (arXiv 2018, WACV 2019)

  • YOLTv5 : YOLTv5 builds upon YOLT and SIMRDWN, and updates these frameworks to use the ultralytics/yolov5 version of the YOLO object detection family.

  • TPH-YOLOv5 : "TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-Captured Scenarios". (ICCV 2021)

  • mwaseema/Drone-Detection : "Dogfight: Detecting Drones from Drones Videos". (CVPR 2021)

  • CEASA : "Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images". (arXiv 2023). "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》"

  • 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 : YOLOV5 小目标检测修改版。

  • 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 : Trained yolo v3 model weights and configuration file to detect small boats in satellite imagery.

  • Resham-Sundar/sahi-yolox : YoloX with SAHI Implementation.

  • YOLO-Z : "YOLO-Z: Improving small object detection in YOLOv5 for autonomous vehicles". (arXiv 2021). "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）》".

  • M2S : "A novel Multi to Single Module for small object detection". (arXiv 2023). "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》".

  • ultralytics/xview-yolov3 : xView 2018 Object Detection Challenge: YOLOv3 Training and Inference.

  • inderpreet1390/yolov5-small-target : Repository for improved yolov5 for small target detection.

  • AllenSquirrel/YOLOv3_ReSAM : YOLOv3_ReSAM:A Small Target Detection Method With Spatial Attention Module.

  • kadirnar/yolov5-sahi : Yolov5 Modelini Kullanarak Özel Nesne Eğitimi ve SAHI Kullanımı.

  • kadirnar/Yolov6-SAHI : Yolov6-SAHI.

  • 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 : 基于yolov8实现小目标检测，在NWPU VHR-10和DOTA上测试。

  • 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)

  • qunshansj/Small-Object-Detection-Head-Improved-YOLOv5-Infrared-Sensing-System : 基于小目标检测头的改进YOLOv5红外遥感图像小目标检测系统。

• Multimodal Image Detection

  多模态图像检测

  • 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,).

  • remaro-network/KD-YOLOX-ViT : This repository holds the code for the Python implementation of YOLOX-ViT. Furthermore, it has the implementation of the Knowledge Distillation (KD) method, evaluation metrics of the object detector and the side-scan sonar image dataset for underwater wall detection. "Knowledge Distillation in YOLOX-ViT for Side-Scan Sonar Object Detection". (arXiv 2024). The Sonar Wall Detection Dataset (SWDD) is publicly accessible at https://zenodo.org/records/10528135.

  • wandahangFY/YOLO-MIF : YOLO-MIF(YOLOv8-RGBT) is an improved version of YOLOv8 for object detection in gray-scale images, incorporating multi-information fusion to enhance detection accuracy. The detection of RGBT mode is also added. YOLO-MIF是在灰度图像中进行目标检测的改进型YOLOv8模型，引入了多信息融合策略，提高了检测准确性。 并添加了RGBT模式的检测,分割以及关节点任务。 "YOLO-MIF: Improved YOLOv8 with Multi-Information fusion for object detection in Gray-Scale images". (Advanced Engineering Informatics, 2024).

  • wandahangFY/YOLOv11-RGBT : YOLOv11-RGBT: Towards a Comprehensive Multispectral Object Detection Framework（Supports RGBT detection for all YOLO series from YOLOv3 to YOLOv12, as well as RTDETR.））

  • mcw1217/Triple_YOLOv8 : This project uses three types of images as inputs RGB, Depth, and thermal images to perform object detection with YOLOv8.

  • ljcuestc/YoloMultispectralFusion-Coarse-to-fine-Registration : "A Novel Multispectral Fusion Defect Detection Framework With Coarse-to-Fine Multispectral Registration". (IEEE Transactions on Instrumentation and Measurement, 2023)

  • SuperYOLO : "SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery". (arXiv 2022)

  • QuincyQAQ/YOLOv8-Multi-Modal-Fusion-Network-RGB-IR : YOLOv8-Multi-Modal-Fusion-Network-RGB-IR.

  • mangoggul/YOLO-MultiModal : Multi-Modal-YOLO detection.

  • DocF/multispectral-object-detection : Multispectral Object Detection with Yolov5 and Transformer.

  • 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🌊💧💦。

  • lzhihan/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。

  • 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.

  • huashu996/dual_result_fusion_yolov5 : result set fusion for visible-light and infrared images. 此双模态检测是通过对可见光和红外图像分别训练，得到两个weight，在运行时会对两种图像分别检测，最后对检测结果求极大似然，并且能够对目标进行测距。

  • MAli-Farooq/Thermal-YOLO : This study is related to object detection in thermal infrared spectrum using YOLO-V5 framework for ADAS application.

  • OrangeSodahub/CRLFnet : Camera-Radar-Lidar Fusion detection net based on ROS, YOLOv3, OpenPCDet integration.

  • mjoshi07/Visual-Sensor-Fusion : LiDAR Fusion with Vision.

  • Arrowes/CEAM-YOLOv7 : CEAM-YOLOv7: Improved YOLOv7 Based on Channel Expansion and Attention Mechanism for Driver Distraction Behavior Detection.

• Video Object Detection

  视频目标检测

  • YOLOV : "YOLOV: Making Still Image Object Detectors Great at Video Object Detection". (arXiv 2022)

  • StreamYOLO : "Real-time Object Detection for Streaming Perception". (CVPR 2022)

  • REPP : "Robust and efficient post-processing for video object detection". (IROS 2020)

  • NoScope : "Noscope: optimizing neural network queries over video at scale". (arXiv 2017)

• Object Tracking

  目标跟踪

  • Multi-Object Tracking

    多目标跟踪

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

    • mikel-brostrom/Yolov7_StrongSORT_OSNet : Real-time multi-camera multi-object tracker using YOLOv7 and StrongSORT with OSNet.

    • RizwanMunawar/yolov8-object-tracking : YOLOv8 Object Tracking Using PyTorch, OpenCV and Ultralytics.

    • 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 : 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 : "BoT-SORT: Robust Associations Multi-Pedestrian Tracking". (arXiv 2022)

    • StrongSORT : "StrongSORT: Make DeepSORT Great Again". (arXiv 2022)

    • UAVMOT : "Multi-Object Tracking Meets Moving UAV". (CVPR 2022)

    • HKPolyU-UAV/AUTO : "Dynamic Object Tracking on Autonomous UAV System for Surveillance Applications". (Sensors 2021)

    • bharath5673/StrongSORT-YOLO : Real-time multi-camera multi-object tracker using (YOLOv5, YOLOv7) and StrongSORT with OSNet.

    • mikel-brostrom/Yolov7_StrongSORT_OSNet : Real-time multi-camera multi-object tracker using YOLOv7 and StrongSORT with OSNet.

    • kadirnar/yolov5-strongsort : Minimal PyTorch implementation of YOLOv5 and StrongSORT.

    • ZQPei/deep_sort_pytorch : MOT using deepsort and yolov3 with pytorch.

    • Qidian213/deep_sort_yolov3 : Real-time Multi-person tracker using YOLO v3 and deep_sort with tensorflow.

    • CSTrack : "Rethinking the competition between detection and ReID in Multi-Object Tracking". (arXiv 2020)

    • ROLO : ROLO is short for Recurrent YOLO, aimed at simultaneous object detection and tracking.

    • FastMOT : "FastMOT: High-Performance Multiple Object Tracking Based on Deep SORT and KLT". (Zenodo 2020)

    • 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 : 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 : Multi-camera live traffic and object counting with YOLO v4, Deep SORT, and Flask.

    • LeonLok/Deep-SORT-YOLOv4 : People detection and optional tracking with Tensorflow backend.

    • obendidi/Tracking-with-darkflow : Real-time people Multitracker using YOLO v2 and deep_sort with tensorflow.

    • DrewNF/Tensorflow_Object_Tracking_Video : Object Tracking in Tensorflow ( Localization Detection Classification ) developed to partecipate to ImageNET VID competition.

    • dyh/unbox_yolov5_deepsort_counting : yolov5 deepsort 行人 车辆 跟踪 检测 计数。

    • theAIGuysCode/yolov3_deepsort : Object tracking implemented with YOLOv3, Deep Sort and Tensorflow.

    • weixu000/libtorch-yolov3-deepsort : libtorch-yolov3-deepsort.

    • pmj110119/YOLOX_deepsort_tracker : using yolox+deepsort for object-tracking.

    • abhyantrika/nanonets_object_tracking : nanonets_object_tracking.

    • 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 : A Software Framework for End-to-End Trajectory Prediction.

    • RichardoMrMu/yolov5-deepsort-tensorrt : A c++ implementation of yolov5 and deepsort.

    • 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 : Open-source Monocular Python HawkEye for Tennis.

    • CaptainEven/YOLOV4_MCMOT : Using YOLOV4 as detector for MCMOT.

    • opendatacam/node-moving-things-tracker : javascript implementation of "tracker by detections" for realtime multiple object tracking (MOT).

    • lanmengyiyu/yolov5-deepmar : 行人轨迹和属性分析。

    • zengwb-lx/Yolov5-Deepsort-Fastreid : YoloV5 + deepsort + Fast-ReID 完整行人重识别系统。

    • tensorturtle/classy-sort-yolov5 : Ready-to-use realtime multi-object tracker that works for any object category. YOLOv5 + SORT implementation.

    • supperted825/FairMOT-X : FairMOT for Multi-Class MOT using YOLOX as Detector.

    • deyiwang89/pytorch-yolov7-deepsort : an implentation of yolov7-deepsort based on pytorch.

    • xuarehere/yolovx_deepsort_pytorch : this project support the existing yolo detection model algorithm (YOLOv3, YOLOV4, YOLOV4Scaled, YOLOV5, YOLOV6, YOLOV7 ).

    • deshwalmahesh/yolov7-deepsort-tracking : Modular and ready to deploy code to detect and track videos using YOLO-v7 and DeepSORT.

    • RizwanMunawar/yolov7-object-tracking : YOLOv7 Object Tracking Using PyTorch, OpenCV and Sort Tracking.

    • RizwanMunawar/yolov5-object-tracking : YOLOv5 Object Tracking + Detection + Object Blurring + Streamlit Dashboard Using OpenCV, PyTorch and Streamlit.

    • Smorodov/Multitarget-tracker : Multiple Object Tracker, Based on Hungarian algorithm + Kalman filter.

    • Naughty-Galileo/YoloV5_MCMOT : 多类别多目标跟踪YoloV5+sort/deepsort/bytetrack/BotSort/motdt.

    • MuhammadMoinFaisal/YOLOv8-DeepSORT-Object-Tracking : YOLOv8 Object Tracking using PyTorch, OpenCV and DeepSORT.

    • 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 : "Dynamic Object Tracking on Autonomous UAV System for Surveillance Applications". (Sensors 2021)

• Deep Reinforcement Learning

  深度强化学习

  • uzkent/EfficientObjectDetection : "Efficient Object Detection in Large Images with Deep Reinforcement Learning". (WACV 2020)

• Motion Control Field

  运动控制领域

  • 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 : ZJU2021_MotionControl_PID_YOLOv5.

  • SananSuleymanov/PID_YOLOv5s_ROS_Diver_Tracking : PID_YOLOv5s_ROS_Diver_Tracking.

  • sumght-z/apex_yolov5 : something by yolov5 and PID.

• Super-Resolution Field

  超分辨率领域

  • Fireboltz/Psychic-CCTV : A video analysis tool built completely in python.

• Spiking Neural Network

  SNN, 脉冲神经网络

  • 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)

  • EMS-YOLO : Offical implementation of "Deep Directly-Trained Spiking Neural Networks for Object Detection" (ICCV 2023)

  • Attention-SNN : Offical implementation of "Attention Spiking Neural Networks" (IEEE TPAMI 2023)

  • Spike-Driven-Transformer : Offical implementation of "Spike-driven Transformer" (NeurIPS 2023)

  • 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)

  • Spiking-YOLOv3 : A PyTorch implementation of Spiking-YOLOv3. Two branches are provided, based on two common PyTorch implementation of YOLOv3(ultralytics/yolov3 & eriklindernoren/PyTorch-YOLOv3), with support for Spiking-YOLOv3-Tiny at present. (AAAI 2020)

  • fjcu-ee-islab/Spiking_Converted_YOLOv4 : Object Detection Based on Dynamic Vision Sensor with Spiking Neural Network.

  • 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 : A modified repository based on Spiking-YOLOv3 and YOLOv3, which makes it suitable for VOC-dataset and YOLOv2.

  • beauty-girl-cxy/spiking-yolov5 : spiking-yolov5.

• Attention and Transformer

  注意力机制

  • 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 : 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)

  • 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 : "Top-Down Visual Attention from Analysis by Synthesis". (CVPR 2023). "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》"。 "微信公众号「极市平台」《CVPR23 Highlight｜拥有top-down attention能力的vision transformer》"。

  • 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 : 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, Andrew-Qibin/CoordAttention)

  • liangzhendong123/Attention-yolov5 : 基于注意力机制改进的yolov5模型。

  • e96031413/AA-YOLO : Attention ALL-CNN Twin Head YOLO (AA -YOLO). "Improving Tiny YOLO with Fewer Model Parameters". (IEEE BigMM 2021)

  • anonymoussss/YOLOX-SwinTransformer : YOLOX with Swin-Transformer backbone.

  • GuanRunwei/MAN-and-CAT : "MAN and CAT: mix attention to nn and concatenate attention to YOLO". ( The Journal of Supercomputing, 2022)

• Oriented Object Detection

  旋转目标检测

  • AlphaRotate : "AlphaRotate: A Rotation Detection Benchmark using TensorFlow". (arXiv 2021)

  • 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 : rotation detection based on yolov5.

  • acai66/yolov5_rotation : rotated bbox detection. inspired by hukaixuan19970627/yolov5_obb, thanks hukaixuan19970627.

  • ming71/rotate-yolov3 : Arbitrary oriented object detection implemented with yolov3 (attached with some tricks).

  • ming71/yolov3-polygon : Arbitrary-oriented object detection based on yolov3.

  • 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 : This repository is based on Ultralytics/yolov5, with adjustments to enable polygon prediction boxes.

  • hukaixuan19970627/DOTA_devkit_YOLO : Trans DOTA OBB format(poly format) to YOLO format.

  • 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 : 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 : 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 : 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！！！》"。

• Face Detection and Recognition

  人脸检测与识别

  • Face Detection

    人脸检测

    • YOLO5Face : "YOLO5Face: Why Reinventing a Face Detector". (arXiv 2021)

    • derronqi/yolov7-face : yolov7 face detection with landmark.

    • derronqi/yolov8-face : yolov8 face detection with landmark.

    • we0091234/yolov7-face-tensorrt : yolov7-face TensorRT.

    • YOLO-FaceV2 : "YOLO-FaceV2: A Scale and Occlusion Aware Face Detector ". (arXiv 2022). "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！》"

    • 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 : 基于yolov5的人脸检测，带关键点检测。

    • sthanhng/yoloface : Deep learning-based Face detection using the YOLOv3 algorithm.

    • 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 : Face Detection and Gender and Age Classification using Keras.

    • dannyblueliu/YOLO-Face-detection : Face detection based on YOLO darknet.

    • wmylxmj/YOLO-V3-IOU : YOLO3 动漫人脸检测 (Based on keras and tensorflow) 2019-1-19.

    • pranoyr/head-detection-using-yolo : Detection of head using YOLO.

    • grapeot/AnimeHeadDetector : An object detector for character heads in animes, based on Yolo V3.

    • 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 : 基于pytorch的Yolov5人脸检测。

    • jinfagang/yolov7-face : Next Gen Face detection based on YOLOv7.

    • Yusepp/YOLOv8-Face : YOLOv8 for Face Detection.

  • Face Recognition

    人脸识别

    • 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 : 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 : 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 : Deepstream app use YOLO, retinaface and arcface for face recognition.

    • 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 : 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 : 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 : 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐅𝐚𝐜𝐞 𝐦𝐚𝐬𝐤 𝐝𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐮𝐬𝐢𝐧𝐠 𝐝𝐞𝐞𝐩𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐀𝐥𝐞𝐫𝐭 𝐬𝐲𝐬𝐭𝐞𝐦 💻🔔.

  • VictorLin000/YOLOv3_mask_detect : Face mask detection using YOLOv3 on GoogleColab.

  • amh28/IBM-Data-Science-Capstone-Alejandra-Marquez : Homemade face mask detector fine-tuning a Yolo-v3 network.

  • LorenRd/JetsonYolov4 : Face Mask Yolov4 detector - Nvidia Jetson Nano.

  • 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 : Detecting person with or without face mask. Trained using YOLOv5.

  • NisargPethani/FACE-MASK-DETECTION-USING-YOLO-V3 : FACE-MASK DETECTION.

  • waittim/mask-detector : Real-time video streaming mask detection based on Python. Designed to defeat COVID-19.

  • BogdanMarghescu/Face-Mask-Detection-Using-YOLOv4 : Face Mask Detector using YOLOv4.

  • 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 : 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 : Social Distancing Analyser to prevent COVID19.

  • abd-shoumik/Social-distance-detection : Social distance detection, a deep learning computer vision project with yolo object detection.

  • ChargedMonk/Social-Distancing-using-YOLOv5 : Classifying people as high risk and low risk based on their distance to other people.

  • JohnBetaCode/Social-Distancing-Analyser : Social Distancing Analyzer.

  • 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 : 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 : Train a yolo v5 object detection model on Bdd100k dataset.

    • Gaussian_YOLOv3 : "Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving". (ICCV 2019)

    • 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 : Created vehicle detection pipeline with two approaches: (1) deep neural networks (YOLO framework) and (2) support vector machines ( OpenCV + HOG).

    • xslittlegrass/CarND-Vehicle-Detection : Vehicle detection using YOLO in Keras runs at 21FPS.

    • 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 : Detect vehicles in a video.

    • 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 : Vehicle counting using Pytorch.

    • MaryamBoneh/Vehicle-Detection : Vehicle Detection Using Deep Learning and YOLO Algorithm.

    • JeffWang0325/Image-Identification-for-Self-Driving-Cars : This project achieves some functions of image identification for Self-Driving Cars.

    • AnarbekovAlt/Traffic-analysis : A traffic analysis system is built on the basis of the YOLO network.

    • ruhyadi/yolov5-nodeflux : YOLOv5 Nodeflux Vehicle Detection.

    • Daheer/Driving-Environment-Detector : Detecting road objects using YOLO CNN Architecture.

    • georgia-tech-db/eva : Exploratory Video Analytics System.

    • 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 : A demo to detect vehicles in traffic cam. zehengl.github.io/yyc-traffic-cam/

    • ruhyadi/vehicle-detection-yolov8 : Vehicle Detection with YOLOv8.

  • License Plate Detection and Recognition

    车牌检测与识别

    • zeusees/License-Plate-Detector : License Plate Detection with Yolov5，基于Yolov5车牌检测。

    • TheophileBuy/LicensePlateRecognition : License Plate Recognition.

    • alitourani/yolo-license-plate-detection : A License-Plate detecttion application based on YOLO.

    • 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 : 基于yolov5的车牌检测，包含车牌角点检测。

    • kyrielw24/License_Plate_Recognition : 基于Yolo&CNN的车牌识别可视化项目。

    • 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 : Automatic Number Plate Detection YOLOv8.

  • Lane Detection

    车道线检测

    • YOLOP : "YOLOP: You Only Look Once for Panoptic Driving Perception". (arXiv 2021).

    • YOLOPv2 : "YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception". (arXiv 2022). "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》"

    • FeiGeChuanShu/YOLOPv2-ncnn : YOLOPv2-ncnn.

    • visualbuffer/copilot : Lane and obstacle detection for active assistance during driving.

    • 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 : YOLOP running in Android by ncnn.

  • Driving Behavior Detection

    驾驶行为检测

    • 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 : "CEAM-YOLOv7:Improved YOLOv7 Based on Channel Expansion and Attention Mechanism for Driver Distraction Behavior Detection". (IEEE Access, 2022).

  • Parking Slot Detection

    停车位检测

    • visualbuffer/parkingslot : Automated parking occupancy detection.

    • 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 : Detecting Traffic Lights in Real-time with YOLOv3.

    • 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 : Traffic Light Detection Yolo.

    • 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 : 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 : Tsinghua-Tencent 100K dataset XML and TXT Label.

    • amazingcodeLYL/Traffic_signs_detection_darket : darknet交通标志检测&TT100K数据集。

    • TalkUHulk/yolov3-TT100k : 使用yolov3训练的TT100k(交通标志)模型。

    • TalkUHulk/yolov4-TT100k : 使用yolov4训练的TT100k(交通标志)模型。

    • sarah-antillia/YOLO_Realistic_USA_RoadSigns_160classes : USA RoadSigns Dataset 160classes annotated by YOLO format.

    • 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 : A lightweight neural-network for rapid detection of traffic cones.

  • Crosswalk Detection

    人行横道/斑马线检测

    • CDNet : "CDNet: a real-time and robust crosswalk detection network on Jetson nano based on YOLOv5". (Neural Computing and Applications 2022). "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》"。

    • 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 : "Road Traffic Accidents Detection Based On Crash Estimation". (IEEE ICENCO 2021)

  • Road Damage Detection

    道路损伤检测

    • 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 : Поиск выбоин на дорогах с использованием YOLOv8 Nano.

    • mounishvatti/pothole_detection_yolov8 : Pothole Detection using Ultralytics YOLOv8

• Animal Detection

  动物检测

  • SaiSwarup27/Animal-Intrusion-Detection : Animal Detection using YOLOv5.

  • xcapt0/animal_recognition : 🦁 Let the robot recognize the animal instead of you | YOLOv5.

  • PhamDangNguyen/YOLOv5_Animals : YOLOv5 for detection Animals.

  • Sabuj-CSE11/AnimalDetection : Cat and Dogs detection using YoloV5.

• Helmet Detection

  头盔/安全帽检测

  • 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 : 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 : 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 : 👷 👷👷 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 : Helmet Detection using tiny-yolo-v3 by training using your own dataset and testing the results in the google colaboratory.

  • BlcaKHat/yolov3-Helmet-Detection : Training a YOLOv3 model to detect the presence of helmet for intrusion or traffic monitoring.

  • yumulinfeng1/YOLOv4-Hat-detection : 基于YOLOv4的安全帽佩戴检测。

  • 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 : 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 : 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)

• Hand Detection

  手部检测

  • 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 : "Unified learning approach for egocentric hand gesture recognition and fingertip detection". (Elsevier 2022)

  • insigh1/Interactive_ABCs_with_American_Sign_Language_using_Yolov5 : Interactive ABC's with American Sign Language.

  • 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 : A realtime action detection frame work based on PytorchVideo.

• Emotion Recognition

  情感识别

  • Tandon-A/emotic : "Context based emotion recognition using emotic dataset". (arXiv 2020)

• Human Pose Estimation

  人体姿态估计

  • 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)

  • TexasInstruments/edgeai-yolov5 : "YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss". (arXiv 2022)

  • TexasInstruments/edgeai-yolox : "YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss". (arXiv 2022)

  • jinfagang/VIBE_yolov5 : Using YOLOv5 as detection on VIBE. "VIBE: Video Inference for Human Body Pose and Shape Estimation". (CVPR 2020)

  • zhuoxiangpang/ism_person_openpose : yolov5人体检测+openpose姿态检测 实现摔倒检测。

  • pengyang1225/yolov5_person_pose : 基于yolov5的person—pose。

  • 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 : YOLOv7 Pose estimation using OpenCV, PyTorch.

  • nanmi/yolov7-pose : pose detection base on yolov7.

• Distance Measurement

  距离测量

  • 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 : 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 : 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 : yolov5+camera_distance+gazebo.

  • magisystem0408/yolov5-DeepSort-RealSenseD435i : yolov5+Realsence+DeepSense D435i.

• Instance and Semantic Segmentation

  实例和语义分割

  • SAM : 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).

  • Grounded-SAM : 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 and Segment Anything which aims to detect and segment Anything with text inputs!

  • Laughing-q/yolov5-q : This repo is plan for instance segmentation based on yolov5-6.0 and yolact.

  • TomMao23/multiyolov5 : Multi YOLO V5——Detection and Semantic Segmentation.

  • ArtyZe/yolo_segmentation : image (semantic segmentation) instance segmentation by darknet or yolo.

  • midasklr/yolov5ds : multi-task yolov5 with detection and segmentation.

  • RizwanMunawar/yolov7-segmentation : YOLOv7 Instance Segmentation using OpenCV and PyTorch.

  • leandro-svg/Yolov7_Segmentation_Tensorrt : The real-time Instance Segmentation Algorithm Yolov7 running on TensoRT and ONNX.

  • 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 : "BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework". (NeurIPS 2022).

  • mit-han-lab/bevfusion : "BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation". (ICRA 2023).

  • SAM3D : "SAM3D: Zero-Shot 3D Object Detection via Segment Anything Model". (arXiv 2023).

  • 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)

  • 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)

  • AI-liu/Complex-YOLO : This is an unofficial implementation of "Complex-YOLO: Real-time 3D Object Detection on Point Clouds in pytorch". (arXiv 2018)

  • ghimiredhikura/Complex-YOLOv3 : Complete but Unofficial PyTorch Implementation of "Complex-YOLO: Real-time 3D Object Detection on Point Clouds with YoloV3". (arXiv 2018)

  • ruhyadi/YOLO3D : YOLO 3D Object Detection for Autonomous Driving Vehicle. Reference by skhadem/3D-BoundingBox, "3D Bounding Box Estimation Using Deep Learning and Geometry". (CVPR 2017)

  • ruhyadi/yolo3d-lightning : YOLO for 3D Object Detection.

  • Yuanchu/YOLO3D : Implementation of a basic YOLO model for object detection in 3D.

  • EmiyaNing/3D-YOLO : YOLO v5 for Lidar-based 3D BEV Detection.

• SLAM Field Detection

  SLAM领域检测

  • bijustin/YOLO-DynaSLAM : YOLO Dynamic ORB_SLAM is a visual SLAM system that is robust in dynamic scenarios for RGB-D configuration.

  • BzdTaisa/YoloPlanarSLAM : YOLO-Planar-SLAM.

  • 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 : Exercise: Use YOLO to detect hot-rolled steel strip surface defects (NEU-DET dataset).

  • VanillaHours/pcbDefectDetectionYOLO : PCB defect detection using YOLOv3, on DeepPCB dataset.

  • 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 : Yolo-MSAPF: Multi-Scale Alignment fusion with Parallel feature Filtering model for high accuracy weld defect detection.

  • 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 : Best Accruacy:speed ratio SAR Ship detection in the world.

• Safety Monitoring Field Detection

  安防监控领域检测

  • 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 : Yolov5 real time smoke detection system.

  • CVUsers/Fire-Detect-by-YoloV5 : 火灾检测，浓烟检测，吸烟检测。

  • spacewalk01/Yolov5-Fire-Detection : Train yolov5 to detect fire in an image or video.

  • roflcoopter/viseron : Viseron - Self-hosted NVR with object detection.

  • dcmartin/motion-ai : AI assisted motion detection for Home Assistant.

  • Nico31415/Drowning-Detector : Using YOLO object detection, this program will detect if a person is drowning.

  • 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 : 2022-1 Individual Research Assignment: Using YOLOv5 to simply recognize each type of fire.

  • bishal116/FireDetection : This project builds fire detecton using YOLO v3 model.

  • Psynosaur/Jetson-SecVision : Person detection for Hikvision DVR with AlarmIO ports, uses TensorRT and yolov4.

  • robmarkcole/fire-detection-from-images : Detect fire in images using neural nets.

  • gaiasd/DFireDataset : D-Fire: an image data set for fire and smoke detection.

  • MuhammadMoinFaisal/FireDetectionYOLOv8 : Fire Detection using YOLOv8.

  • AI-Expert-04/School_Zone_Eye_Level : Prevention of accidents in school zones using deep learning.

  • roboflow/supervision : We write your reusable computer vision tools. 💜 roboflow.github.io/supervision

  • AntroSafin/Fire_Detection_YoloV5 : This is the YoloV5 fire detection application.

  • 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 : 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 : Fire and smoke detection using spatial and temporal patterns.

• Anti-UAV Field Detection

  反无人机领域检测

  • Anti-UAV : 🔥🔥Official Repository for Anti-UAV🔥🔥. Anti-UAV300, Anti-UAV410, Anti-UAV600. Please refer to our Anti-UAV v1 paper and Anti-UAV v3 paper for more details (WeChat News). "Anti-UAV: A Large-Scale Benchmark for Vision-Based UAV Tracking". (IEEE Transactions on Multimedia, 2022). "Evidential Detection and Tracking Collaboration: New Problem, Benchmark and Algorithm for Robust Anti-UAV System". (arXiv 2023).

  • DUT-Anti-UAV : DUT Anti-UAV Detection and Tracking. "Vision-based Anti-UAV Detection and Tracking". (IEEE Transactions on Intelligent Transportation Systems, 2022).

• Medical Field Detection

  医学领域检测

  • 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 : In this project, a new model for automatic detection of covid-19 using raw chest X-ray images is presented.

  • fahriwps/breast-cancer-detection : Breast cancer mass detection using YOLO object detection algorithm and GUI.

  • niehusst/YOLO-Cancer-Detection : An implementation of the YOLO algorithm trained to spot tumors in DICOM images.

  • safakgunes/Blood-Cancer-Detection-YOLOV5 : Blood Cancer Detection with YOLOV5.

  • shchiang0708/YOLOv2_skinCancer : YOLOv2_skinCancer.

  • avral1810/parkinsongait : Parkinson’s Disease.

  • 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 : skin_disease_detection_yolov5.

  • Moqixis/object_detection_yolov5_deepsort : 基于yolov5+deepsort的息肉目标检测。

  • mdciri/YOLOv7-Bone-Fracture-Detection : YOLOv7 to detect bone fractures on X-ray images.

  • MIRACLE-Center/YOLO_Universal_Anatomical_Landmark_Detection : [MICCAI 2021] You Only Learn Once: Universal Anatomical Landmark Detection

  • fahriwps/breast-cancer-detection : Breast cancer mass detection using YOLO object detection algorithm and GUI.

  • 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 : [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 : 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 : MGA-YOLO: A Lightweight One-Stage Network for Apple Leaf Disease Detection.

  • tanmaypandey7/wheat-detection : Detecting wheat heads using YOLOv5.

  • WoodratTradeCo/crop-rows-detection : It is an real-time crop rows detection method using YOLOv5.

  • denghv/Vegetables_Fruit_Detection : Using YOLOv10 to detect vegetables & fruit.

• Sports Field Detection

  体育领域检测

  • tomer-erez/pingpong-referee : using the YOlO algorithm for an automated pingpong referee.

• Aerial Imagery Detection

  遥感图像检测

  • WALDO : Whereabouts Ascertainment for Low-lying Detectable Objects. The SOTA in FOSS AI for drones!

• Adverse Weather Conditions

  恶劣天气情况

  • Image-Adaptive YOLO : "Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions". (AAAI 2022). "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）》"

• Adversarial Attack and Defense

  对抗攻击与防御

  • EAVISE/adversarial-yolo : "Fooling automated surveillance cameras: adversarial patches to attack person detection". (CVPR 2019)

  • git-disl/TOG : "Adversarial Objectness Gradient Attacks on Real-time Object Detection Systems". (IEEE TPS-ISA 2020) | "Understanding Object Detection Through an Adversarial Lens". (ESORICS 2020)

  • 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)

  • 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)

  • veralauee/DPatch : "DPatch: An Adversarial Patch Attack on Object Detectors". (arXiv 2018)

  • Shudeng/GPAttack : Grid Patch Attack for Object Detection.

  • Wu-Shudeng/DPAttack : "DPAttack: Diffused Patch Attacks against Universal Object Detection". (arXiv 2020)

  • 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)

  • 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)

  • mesunhlf/UPC-tf : "Universal Physical Camouflage Attacks on Object Detectors". (CVPR 2020)

  • alex96295/YOLOv3_adversarial_defense : YOLOv3_adversarial_defense.

  • alex96295/YOLO_adversarial_attacks : YOLO_adversarial_attacks.

  • 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 : Developing adversarial attacks on YOLO algorithm for computer vision.

  • Rushi314/GPR-Object-Detection : Detecting Objects in Ground Penetrating Radars Scans.

  • realtxy/pso-adversarial-yolo_v3 : pso-adversarial-yolo_v3.

  • sowgali/ObjCAM : Visualizations for adversarial attacks in object detectors like YOLO.

  • andrewpatrickdu/adversarial-yolov3-cowc : "Physical Adversarial Attacks on an Aerial Imagery Object Detector". (WACV 2022)

  • IQTLabs/camolo : Camouflage YOLO - (CAMOLO) trains adversarial patches to confuse the YOLO family of object detectors.

  • AdvTexture : "Adversarial Texture for Fooling Person Detectors in the Physical World". (CVPR 2022). "知乎「WhoTH」《CVPR2022 Oral 物理对抗样本 如何做一件“隐形衣”》"。

  • SamSamhuns/yolov5_adversarial : Generate adversarial patches against YOLOv5 🚀

• Camouflaged Detection

  伪装目标检测

  • 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 : 🌲Aim-bot based on AI for all FPS games. boosty.to/sunone

  • Passer1072/RookieAI_yolov8 : 基于yolov8实现的AI自瞄项目 AI self-aiming project based on yolov8.

  • petercunha/Pine : 🌲 Aimbot powered by real-time object detection with neural networks, GPU accelerated with Nvidia. Optimized for use with CS:GO.

  • chaoyu1999/FPSAutomaticAiming : 基于yolov5的FPS类游戏AI自瞄AI。

  • Lu-tju/CSGO_AI : 基于YOLOv3的csgo自瞄。

  • kir486680/csgo_aim : Aim assist for CSGO with python and yolo.

  • c925777075/yolov5-dnf : yolov5-DNF.

  • davidhoung2/APEX-yolov5-aim-assist : using yolov5 to help you aim enemies.

  • 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 : 基于yolov5实现的csgo自瞄。

  • SCRN-VRC/YOLOv4-Tiny-in-UnityCG-HLSL : A modern object detector inside fragment shaders.

  • 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 : A neural network for CounterStrike:GlobalOffensive character detection and classification. Built on a custom-made dataset (csgo-data-collector).

  • 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 : Target Detection for CSGO by YOLOv5.

  • Lucid1ty/Yolov5ForCSGO : CSGO character detection and auto aim.

  • leo4048111/Yolov5-LabelMaker-For-CSGO : A simple tool for making CSGO dataset in YOLO format.

  • 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 : Yolov5 Object Detection In OSRS using Python code, Detecting Cows - Botting.

  • 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 : Python Yolo v5 6.2 Csgo.

  • 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 : 基于yolov5-trt的穿越火线ai自瞄。

  • DuGuYifei/Yolov5_FPS_AICheatPrinciple : The AI cheating principle of fps game. (This is only used for learning).

  • MistyAI/MistyFN : Aimbot and Triggerbot for Fortnite based on artificial intelligence.

  • suixin1424/crossfire-yolo-TensorRT : crossfire-yolo-TensorRT. 基于yolo-trt的穿越火线ai自瞄。

  • EthanH3514/AL_Yolo : 基于Yolov5的Apex Legend游戏 AI 辅瞄外挂。

  • SunOner/yolov8_aimbot : Aim-bot based on AI for all FPS games.

  • bigQY/calabiyau-cheat : 基于yolov10的卡拉彼丘自瞄。

• Automatic Annotation Tools

  自动标注工具

  • Label Studio : Label Studio is a multi-type data labeling and annotation tool with standardized output format. labelstud.io

  • AnyLabeling : 🌟 AnyLabeling 🌟. Effortless AI-assisted data labeling with AI support from YOLO, Segment Anything, MobileSAM!! anylabeling.nrl.ai

  • 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 : OpenMMLab PlayGround: Semi-Automated Annotation with Label-Studio and SAM.

  • LabelImg : 🖍️ LabelImg is a graphical image annotation tool and label object bounding boxes in images.

  • labelme : Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).

  • DarkLabel : Video/Image Labeling and Annotation Tool.

  • AlexeyAB/Yolo_mark : GUI for marking bounded boxes of objects in images for training neural network Yolo v3 and v2.

  • Cartucho/OpenLabeling : Label images and video for Computer Vision applications.

  • 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 : Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos.

  • 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 : RectLabel - An image annotation tool to label images for bounding box object detection and segmentation.

  • 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 : 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 : Computer vision based ML training data generation tool 🚀

  • 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 : Darknet YOLOv2/3 annotation tool written in C#/WPF.

  • 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 : YoloV5 AI Assisted Labeling.

  • 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 : YOLOv5_6.1 自动标记目标框。

  • 2vin/PyYAT : Semi-Automatic Yolo Annotation Tool In Python.

  • AlturosDestinations/Alturos.ImageAnnotation : A collaborative tool for labeling image data for yolo.

  • stephanecharette/DarkMark : Marking up images for use with Darknet.

  • 2vin/yolo_annotation_tool : Annotation tool for YOLO in opencv.

  • sanfooh/quick_yolo2_label_tool : yolo快速标注工具 quick yolo2 label tool.

  • 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 : 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 : Uniform, Unlimited, Universal and Unbelievable Annotation Toolbox.

• Feature Map Visualization

  特征图可视化

  • pooya-mohammadi/yolov5-gradcam : Visualizing Yolov5's layers using GradCam.

  • TorchCAM : 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 : 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 : 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)

  • rafaelpadilla/Object-Detection-Metrics : Most popular metrics used to evaluate object detection algorithms. "A Survey on Performance Metrics for Object-Detection Algorithms". (IWSSIP 2020)

  • Cartucho/mAP : mean Average Precision - This code evaluates the performance of your neural net for object recognition.

  • Lightning-AI/metrics : Machine learning metrics for distributed, scalable PyTorch applications.

  • 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 : 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 : Ultralytics YOLO iOS App source code for running YOLOv8 in your own iOS apps 🌟. ultralytics.com/yolo

  • Flutter-Related

    • ultralytics/yolo-flutter-app : A Flutter plugin for Ultralytics YOLO computer vision models. ultralytics.com

    • hiennguyen92/flutter_realtime_object_detection : Flutter App real-time object detection with Tensorflow Lite.

  • Streamlit-Related

    • wjnwjn59/YOLOv10_Streamlit_Demo : A simple object detection web demo using YOLOv10 and Streamlit.

    • rampal-punia/yolov8-streamlit-detection-tracking : Object detection and tracking algorithm implemented for Real-Time video streams and static images.

    • 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》"。

    • 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 : Deploy YOLOv5 detection with Streamlit.

    • 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 : 手把手带你实战 YOLOv5。

    • 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 : 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 : 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 : Qt QML based yolov5 + RGBD camera program.

    • 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 : Detecting objects from camera or local video files vi qt and yolo.

    • Javacr/PyQt5-YOLOv5 : YOLOv5检测界面-PyQt5实现。

    • 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 : Yolov4-QtGUI是基于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 : 给yolov5加个gui界面，使用pyqt5，yolov5是5.0版本。

    • 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 : Add gui for YoloV5 using PyQt5.

    • LuckyBoy1798/yolov5-pyqt : 基于yolov5+pyqt的甲骨文图形化检测工具。

    • PySimpleGUI/PySimpleGUI-YOLO : A YOLO Artificial Intelligence algorithm demonstration using PySimpleGUI.

    • prabindh/qt5-opencv3-darknet : Qt5 + Darknet/Yolo + OpenCV3.

    • GinkgoX/YOLOv3GUI_Pytorch_PyQt5 : This is a GUI project for Deep Learning Object Detection based on YOLOv3 model.

    • FatemeZamanian/Yolov5-Fruit-Detector : A program to recognize fruits on pictures or videos using yolov5.

    • BioMeasure/PyQt5_YoLoV5_DeepSort : This is a PyQt5 GUI program, which is based on YoloV5 and DeepSort to track person.

    • 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 : 用ncnn调用yolov5和deep sort模型，opencv读取视频。

    • 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 : 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 : 把YOLOv5的视频显示到pyqt5ui上。

    • 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 : Real-time object detection app with Python and PyQt framework.

    • Powercube7/YOLOv5-GUI : A simple GUI made for creating jobs in YOLOv5.

    • cdmstrong/yolov5-pyqt-moke : 利用yolov5和pyqt做可视化检测。

    • 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 : 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 : 基于Yolov5 + PyQt5 + onnxruntime的目标检测部署。

    • LPC1616/pyqt-yolox-modbus : qt界面+yolox识别算法+modbus通信。

    • 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 : 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 : 使用Pyqt5搭建YOLO系列多线程目标检测系统。

    • 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 : Qt-GUI implementation of the YOLOv5 algorithm (ver.6).

    • BraunGe/YOLOv5-GUI : A GUI for YOLOv5, support all the 11 inference formats that YOLOv5 supports.

    • PetervanLunteren/EcoAssist : A no-code platform to train and deploy YOLOv5 object detection models.

    • SwimmingLiu/yolov7-Pyside6 : PySide6 implementation of YOLOv7 GUI.

  • PySide-Related

    • JSwimmingLiu/YOLOSHOW : YOLO SHOW - YOLOv10 / YOLOv9 / YOLOv8 / YOLOv7 / YOLOv5 / RTDETR GUI based on Pyside6.swimmingliu.cn/posts/diary/yoloshow

    • Jai-wei/YOLOv8-PySide6-GUI : YoloSide - YOLOv8 GUI By PySide6.

• Other Applications

  其它应用

  • Ikomia-dev/IkomiaApi : State-of-the-art algorithms in Computer Vision with a few lines of code.

  • 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 : table detect(yolo) , table line(unet) （表格检测/表格单元格定位）。

  • thisiszhou/SexyYolo : An implementation of Yolov3 with Tensorflow1.x, which could detect COCO and sexy or porn person simultaneously.

  • javirk/Person_remover : People removal in images using Pix2Pix and YOLO.

  • 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 : Parsing pdf tables using YOLOV3.

  • 008karan/PAN_OCR : Building OCR using YOLO and Tesseract.

  • zeyad-mansour/lunar : Lunar is a neural network aimbot that uses real-time object detection accelerated with CUDA on Nvidia GPUs.

  • lannguyen0910/food-recognition : 🍔🍟🍗 Food analysis baseline with Theseus. Integrate object detection, image classification and multi-class semantic segmentation. 🍞🍖🍕

  • killnice/yolov5-D435i : using yolov5 and realsense D435i.

  • SahilChachra/Video-Analytics-Dashboard : Video Analytics dashboard built using YoloV5 and Streamlit.

  • isLinXu/YOLOv5_Efficient : Use yolov5 efficiently(高效地使用Yolo v5).

  • 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 : 基于YOLOv5的水表读数系统。

  • 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 : This repo explain how qr codes works, qr detection and decoding.

  • LUMAIS/AntDet_YOLOv5 : Ants and their Activiteis (Trophallaxis) Detection using YOLOv5 based on PyTorch.

  • Jiseong-Ok/OCR-Yolov5-SwinIR-SVTR : OCR(Korean).

  • QIN2DIM/hcaptcha-challenger : 🥂 Gracefully face hCaptcha challenge with YOLOv6(ONNX) embedded solution.

  • 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 : YOLOv7 Object Cropping Using OpenCV.

  • RizwanMunawar/yolov7-object-blurring : YOLOv7 Object Blurring Using PyTorch and OpenCV.

  • pacocp/YOLOF : 📹 YOLO meets Optical Flow.

  • FabianPlum/OmniTrax : Deep learning-based multi animal tracking and pose estimation Blender Add-on.

  • 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 : 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 : This is an AI model for detecting and recognizing invoice information by yolov5 and OCR.

  • vmc-7645/YOLOv8-retail : Detect retail products via the YOLOv8 object recognition engine.

  • TAber-W/RM_4-points_yolov5 : Robomaster 基于yoloface和MobileNet修改的四点模型.

  • eternal-echo/picking : 基于YOLO v5视觉分拣零件系统设计。

  • swordswind/yolo_ocr_api_server : YOLOv10&EasyOCR融合图像识别API服务器。

• Datasets Share Platform

  • OpenDataLab : OpenDataLab 是上海人工智能实验室的大模型数据基座团队打造的数据开放平台，现已成为中国大模型语料数据联盟开源数据服务指定平台，为开发者提供全链条的 AI 数据支持，应对和解决数据处理中的风险与挑战，推动 AI 研究及应用。

  • Science Data Bank(ScienceDB) : Make your research data citable, discoverable and persistently accessible Satisfy flexible data sharing requirements Dedicate to facilitating data dissemination and reusing. Science Data Bank (ScienceDB) is a public, general-purpose data repository aiming to provide data services (e.g. data acquisition, long-term preservation, publishing, sharing and access) for researchers, research projects/teams, journals, institutions, universities, etc. It supports a variety of data acquisition and data licenses. ScienceDB is dedicated to promoting data findable, citable and reusable on the prerequisite of protecting the rights and interests of data owners and it is built and operated by Computer Network Information Center, Chinese Academy of Sciences.

  • 中国科学数据 : 《中国科学数据（中英文网络版）》（China Scientific Data）（CN11-6035/N，ISSN 2096-2223）是目前中国唯一的专门面向多学科领域科学数据出版的学术期刊，作为国家网络连续型出版物的首批试点之一，由中国科学院主管，中国科学院计算机网络信息中心和ISC CODATA中国全国委员会合办，国家科技基础条件平台中心、中国科学院网络安全和信息化领导小组办公室指导，国内外公开发行，中英文，季刊。 中国科学引文数据库（CSCD）来源期刊，中国科技核心期刊 ，收录于中国科协高质量科技期刊分级目录。

  • 飞桨AI Studio : 飞桨AI Studio开放数据集。

  • 极市开发者平台 : 极市开发者平台开放数据集。

  • openvinotoolkit/datumaro : Dataset Management Framework, a Python library and a CLI tool to build, analyze and manage Computer Vision datasets.

• Datasets Tools

  • Data Annotation

    • Label Studio : Label Studio is a multi-type data labeling and annotation tool with standardized output format. labelstud.io

    • X-AnyLabeling : Effortless data labeling with AI support from Segment Anything and other awesome models.

    • AnyLabeling : Effortless AI-assisted data labeling with AI support from YOLO, Segment Anything (SAM+SAM2), MobileSAM!! AnyLabeling = LabelImg + Labelme + Improved UI + Auto-labeling. anylabeling.nrl.ai

    • SAMLabelerPro : label your image with Segment Anything Model or MobileSAM, support remote labeling for multiple persons。使用Segment Anything Model或MobileSAM辅助标注的工具，支持多人远程标注。

    • LabelImg : 🖍️ LabelImg is a graphical image annotation tool and label object bounding boxes in images.

    • labelme : Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).

    • DarkLabel : Video/Image Labeling and Annotation Tool.

    • AlexeyAB/Yolo_mark : GUI for marking bounded boxes of objects in images for training neural network Yolo v3 and v2.

    • Cartucho/OpenLabeling : Label images and video for Computer Vision applications.

    • 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 : Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos.

    • 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 : RectLabel - An image annotation tool to label images for bounding box object detection and segmentation.

    • 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 : 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 : Computer vision based ML training data generation tool 🚀

    • 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 : Darknet YOLOv2/3 annotation tool written in C#/WPF.

    • 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 : YoloV5 AI Assisted Labeling.

    • 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 : YOLOv5_6.1 自动标记目标框。

    • 2vin/PyYAT : Semi-Automatic Yolo Annotation Tool In Python.

    • AlturosDestinations/Alturos.ImageAnnotation : A collaborative tool for labeling image data for yolo.

    • stephanecharette/DarkMark : Marking up images for use with Darknet.

    • 2vin/yolo_annotation_tool : Annotation tool for YOLO in opencv.

    • sanfooh/quick_yolo2_label_tool : yolo快速标注工具 quick yolo2 label tool.

    • 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 : 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 : Uniform, Unlimited, Universal and Unbelievable Annotation Toolbox.

  • Data Augmentation

    • 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). albumentations.ai

    • doubleZ0108/Data-Augmentation : General Data Augmentation Algorithms for Object Detection(esp. Yolo).

  • Data Management

    • YOLOExplorer : YOLOExplorer : Iterate on your YOLO / CV datasets using SQL, Vector semantic search, and more within seconds. Explore, manipulate and iterate on Computer Vision datasets with precision using simple APIs. Supports SQL filters, vector similarity search, native interface with Pandas and more.

• General Detection and Recognition Datasets

  • General Object Detection Datasets

    • COCO : "Microsoft COCO: Common Objects in Context". (ECCV 2014)

    • PASCAL VOC : "The Pascal Visual Object Classes Challenge: A Retrospective". (IJCV 2015)

    • Objects365 : "Objects365: A Large-scale, High-quality Dataset for Object Detection". (ICCV 2019)

    • V3Det : "V3Det: Vast Vocabulary Visual Detection Dataset". (arXiv 2023)

  • General Object Recognition Datasets

    • ImageNet : "ImageNet Large Scale Visual Recognition Challenge". (IJCV 2015)

• Autonomous Driving Datasets

  • Diverse Autonomous Driving Datasets

    • BDD100K : "BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning". (CVPR 2020)

    • CODA : "CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving". (ECCV 2022)

  • Traffic Sign Detection Datasets

    • TT100K : "Traffic-Sign Detection and Classification in the Wild". (CVPR 2016)

    • CCTSDB : CSUST Chinese Traffic Sign Detection Benchmark 中国交通数据集由长沙理工大学综合交通运输大数据智能处理湖南省重点实验室张建明老师团队制作完成。 "A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2". (Algorithms, 2017)

    • CCTSDB2021 : "CCTSDB 2021: a more comprehensive traffic sign detection benchmark". (Human-centric Computing and Information Sciences, 2022)

  • License Plate Detection and Recognition Datasets

    • CCPD : "Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline". (ECCV 2018)

• Adverse Weather Datasets

  • RESID : "Benchmarking Single-Image Dehazing and Beyond". (IEEE Transactions on Image Processing 2018)

• Person Detection Datasets

  • INRIA Person : "Histograms of oriented gradients for human detection". (CVPR 2005)

  • CrowdHuman : "CrowdHuman: A Benchmark for Detecting Human in a Crowd". (arXiv 2018)

  • PANDA : "PANDA: A Gigapixel-Level Human-Centric Video Dataset". (CVPR 2020)

  • TinyPerson : "Scale Match for Tiny Person Detection". (WACV 2020)

  • TinyPerson v2 | SeaPerson : "Object Localization Under Single Coarse Point Supervision". (CVPR 2022)

  Anti-UAV Datasets

  • Anti-UAV : 🔥🔥Official Repository for Anti-UAV🔥🔥. Anti-UAV300, Anti-UAV410, Anti-UAV600. Please refer to our Anti-UAV v1 paper and Anti-UAV v3 paper for more details (WeChat News). "Anti-UAV: A Large-Scale Benchmark for Vision-Based UAV Tracking". (IEEE Transactions on Multimedia, 2022). "Evidential Detection and Tracking Collaboration: New Problem, Benchmark and Algorithm for Robust Anti-UAV System". (arXiv 2023).

  • DUT-Anti-UAV : DUT Anti-UAV Detection and Tracking. "Vision-based Anti-UAV Detection and Tracking". (IEEE Transactions on Intelligent Transportation Systems, 2022).

• Optical Aerial Imagery Datasets

  • COWC : "A large contextual dataset for classification, detection and counting of cars with deep learning". (ECCV 2016)

  • RSOD : "Accurate object localization in remote sensing images based on convolutional neural networks". (IEEE TGRS 2017)

  • LEVIR : "Random access memories: A new paradigm for target detection in high resolution aerial remote sensing images". (IEEE Transactions on Image Processing 2017)

  • LEVIR-Ship : "A Degraded Reconstruction Enhancement-based Method for Tiny Ship Detection in Remote Sensing Images with A New Large-scale Dataset". (IEEE TGRS 2022)

  • MASATI : "Automatic ship classification from optical aerial images with convolutional neural networks". (Remote Sensing 2018)

  • xView : "xView: Objects in Context in Overhead Imagery". (arXiv 2018)

  • DOTA : "DOTA: A Large-Scale Dataset for Object Detection in Aerial Images". (CVPR 2018). "Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges". (IEEE TPAMI 2021).

  • ITCVD : "Deep Learning for Vehicle Detection in Aerial Images". (IEEE ICIP 2018)

  • Bridge Dataset : "A Tool for Bridge Detection in Major Infrastructure Works Using Satellite Images". (IEEE ICIP 2018)

  • DIOR : "Object detection in optical remote sensing images: A survey and a new benchmark". (ISPRS 2020)

  • PESMOD : "UAV Images Dataset for Moving Object Detection from Moving Cameras". (arXiv 2021)

  • AI-TOD : "Tiny Object Detection in Aerial Images". (IEEE ICPR 2021)

  • RsCarData : "DSFNet: Dynamic and Static Fusion Network for Moving Object Detection in Satellite Videos". (IEEE GRSL 2021)

  • VISO : "Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark". (IEEE TGRS 2021)

  • VisDrone : "Detection and Tracking Meet Drones Challenge". (IEEE TPAMI 2021)

  • FAIR1M : "FAIR1M: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery". (ISPRS 2021)

  • SeaDronesSee : "SeaDronesSee: A Maritime Benchmark for Detecting Humans in Open Water". (WACV 2022)

• Low-light Image Datasets

  • NightOwls : "NightOwls: A Pedestrians at Night Dataset". (ACCV 2018).

  • ExDark : "Getting to know low-light images with the exclusively dark dataset". (CVIU 2019). "Low-light image enhancement using Gaussian Process for features retrieval". (Signal Processing: Image Communication, 2019).

  • DARK FACE : DARK FACE: Face Detection in Low Light Condition. "Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study". (IEEE Transactions on Image Processing 2020).

• Infrared Image Datasets

  • 地/空背景下红外图像弱小飞机目标检测跟踪数据集 (中国科学数据, 2020)

  • 复杂背景下红外弱小运动目标检测数据集 (中国科学数据, 2021)

  • 面向空地应用的红外时敏目标检测跟踪数据集 (中国科学数据, 2022)

  • SCUT_FIR_Pedestrian_Dataset : "Benchmarking a large-scale FIR dataset for on-road pedestrian detection". (Infrared Physics & Technology, 2019)

  • NUDT-SIRST : "Dense Nested Attention Network for Infrared Small Target Detection". (arXiv 2021)

  • SIRST : "Asymmetric Contextual Modulation for Infrared Small Target Detection". (WACV 2021)

• SAR Image Datasets

  • SNL VideoSAR : "Developments in sar and ifsar systems and technologies at sandia national laboratories". (IEEE Aerospace Conference Proceedings, 2003)

  • MSTAR : MSTAR public dataset. "Object recognition results using MSTAR synthetic aperture radar data". (IEEE CVBVS 2000)

  • OpenSARShip : "OpenSARShip: A Dataset Dedicated to Sentinel-1 Ship Interpretation". (IEEE JSTAEORS 2017)

  • OpenSARShip 2.0 : "OpenSARShip 2.0: A large-volume dataset for deeper interpretation of ship targets in Sentinel-1 imagery". (IEEE BIGSARDATA 2017)

  • SSDD : "Ship detection in SAR images based on an improved faster R-CNN". (IEEE BIGSARDATA 2017). "基于深度学习的SAR图像舰船检测数据集及性能分析". (第五届高分辨率对地观测学术年会, 2018)

  • AIR-SARShip : "高分辨率SAR舰船检测数据集-2.0". "AIR-SARShip-1.0: 高分辨率 SAR 舰船检测数据集". (雷达学报 2019)

  • SAR-Ship-Dataset : "A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds". (Remote Sensing, 2019)

  • OpenSARUrban : "OpenSARUrban: A Sentinel-1 SAR Image Dataset for Urban Interpretation". (IEEE JSTAEORS 2020)

  • HRSID : "HRSID: A High-Resolution SAR Images Dataset for Ship Detection and Instance Segmentation". (IEEE Access 2020)

  • FUSAR-Ship : 高分辨率船只数据集FUSAR-Ship1.0. (雷达学报). "FUSAR-Ship: building a high-resolution SAR-AIS matchup dataset of Gaofen-3 for ship detection and recognition". (Science China Information Sciences, 2020)

  • Official-SSDD : "SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis ". (Remote Sensing, 2021)

  • MSAR : "大规模多类SAR目标检测数据集-1.0"。(雷达学报 2022)

  • RSDD-SAR : "RSDD-SAR:SAR舰船斜框检测数据集"。(雷达学报 2022)

• Sonar Image Datasets

  • SWDD : SWDD: Sonar Wall Detection Dataset. "Knowledge Distillation in YOLOX-ViT for Side-Scan Sonar Object Detection". (arXiv 2024). The Sonar Wall Detection Dataset (SWDD) is publicly accessible at https://zenodo.org/records/10528135.

• Multimodal Image Datasets

  • FLIR_ADAS : Teledyne FLIR Free ADAS Thermal Dataset v2.

  • Anti-UAV : 🔥🔥Official Repository for Anti-UAV🔥🔥. Anti-UAV300, Anti-UAV410, Anti-UAV600. Please refer to our Anti-UAV v1 paper and Anti-UAV v3 paper for more details (WeChat News). "Anti-UAV: A Large-Scale Benchmark for Vision-Based UAV Tracking". (IEEE Transactions on Multimedia, 2022). "Evidential Detection and Tracking Collaboration: New Problem, Benchmark and Algorithm for Robust Anti-UAV System". (arXiv 2023).

  • VEDAI : "Vehicle Detection in Aerial Imagery: A small target detection benchmark". (Journal of Visual Communication and Image Representation 2015)

  • KAIST_rgbt : "Multispectral Pedestrian Detection: Benchmark Dataset and Baseline". (CVPR 2015)

  • LLVIP : "LLVIP: A Visible-Infrared Paired Dataset for Low-Light Vision". (ICCV 2021)

  • TNO : "The TNO multiband image data collection". (Data in brief, 2017)

  • MFNet : MFNet-pytorch, image semantic segmentation using RGB-Thermal images. "MFNet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes". (IROS 2017). (MFNet Dataset : Multi-spectral Object Detection and Semantic Segmentation Datasets)

  • MSRS : MSRS: Multi-Spectral Road Scenarios for Practical Infrared and Visible Image Fusion. "PIAFusion : A progressive infrared and visible image fusion network based on illumination aware". (Information Fusion, 2022)

  • TarDAL : "Target-Aware Dual Adversarial Learning and a Multi-Scenario Multi-Modality Benchmark To Fuse Infrared and Visible for Object Detection". (CVPR 2022). (M3FD Dataset)

  • DroneVehicle : "Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning". (IEEE TCSVT 2022)

• 3D Object Detection Datasets

  • Objectron : "Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations". (CVPR, 2021)

• Vehicle-to-Everything Field Datasets

  • OpenCOOD|OPV2V : OpenCOOD is an Open COOperative Detection framework for autonomous driving. It is also the official implementation of the ICRA 2022 paper OPV2V. "OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication". (ICRA, 2022). mobility-lab.seas.ucla.edu/opv2v/

  • CoBEVT : "CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers". (CoRL, 2022).

  • Where2comm : "Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps". (Neurips, 2022).

  • PJLab-ADG/LiDARSimLib-and-Placement-Evaluation : "Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Library". (ICRA, 2023).

  • CoAlign : "Robust Collaborative 3D Object Detection in Presence of Pose Errors". (ICRA, 2023).

  • V2V4Real : "V2V4Real: A Real-World Large-Scale Dataset for Vehicle-to-Vehicle Cooperative Perception". (CVPR, 2023).

  • V2X-ViT|V2XSet : "V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer". (ECCV, 2022).

  • DAIR-V2X : "DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection". (CVPR, 2022). 全球首个车路协同自动驾驶数据集发布

  • V2X-Seq : "V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting". (CVPR, 2023). 全球首个大规模时序车路协同自动驾驶数据集发布

• Super-Resolution Field Datasets

  • VideoLQ : "Investigating Tradeoffs in Real-World Video Super-Resolution". (CVPR, 2022)

• Face Detection and Recognition Datasets

  • Face Detection Datasets

    • WIDER FACE : "WIDER FACE: A Face Detection Benchmark". (CVPR 2016)

    • UFDD : Unconstrained Face Detection Dataset(UFDD). "Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results". (IEEE BTAS 2018)

  • Face Recognition Datasets

    • LFW : Labeled Faces in the Wild(LFW). "Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments". (Workshop on faces in'Real-Life'Images: detection, alignment, and recognition. 2008)

    • YouTube Faces (YTF) : "Face recognition in unconstrained videos with matched background similarity". (CVPR 2011)

    • CASIA-WebFace : "Learning Face Representation from Scratch". (arXiv 2014)

    • IJB-A : "Pushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark A". (CVPR 2015)

    • MS-Celeb-1M : "MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition". (ECCV 2016)

    • MegaFace : "The MegaFace Benchmark: 1 Million Faces for Recognition at Scale". (CVPR 2016)

    • UMDFaces : "UMDFaces: An annotated face dataset for training deep networks". (IJCB 2017)

    • IJB-B : "IARPA Janus Benchmark-B Face Dataset". (CVPR 2017)

    • IJB-C : "IARPA Janus Benchmark - C: Face Dataset and Protocol". (ICB 2018)

    • VGGFace2 : "VGGFace2: A Dataset for Recognising Faces across Pose and Age". (FG 2018)