yolo论文系列-v1-v8

# Yolo v4, v3 and v2 for Windows and Linux ## (neural networks for object detection) * Paper **YOLOv7**: https://arxiv.org/abs/2207.02696 * source code YOLOv7 - Pytorch (use to reproduce results): https://github.com/WongKinYiu/yolov7 ---- * Paper **YOLOv4**: https://arxiv.org/abs/2004.10934 * source code YOLOv4 - Darknet (use to reproduce results): https://github.com/AlexeyAB/darknet ---- * Paper **Scaled-YOLOv4 (CVPR 2021)**: https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scaled-YOLOv4_Scaling_Cross_Stage_Partial_Network_CVPR_2021_paper.html * source code Scaled-YOLOv4 - Pytorch (use to reproduce results): https://github.com/WongKinYiu/ScaledYOLOv4 ---- ### YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors * **Paper**: https://arxiv.org/abs/2207.02696 * **source code - Pytorch (use to reproduce results):** https://github.com/WongKinYiu/yolov7 YOLOv7 is more accurate and faster than YOLOv5 by **120%** FPS, than YOLOX by **180%** FPS, than Dual-Swin-T by **1200%** FPS, than ConvNext by **550%** FPS, than SWIN-L by **500%** FPS, than PPYOLOE-X by **150%** FPS. YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100, batch=1. * YOLOv7-e6 (55.9% AP, 56 FPS V100 b=1) by `+500%` FPS faster than SWIN-L C-M-RCNN (53.9% AP, 9.2 FPS A100 b=1) * YOLOv7-e6 (55.9% AP, 56 FPS V100 b=1) by `+550%` FPS faster than ConvNeXt-XL C-M-RCNN (55.2% AP, 8.6 FPS A100 b=1) * YOLOv7-w6 (54.6% AP, 84 FPS V100 b=1) by `+120%` FPS faster than YOLOv5-X6-r6.1 (55.0% AP, 38 FPS V100 b=1) * YOLOv7-w6 (54.6% AP, 84 FPS V100 b=1) by `+1200%` FPS faster than Dual-Swin-T C-M-RCNN (53.6% AP, 6.5 FPS V100 b=1) * YOLOv7x (52.9% AP, 114 FPS V100 b=1) by `+150%` FPS faster than PPYOLOE-X (51.9% AP, 45 FPS V100 b=1) * YOLOv7 (51.2% AP, 161 FPS V100 b=1) by `+180%` FPS faster than YOLOX-X (51.1% AP, 58 FPS V100 b=1) ----  ----  ---- More details in articles on medium: - [Scaled_YOLOv4](https://alexeyab84.medium.com/scaled-yolo-v4-is-the-best-neural-network-for-object-detection-on-ms-coco-dataset-39dfa22fa982?source=friends_link&sk=c8553bfed861b1a7932f739d26f487c8) - [YOLOv4](https://medium.com/@alexeyab84/yolov4-the-most-accurate-real-time-neural-network-on-ms-coco-dataset-73adfd3602fe?source=friends_link&sk=6039748846bbcf1d960c3061542591d7) Manual: https://github.com/AlexeyAB/darknet/wiki Discussion: - [Reddit](https://www.reddit.com/r/MachineLearning/comments/gydxzd/p_yolov4_the_most_accurate_realtime_neural/) - [Google-groups](https://groups.google.com/forum/#!forum/darknet) - [Discord](https://discord.gg/zSq8rtW) About Darknet framework: http://pjreddie.com/darknet/ [](https://github.com/AlexeyAB/darknet/actions?query=workflow%3A%22Darknet+Continuous+Integration%22) [](https://circleci.com/gh/AlexeyAB/darknet) [](https://github.com/AlexeyAB/darknet/graphs/contributors) [](https://github.com/AlexeyAB/darknet/blob/master/LICENSE) [](https://zenodo.org/badge/latestdoi/75388965) [](https://arxiv.org/abs/2004.10934) [](https://arxiv.org/abs/2011.08036) [](https://colab.research.google.com/drive/12QusaaRj_lUwCGDvQNfICpa7kA7_a2dE) [](https://colab.research.google.com/drive/1_GdoqCJWXsChrOiY8sZMr_zbr_fH-0Fg) - [YOLOv4 model zoo](https://github.com/AlexeyAB/darknet/wiki/YOLOv4-model-zoo) - [Requirements (and how to install dependencies)](#requirements-for-windows-linux-and-macos) - [Pre-trained models](#pre-trained-models) - [FAQ - frequently asked questions](https://github.com/AlexeyAB/darknet/wiki/FAQ---frequently-asked-questions) - [Explanations in issues](https://github.com/AlexeyAB/darknet/issues?q=is%3Aopen+is%3Aissue+label%3AExplanations) - [Yolo v4 in other frameworks (TensorRT, TensorFlow, PyTorch, OpenVINO, OpenCV-dnn, TVM,...)](#yolo-v4-in-other-frameworks) - [Datasets](#datasets) - [Yolo v4, v3 and v2 for Windows and Linux](#yolo-v4-v3-and-v2-for-windows-and-linux) - [(neural networks for object detection)](#neural-networks-for-object-detection) - [GeForce RTX 2080 Ti](#geforce-rtx-2080-ti) - [Youtube video of results](#youtube-video-of-results) - [How to evaluate AP of YOLOv4 on the MS COCO evaluation server](#how-to-evaluate-ap-of-yolov4-on-the-ms-coco-evaluation-server) - [How to evaluate FPS of YOLOv4 on GPU](#how-to-evaluate-fps-of-yolov4-on-gpu) - [Pre-trained models](#pre-trained-models) - [Requirements for Windows, Linux and macOS](#requirements-for-windows-linux-and-macos) - [Yolo v4 in other frameworks](#yolo-v4-in-other-frameworks) - [Datasets](#datasets) - [Improvements in this repository](#improvements-in-this-repository) - [How to use on the command line](#how-to-use-on-the-command-line) - [For using network video-camera mjpeg-stream with any Android smartphone](#for-using-network-video-camera-mjpeg-stream-with-any-android-smartphone) - [How to compile on Linux/macOS (using `CMake`)](#how-to-compile-on-linuxmacos-using-cmake) - [Using also PowerShell](#using-also-powershell) - [How to compile on Linux (using `make`)](#how-to-compile-on-linux-using-make) - [How to compile on Windows (using `CMake`)](#how-to-compile-on-windows-using-cmake) - [How to compile on Windows (using `vcpkg`)](#how-to-compile-on-windows-using-vcpkg) - [How to train with multi-GPU](#how-to-train-with-multi-gpu) - [How to train (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects) - [How to train tiny-yolo (to detect your custom objects)](#how-to-train-tiny-yolo-to-detect-your-custom-objects) - [When should I stop training](#when-should-i-stop-training) - [Custom object detection](#custom-object-detection) - [How to improve object detection](#how-to-improve-object-detection) - [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) - [How to use Yolo as DLL and SO libraries](#how-to-use-yolo-as-dll-and-so-libraries) - [Citation](#citation)   AP50:95 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2011.08036 ----  AP50:95 / AP50 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2004.10934 tkDNN-TensorRT accelerates YOLOv4 **~2x** times for batch=1 and **3x-4x** times for batch=4. - tkDNN: https://github.com/ceccocats/tkDNN - OpenCV: https://gist.github.com/YashasSamaga/48bdb167303e10f4d07b754888ddbdcf ### GeForce RTX 2080 Ti | Network Size | Darknet, FPS (avg) | tkDNN TensorRT FP32, FPS | tkDNN TensorRT FP16, FPS | OpenCV FP16, FPS | tkDNN TensorRT FP16 batch=4, FPS | O