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GitHub - iberganzo/darknet: Hybrid MSRM-based deep learning and multitemporal Sentinel-2-based machine learning algorithm detects near 10k archaeological tumuli in North-western Iberia
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v2 for Windows and Linux\u003c/h1\u003e\u003ca id=\"user-content-yolo-v4-v3-and-v2-for-windows-and-linux\" class=\"anchor\" aria-label=\"Permalink: Yolo v4, v3 and v2 for Windows and Linux\" href=\"#yolo-v4-v3-and-v2-for-windows-and-linux\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e(neural networks for object detection)\u003c/h2\u003e\u003ca id=\"user-content-neural-networks-for-object-detection\" class=\"anchor\" aria-label=\"Permalink: (neural networks for object detection)\" href=\"#neural-networks-for-object-detection\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePaper YOLO v4: \u003ca href=\"https://arxiv.org/abs/2004.10934\" rel=\"nofollow\"\u003ehttps://arxiv.org/abs/2004.10934\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003ePaper Scaled YOLO v4: \u003ca href=\"https://arxiv.org/abs/2011.08036\" rel=\"nofollow\"\u003ehttps://arxiv.org/abs/2011.08036\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eMore details: \u003ca href=\"https://medium.com/@alexeyab84/yolov4-the-most-accurate-real-time-neural-network-on-ms-coco-dataset-73adfd3602fe?source=friends_link\u0026amp;sk=6039748846bbcf1d960c3061542591d7\" rel=\"nofollow\"\u003emedium link\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eManual: \u003ca href=\"https://github.com/AlexeyAB/darknet/wiki\"\u003ehttps://github.com/AlexeyAB/darknet/wiki\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eDiscussion:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://www.reddit.com/r/MachineLearning/comments/gydxzd/p_yolov4_the_most_accurate_realtime_neural/\" rel=\"nofollow\"\u003eReddit\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://groups.google.com/forum/#!forum/darknet\" rel=\"nofollow\"\u003eGoogle-groups\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://discord.gg/zSq8rtW\" rel=\"nofollow\"\u003eDiscord\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eAbout Darknet framework: \u003ca href=\"http://pjreddie.com/darknet/\" rel=\"nofollow\"\u003ehttp://pjreddie.com/darknet/\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://github.com/AlexeyAB/darknet/actions?query=workflow%3A%22Darknet+Continuous+Integration%22\"\u003e\u003cimg src=\"https://github.com/AlexeyAB/darknet/workflows/Darknet%20Continuous%20Integration/badge.svg\" alt=\"Darknet Continuous Integration\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://circleci.com/gh/AlexeyAB/darknet\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2a61971b36eda7db271b4e5452c78a9687c202537326c377f2fe022119e68a13/68747470733a2f2f636972636c6563692e636f6d2f67682f416c6578657941422f6461726b6e65742e7376673f7374796c653d737667\" alt=\"CircleCI\" data-canonical-src=\"https://circleci.com/gh/AlexeyAB/darknet.svg?style=svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://travis-ci.org/AlexeyAB/darknet\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f5ac6f898829b2c820a5ed146577c36d5268515cbad3d2ff955b28e1bc70e6cf/68747470733a2f2f7472617669732d63692e6f72672f416c6578657941422f6461726b6e65742e7376673f6272616e63683d6d6173746572\" alt=\"TravisCI\" data-canonical-src=\"https://travis-ci.org/AlexeyAB/darknet.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/AlexeyAB/darknet/graphs/contributors\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b634abea55c448d288f07d6a300a27e3ac29996fa2c12cb9bb190765d4fb4bb8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f636f6e7472696275746f72732f416c6578657941422f4461726b6e65742e737667\" alt=\"Contributors\" data-canonical-src=\"https://img.shields.io/github/contributors/AlexeyAB/Darknet.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/AlexeyAB/darknet/blob/master/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b4c74d90cf40e8e768fcacf6d423cb23f849b05dd7fa01bd6bfd441291f0c53c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d556e6c6963656e73652d626c75652e737667\" alt=\"License: Unlicense\" data-canonical-src=\"https://img.shields.io/badge/license-Unlicense-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://zenodo.org/badge/latestdoi/75388965\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ef0a402c8c6fb5952d2fb54b6859ff3c32941c7e112d45d69f0f067e578e68af/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f37353338383936352e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/75388965.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://arxiv.org/abs/2004.10934\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/896881fe1ae407dcc8bb09df37e83b70feba9f7aa611fe1a298e4d0ac6852612/687474703a2f2f696d672e736869656c64732e696f2f62616467652f63732e43562d6172586976253341323030342e31303933342d4233314231422e737667\" alt=\"arxiv.org\" data-canonical-src=\"http://img.shields.io/badge/cs.CV-arXiv%3A2004.10934-B31B1B.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://colab.research.google.com/drive/12QusaaRj_lUwCGDvQNfICpa7kA7_a2dE\" rel=\"nofollow\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/4096485/86174089-b2709f80-bb29-11ea-9faf-3d8dc668a1a5.png\" alt=\"colab\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://colab.research.google.com/drive/1_GdoqCJWXsChrOiY8sZMr_zbr_fH-0Fg\" rel=\"nofollow\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/4096485/86174097-b56b9000-bb29-11ea-9240-c17f6bacfc34.png\" alt=\"colab\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/AlexeyAB/darknet/wiki/YOLOv4-model-zoo\"\u003eYOLOv4 model zoo\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#requirements\"\u003eRequirements (and how to install dependecies)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#pre-trained-models\"\u003ePre-trained models\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/AlexeyAB/darknet/wiki/FAQ---frequently-asked-questions\"\u003eFAQ - frequently asked questions\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/AlexeyAB/darknet/issues?q=is%3Aopen+is%3Aissue+label%3AExplanations\"\u003eExplanations in issues\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#yolo-v4-in-other-frameworks\"\u003eYolo v4 in other frameworks (TensorRT, TensorFlow, PyTorch, OpenVINO, OpenCV-dnn, TVM,...)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#datasets\"\u003eDatasets\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"0\" dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#improvements-in-this-repository\"\u003eImprovements in this repository\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#how-to-use-on-the-command-line\"\u003eHow to use\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eHow to compile on Linux\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#how-to-compile-on-linux-using-cmake\"\u003eUsing cmake\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#how-to-compile-on-linux-using-make\"\u003eUsing make\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eHow to compile on Windows\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#how-to-compile-on-windows-using-cmake\"\u003eUsing cmake\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#how-to-compile-on-windows-using-vcpkg\"\u003eUsing vcpkg\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#how-to-compile-on-windows-legacy-way\"\u003eLegacy way\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/AlexeyAB/darknet/wiki#training-and-evaluation-of-speed-and-accuracy-on-ms-coco\"\u003eTraining and Evaluation of speed and accuracy on MS COCO\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#how-to-train-with-multi-gpu\"\u003eHow to train with multi-GPU:\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#how-to-train-to-detect-your-custom-objects\"\u003eHow to train (to detect your custom objects)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#how-to-train-tiny-yolo-to-detect-your-custom-objects\"\u003eHow to train tiny-yolo (to detect your custom objects)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#when-should-i-stop-training\"\u003eWhen should I stop training\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#how-to-improve-object-detection\"\u003eHow to improve object detection\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files\"\u003eHow to mark bounded boxes of objects and create annotation files\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#how-to-use-yolo-as-dll-and-so-libraries\"\u003eHow to use Yolo as DLL and SO libraries\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/fe9d157cc2c42d6b70dec5a1273c7e1be99e2cc371437d28ddf4b8ce64e44bf5/687474703a2f2f706a7265646469652e636f6d2f6d656469612f66696c65732f6461726b6e65742d626c61636b2d736d616c6c2e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fe9d157cc2c42d6b70dec5a1273c7e1be99e2cc371437d28ddf4b8ce64e44bf5/687474703a2f2f706a7265646469652e636f6d2f6d656469612f66696c65732f6461726b6e65742d626c61636b2d736d616c6c2e706e67\" alt=\"Darknet Logo\" data-canonical-src=\"http://pjreddie.com/media/files/darknet-black-small.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/4096485/101356322-f1f5a180-38a8-11eb-9907-4fe4f188d887.png\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/4096485/101356322-f1f5a180-38a8-11eb-9907-4fe4f188d887.png\" alt=\"scaled_yolov4\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e AP50:95 - FPS (Tesla V100) Paper: \u003ca href=\"https://arxiv.org/abs/2011.08036\" rel=\"nofollow\"\u003ehttps://arxiv.org/abs/2011.08036\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/4096485/82835867-f1c62380-9ecd-11ea-9134-1598ed2abc4b.png\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/4096485/82835867-f1c62380-9ecd-11ea-9134-1598ed2abc4b.png\" alt=\"modern_gpus\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e AP50:95 / AP50 - FPS (Tesla V100) Paper: \u003ca href=\"https://arxiv.org/abs/2004.10934\" rel=\"nofollow\"\u003ehttps://arxiv.org/abs/2004.10934\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003etkDNN-TensorRT accelerates YOLOv4 \u003cstrong\u003e~2x\u003c/strong\u003e times for batch=1 and \u003cstrong\u003e3x-4x\u003c/strong\u003e times for batch=4.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003etkDNN: \u003ca href=\"https://github.com/ceccocats/tkDNN\"\u003ehttps://github.com/ceccocats/tkDNN\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eOpenCV: \u003ca href=\"https://gist.github.com/YashasSamaga/48bdb167303e10f4d07b754888ddbdcf\"\u003ehttps://gist.github.com/YashasSamaga/48bdb167303e10f4d07b754888ddbdcf\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGeForce RTX 2080 Ti:\u003c/h4\u003e\u003ca id=\"user-content-geforce-rtx-2080-ti\" class=\"anchor\" aria-label=\"Permalink: GeForce RTX 2080 Ti:\" href=\"#geforce-rtx-2080-ti\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eNetwork Size\u003c/th\u003e\n\u003cth align=\"center\"\u003eDarknet, FPS (avg)\u003c/th\u003e\n\u003cth align=\"right\"\u003etkDNN TensorRT FP32, FPS\u003c/th\u003e\n\u003cth align=\"right\"\u003etkDNN TensorRT FP16, FPS\u003c/th\u003e\n\u003cth align=\"right\"\u003eOpenCV FP16, FPS\u003c/th\u003e\n\u003cth align=\"right\"\u003etkDNN TensorRT FP16 batch=4, FPS\u003c/th\u003e\n\u003cth align=\"right\"\u003eOpenCV FP16 batch=4, FPS\u003c/th\u003e\n\u003cth align=\"right\"\u003etkDNN Speedup\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e320\u003c/td\u003e\n\u003ctd align=\"center\"\u003e100\u003c/td\u003e\n\u003ctd align=\"right\"\u003e116\u003c/td\u003e\n\u003ctd align=\"right\"\u003e\u003cstrong\u003e202\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"right\"\u003e183\u003c/td\u003e\n\u003ctd align=\"right\"\u003e423\u003c/td\u003e\n\u003ctd align=\"right\"\u003e\u003cstrong\u003e430\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"right\"\u003e\u003cstrong\u003e4.3x\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e416\u003c/td\u003e\n\u003ctd align=\"center\"\u003e82\u003c/td\u003e\n\u003ctd align=\"right\"\u003e103\u003c/td\u003e\n\u003ctd align=\"right\"\u003e\u003cstrong\u003e162\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"right\"\u003e159\u003c/td\u003e\n\u003ctd align=\"right\"\u003e284\u003c/td\u003e\n\u003ctd align=\"right\"\u003e\u003cstrong\u003e294\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"right\"\u003e\u003cstrong\u003e3.6x\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e512\u003c/td\u003e\n\u003ctd align=\"center\"\u003e69\u003c/td\u003e\n\u003ctd align=\"right\"\u003e91\u003c/td\u003e\n\u003ctd align=\"right\"\u003e134\u003c/td\u003e\n\u003ctd align=\"right\"\u003e\u003cstrong\u003e138\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"right\"\u003e206\u003c/td\u003e\n\u003ctd align=\"right\"\u003e\u003cstrong\u003e216\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"right\"\u003e\u003cstrong\u003e3.1x\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e608\u003c/td\u003e\n\u003ctd align=\"center\"\u003e53\u003c/td\u003e\n\u003ctd align=\"right\"\u003e62\u003c/td\u003e\n\u003ctd align=\"right\"\u003e103\u003c/td\u003e\n\u003ctd align=\"right\"\u003e\u003cstrong\u003e115\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"right\"\u003e150\u003c/td\u003e\n\u003ctd align=\"right\"\u003e\u003cstrong\u003e150\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"right\"\u003e\u003cstrong\u003e2.8x\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eTiny 416\u003c/td\u003e\n\u003ctd align=\"center\"\u003e443\u003c/td\u003e\n\u003ctd align=\"right\"\u003e609\u003c/td\u003e\n\u003ctd align=\"right\"\u003e\u003cstrong\u003e790\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"right\"\u003e773\u003c/td\u003e\n\u003ctd align=\"right\"\u003e\u003cstrong\u003e1774\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"right\"\u003e1353\u003c/td\u003e\n\u003ctd align=\"right\"\u003e\u003cstrong\u003e3.5x\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eTiny 416 CPU Core i7 7700HQ\u003c/td\u003e\n\u003ctd align=\"center\"\u003e3.4\u003c/td\u003e\n\u003ctd align=\"right\"\u003e-\u003c/td\u003e\n\u003ctd align=\"right\"\u003e-\u003c/td\u003e\n\u003ctd align=\"right\"\u003e42\u003c/td\u003e\n\u003ctd align=\"right\"\u003e-\u003c/td\u003e\n\u003ctd align=\"right\"\u003e39\u003c/td\u003e\n\u003ctd align=\"right\"\u003e\u003cstrong\u003e12x\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYolo v4 Full comparison: \u003ca href=\"https://user-images.githubusercontent.com/4096485/80283279-0e303e00-871f-11ea-814c-870967d77fd1.png\" rel=\"nofollow\"\u003emap_fps\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eYolo v4 tiny comparison: \u003ca href=\"https://user-images.githubusercontent.com/4096485/85734112-6e366700-b705-11ea-95d1-fcba0de76d72.png\" rel=\"nofollow\"\u003etiny_fps\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eCSPNet: \u003ca href=\"https://arxiv.org/abs/1911.11929\" rel=\"nofollow\"\u003epaper\u003c/a\u003e and \u003ca href=\"https://user-images.githubusercontent.com/4096485/71702416-6645dc00-2de0-11ea-8d65-de7d4b604021.png\" rel=\"nofollow\"\u003emap_fps\u003c/a\u003e comparison: \u003ca href=\"https://github.com/WongKinYiu/CrossStagePartialNetworks\"\u003ehttps://github.com/WongKinYiu/CrossStagePartialNetworks\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eYolo v3 on MS COCO: \u003ca href=\"https://user-images.githubusercontent.com/4096485/52151356-e5d4a380-2683-11e9-9d7d-ac7bc192c477.jpg\" rel=\"nofollow\"\u003eSpeed / Accuracy (mAP@0.5) chart\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eYolo v3 on MS COCO (Yolo v3 vs RetinaNet) - Figure 3: \u003ca href=\"https://arxiv.org/pdf/1804.02767v1.pdf\" rel=\"nofollow\"\u003ehttps://arxiv.org/pdf/1804.02767v1.pdf\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eYolo v2 on Pascal VOC 2007: \u003ca href=\"https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg\" rel=\"nofollow\"\u003ehttps://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eYolo v2 on Pascal VOC 2012 (comp4): \u003ca href=\"https://hsto.org/files/3a6/fdf/b53/3a6fdfb533f34cee9b52bdd9bb0b19d9.jpg\" rel=\"nofollow\"\u003ehttps://hsto.org/files/3a6/fdf/b53/3a6fdfb533f34cee9b52bdd9bb0b19d9.jpg\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eYoutube video of results\u003c/h4\u003e\u003ca id=\"user-content-youtube-video-of-results\" class=\"anchor\" aria-label=\"Permalink: Youtube video of results\" href=\"#youtube-video-of-results\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003ca href=\"https://youtu.be/1_SiUOYUoOI\" title=\"Yolo v4\" rel=\"nofollow\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/4096485/101360000-1a33cf00-38ae-11eb-9e5e-b29c5fb0afbe.png\" alt=\"Yolo v4\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca href=\"https://youtu.be/YDFf-TqJOFE\" title=\"Scaled Yolo v4\" rel=\"nofollow\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/4096485/101359389-43a02b00-38ad-11eb-866c-f813e96bf61a.png\" alt=\"Scaled Yolo v4\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003eOthers: \u003ca href=\"https://www.youtube.com/user/pjreddie/videos\" rel=\"nofollow\"\u003ehttps://www.youtube.com/user/pjreddie/videos\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow to evaluate AP of YOLOv4 on the MS COCO evaluation server\u003c/h4\u003e\u003ca id=\"user-content-how-to-evaluate-ap-of-yolov4-on-the-ms-coco-evaluation-server\" class=\"anchor\" aria-label=\"Permalink: How to evaluate AP of YOLOv4 on the MS COCO evaluation server\" href=\"#how-to-evaluate-ap-of-yolov4-on-the-ms-coco-evaluation-server\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003eDownload and unzip test-dev2017 dataset from MS COCO server: \u003ca href=\"http://images.cocodataset.org/zips/test2017.zip\" rel=\"nofollow\"\u003ehttp://images.cocodataset.org/zips/test2017.zip\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eDownload list of images for Detection taks and replace the paths with yours: \u003ca href=\"https://raw.githubusercontent.com/AlexeyAB/darknet/master/scripts/testdev2017.txt\" rel=\"nofollow\"\u003ehttps://raw.githubusercontent.com/AlexeyAB/darknet/master/scripts/testdev2017.txt\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eDownload \u003ccode\u003eyolov4.weights\u003c/code\u003e file 245 MB: \u003ca href=\"https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights\"\u003eyolov4.weights\u003c/a\u003e (Google-drive mirror \u003ca href=\"https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT\" rel=\"nofollow\"\u003eyolov4.weights\u003c/a\u003e )\u003c/li\u003e\n\u003cli\u003eContent of the file \u003ccode\u003ecfg/coco.data\u003c/code\u003e should be\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-ini notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"classes= 80\ntrain = \u0026lt;replace with your path\u0026gt;/trainvalno5k.txt\nvalid = \u0026lt;replace with your path\u0026gt;/testdev2017.txt\nnames = data/coco.names\nbackup = backup\neval=coco\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eclasses\u003c/span\u003e= 80\n\u003cspan class=\"pl-k\"\u003etrain\u003c/span\u003e = \u0026lt;replace with your path\u0026gt;/trainvalno5k.txt\n\u003cspan class=\"pl-k\"\u003evalid\u003c/span\u003e = \u0026lt;replace with your path\u0026gt;/testdev2017.txt\n\u003cspan class=\"pl-k\"\u003enames\u003c/span\u003e = data/coco.names\n\u003cspan class=\"pl-k\"\u003ebackup\u003c/span\u003e = backup\n\u003cspan class=\"pl-k\"\u003eeval\u003c/span\u003e=coco\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"5\" dir=\"auto\"\u003e\n\u003cli\u003eCreate \u003ccode\u003e/results/\u003c/code\u003e folder near with \u003ccode\u003e./darknet\u003c/code\u003e executable file\u003c/li\u003e\n\u003cli\u003eRun validation: \u003ccode\u003e./darknet detector valid cfg/coco.data cfg/yolov4.cfg yolov4.weights\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eRename the file \u003ccode\u003e/results/coco_results.json\u003c/code\u003e to \u003ccode\u003edetections_test-dev2017_yolov4_results.json\u003c/code\u003e and compress it to \u003ccode\u003edetections_test-dev2017_yolov4_results.zip\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eSubmit file \u003ccode\u003edetections_test-dev2017_yolov4_results.zip\u003c/code\u003e to the MS COCO evaluation server for the \u003ccode\u003etest-dev2019 (bbox)\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow to evaluate FPS of YOLOv4 on GPU\u003c/h4\u003e\u003ca id=\"user-content-how-to-evaluate-fps-of-yolov4-on-gpu\" class=\"anchor\" aria-label=\"Permalink: How to evaluate FPS of YOLOv4 on GPU\" href=\"#how-to-evaluate-fps-of-yolov4-on-gpu\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003eCompile Darknet with \u003ccode\u003eGPU=1 CUDNN=1 CUDNN_HALF=1 OPENCV=1\u003c/code\u003e in the \u003ccode\u003eMakefile\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eDownload \u003ccode\u003eyolov4.weights\u003c/code\u003e file 245 MB: \u003ca href=\"https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights\"\u003eyolov4.weights\u003c/a\u003e (Google-drive mirror \u003ca href=\"https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT\" rel=\"nofollow\"\u003eyolov4.weights\u003c/a\u003e )\u003c/li\u003e\n\u003cli\u003eGet any .avi/.mp4 video file (preferably not more than 1920x1080 to avoid bottlenecks in CPU performance)\u003c/li\u003e\n\u003cli\u003eRun one of two commands and look at the AVG FPS:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003einclude video_capturing + NMS + drawing_bboxes:\n\u003ccode\u003e./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -dont_show -ext_output\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eexclude video_capturing + NMS + drawing_bboxes:\n\u003ccode\u003e./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -benchmark\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePre-trained models\u003c/h4\u003e\u003ca id=\"user-content-pre-trained-models\" class=\"anchor\" aria-label=\"Permalink: Pre-trained models\" href=\"#pre-trained-models\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThere are weights-file for different cfg-files (trained for MS COCO dataset):\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFPS on RTX 2070 (R) and Tesla V100 (V):\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4x-mish.cfg\" rel=\"nofollow\"\u003eyolov4x-mish.cfg\u003c/a\u003e - \u003cstrong\u003e67.9% mAP@0.5 (49.4% AP@0.5:0.95) - 23(R) FPS / 50(V) FPS\u003c/strong\u003e - 221 BFlops (110 FMA) - 381 MB: \u003ca href=\"https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.weights\"\u003eyolov4x-mish.weights\u003c/a\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003epre-trained weights for training: \u003ca href=\"https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.conv.166\"\u003ehttps://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.conv.166\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-csp.cfg\" rel=\"nofollow\"\u003eyolov4-csp.cfg\u003c/a\u003e - 202 MB: \u003ca href=\"https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.weights\"\u003eyolov4-csp.weights\u003c/a\u003e paper \u003ca href=\"https://arxiv.org/abs/2011.08036\" rel=\"nofollow\"\u003eScaled Yolo v4\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003ejust change \u003ccode\u003ewidth=\u003c/code\u003e and \u003ccode\u003eheight=\u003c/code\u003e parameters in \u003ccode\u003eyolov4-csp.cfg\u003c/code\u003e file and use the same \u003ccode\u003eyolov4-csp.weights\u003c/code\u003e file for all cases:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ccode\u003ewidth=608 height=608\u003c/code\u003e in cfg: \u003cstrong\u003e66.2% mAP@0.5 (47.5% AP@0.5:0.95) - 70(V) FPS\u003c/strong\u003e - 120 (60 FMA) BFlops\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ewidth=512 height=512\u003c/code\u003e in cfg: \u003cstrong\u003e64.8% mAP@0.5 (46.2% AP@0.5:0.95) - 93(V) FPS\u003c/strong\u003e - 77 (39 FMA) BFlops\u003c/li\u003e\n\u003cli\u003epre-trained weights for training: \u003ca href=\"https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.conv.142\"\u003ehttps://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.conv.142\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg\" rel=\"nofollow\"\u003eyolov4.cfg\u003c/a\u003e - 245 MB: \u003ca href=\"https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights\"\u003eyolov4.weights\u003c/a\u003e (Google-drive mirror \u003ca href=\"https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT\" rel=\"nofollow\"\u003eyolov4.weights\u003c/a\u003e ) paper \u003ca href=\"https://arxiv.org/abs/2004.10934\" rel=\"nofollow\"\u003eYolo v4\u003c/a\u003e\njust change \u003ccode\u003ewidth=\u003c/code\u003e and \u003ccode\u003eheight=\u003c/code\u003e parameters in \u003ccode\u003eyolov4.cfg\u003c/code\u003e file and use the same \u003ccode\u003eyolov4.weights\u003c/code\u003e file for all cases:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ccode\u003ewidth=608 height=608\u003c/code\u003e in cfg: \u003cstrong\u003e65.7% mAP@0.5 (43.5% AP@0.5:0.95) - 34(R) FPS / 62(V) FPS\u003c/strong\u003e - 128.5 BFlops\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ewidth=512 height=512\u003c/code\u003e in cfg: \u003cstrong\u003e64.9% mAP@0.5 (43.0% AP@0.5:0.95) - 45(R) FPS / 83(V) FPS\u003c/strong\u003e - 91.1 BFlops\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ewidth=416 height=416\u003c/code\u003e in cfg: \u003cstrong\u003e62.8% mAP@0.5 (41.2% AP@0.5:0.95) - 55(R) FPS / 96(V) FPS\u003c/strong\u003e - 60.1 BFlops\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003ewidth=320 height=320\u003c/code\u003e in cfg: \u003cstrong\u003e60% mAP@0.5 ( 38% AP@0.5:0.95) - 63(R) FPS / 123(V) FPS\u003c/strong\u003e - 35.5 BFlops\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny.cfg\" rel=\"nofollow\"\u003eyolov4-tiny.cfg\u003c/a\u003e - \u003cstrong\u003e40.2% mAP@0.5 - 371(1080Ti) FPS / 330(RTX2070) FPS\u003c/strong\u003e - 6.9 BFlops - 23.1 MB: \u003ca href=\"https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights\"\u003eyolov4-tiny.weights\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/enet-coco.cfg\" rel=\"nofollow\"\u003eenet-coco.cfg (EfficientNetB0-Yolov3)\u003c/a\u003e - \u003cstrong\u003e45.5% mAP@0.5 - 55(R) FPS\u003c/strong\u003e - 3.7 BFlops - 18.3 MB: \u003ca href=\"https://drive.google.com/file/d/1FlHeQjWEQVJt0ay1PVsiuuMzmtNyv36m/view\" rel=\"nofollow\"\u003eenetb0-coco_final.weights\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-openimages.cfg\" rel=\"nofollow\"\u003eyolov3-openimages.cfg\u003c/a\u003e - 247 MB - 18(R) FPS - OpenImages dataset: \u003ca href=\"https://pjreddie.com/media/files/yolov3-openimages.weights\" rel=\"nofollow\"\u003eyolov3-openimages.weights\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdetails\u003e\u003csummary\u003e\u003cb\u003eCLICK ME\u003c/b\u003e - Yolo v3 models\u003c/summary\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/csresnext50-panet-spp-original-optimal.cfg\" rel=\"nofollow\"\u003ecsresnext50-panet-spp-original-optimal.cfg\u003c/a\u003e - \u003cstrong\u003e65.4% mAP@0.5 (43.2% AP@0.5:0.95) - 32(R) FPS\u003c/strong\u003e - 100.5 BFlops - 217 MB: \u003ca href=\"https://drive.google.com/open?id=1_NnfVgj0EDtb_WLNoXV8Mo7WKgwdYZCc\" rel=\"nofollow\"\u003ecsresnext50-panet-spp-original-optimal_final.weights\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-spp.cfg\" rel=\"nofollow\"\u003eyolov3-spp.cfg\u003c/a\u003e - \u003cstrong\u003e60.6% mAP@0.5 - 38(R) FPS\u003c/strong\u003e - 141.5 BFlops - 240 MB: \u003ca href=\"https://pjreddie.com/media/files/yolov3-spp.weights\" rel=\"nofollow\"\u003eyolov3-spp.weights\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/csresnext50-panet-spp.cfg\" rel=\"nofollow\"\u003ecsresnext50-panet-spp.cfg\u003c/a\u003e - \u003cstrong\u003e60.0% mAP@0.5 - 44 FPS\u003c/strong\u003e - 71.3 BFlops - 217 MB: \u003ca href=\"https://drive.google.com/file/d/1aNXdM8qVy11nqTcd2oaVB3mf7ckr258-/view?usp=sharing\" rel=\"nofollow\"\u003ecsresnext50-panet-spp_final.weights\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3.cfg\" rel=\"nofollow\"\u003eyolov3.cfg\u003c/a\u003e - \u003cstrong\u003e55.3% mAP@0.5 - 66(R) FPS\u003c/strong\u003e - 65.9 BFlops - 236 MB: \u003ca href=\"https://pjreddie.com/media/files/yolov3.weights\" rel=\"nofollow\"\u003eyolov3.weights\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny.cfg\" rel=\"nofollow\"\u003eyolov3-tiny.cfg\u003c/a\u003e - \u003cstrong\u003e33.1% mAP@0.5 - 345(R) FPS\u003c/strong\u003e - 5.6 BFlops - 33.7 MB: \u003ca href=\"https://pjreddie.com/media/files/yolov3-tiny.weights\" rel=\"nofollow\"\u003eyolov3-tiny.weights\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny-prn.cfg\" rel=\"nofollow\"\u003eyolov3-tiny-prn.cfg\u003c/a\u003e - \u003cstrong\u003e33.1% mAP@0.5 - 370(R) FPS\u003c/strong\u003e - 3.5 BFlops - 18.8 MB: \u003ca href=\"https://drive.google.com/file/d/18yYZWyKbo4XSDVyztmsEcF9B_6bxrhUY/view?usp=sharing\" rel=\"nofollow\"\u003eyolov3-tiny-prn.weights\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\u003csummary\u003e\u003cb\u003eCLICK ME\u003c/b\u003e - Yolo v2 models\u003c/summary\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ccode\u003eyolov2.cfg\u003c/code\u003e (194 MB COCO Yolo v2) - requires 4 GB GPU-RAM: \u003ca href=\"https://pjreddie.com/media/files/yolov2.weights\" rel=\"nofollow\"\u003ehttps://pjreddie.com/media/files/yolov2.weights\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eyolo-voc.cfg\u003c/code\u003e (194 MB VOC Yolo v2) - requires 4 GB GPU-RAM: \u003ca href=\"http://pjreddie.com/media/files/yolo-voc.weights\" rel=\"nofollow\"\u003ehttp://pjreddie.com/media/files/yolo-voc.weights\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eyolov2-tiny.cfg\u003c/code\u003e (43 MB COCO Yolo v2) - requires 1 GB GPU-RAM: \u003ca href=\"https://pjreddie.com/media/files/yolov2-tiny.weights\" rel=\"nofollow\"\u003ehttps://pjreddie.com/media/files/yolov2-tiny.weights\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eyolov2-tiny-voc.cfg\u003c/code\u003e (60 MB VOC Yolo v2) - requires 1 GB GPU-RAM: \u003ca href=\"http://pjreddie.com/media/files/yolov2-tiny-voc.weights\" rel=\"nofollow\"\u003ehttp://pjreddie.com/media/files/yolov2-tiny-voc.weights\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eyolo9000.cfg\u003c/code\u003e (186 MB Yolo9000-model) - requires 4 GB GPU-RAM: \u003ca href=\"http://pjreddie.com/media/files/yolo9000.weights\" rel=\"nofollow\"\u003ehttp://pjreddie.com/media/files/yolo9000.weights\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/details\u003e\n\u003cp dir=\"auto\"\u003ePut it near compiled: darknet.exe\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eYou can get cfg-files by path: \u003ccode\u003edarknet/cfg/\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRequirements\u003c/h3\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eWindows or Linux\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCMake \u0026gt;= 3.12\u003c/strong\u003e: \u003ca href=\"https://cmake.org/download/\" rel=\"nofollow\"\u003ehttps://cmake.org/download/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCUDA \u0026gt;= 10.0\u003c/strong\u003e: \u003ca href=\"https://developer.nvidia.com/cuda-toolkit-archive\" rel=\"nofollow\"\u003ehttps://developer.nvidia.com/cuda-toolkit-archive\u003c/a\u003e (on Linux do \u003ca href=\"https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions\" rel=\"nofollow\"\u003ePost-installation Actions\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eOpenCV \u0026gt;= 2.4\u003c/strong\u003e: use your preferred package manager (brew, apt), build from source using \u003ca href=\"https://github.com/Microsoft/vcpkg\"\u003evcpkg\u003c/a\u003e or download from \u003ca href=\"https://opencv.org/releases.html\" rel=\"nofollow\"\u003eOpenCV official site\u003c/a\u003e (on Windows set system variable \u003ccode\u003eOpenCV_DIR\u003c/code\u003e = \u003ccode\u003eC:\\opencv\\build\u003c/code\u003e - where are the \u003ccode\u003einclude\u003c/code\u003e and \u003ccode\u003ex64\u003c/code\u003e folders \u003ca href=\"https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png\" rel=\"nofollow\"\u003eimage\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ecuDNN \u0026gt;= 7.0\u003c/strong\u003e \u003ca href=\"https://developer.nvidia.com/rdp/cudnn-archive\" rel=\"nofollow\"\u003ehttps://developer.nvidia.com/rdp/cudnn-archive\u003c/a\u003e (on \u003cstrong\u003eLinux\u003c/strong\u003e copy \u003ccode\u003ecudnn.h\u003c/code\u003e,\u003ccode\u003elibcudnn.so\u003c/code\u003e... as desribed here \u003ca href=\"https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar\" rel=\"nofollow\"\u003ehttps://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar\u003c/a\u003e , on \u003cstrong\u003eWindows\u003c/strong\u003e copy \u003ccode\u003ecudnn.h\u003c/code\u003e,\u003ccode\u003ecudnn64_7.dll\u003c/code\u003e, \u003ccode\u003ecudnn64_7.lib\u003c/code\u003e as desribed here \u003ca href=\"https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows\" rel=\"nofollow\"\u003ehttps://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows\u003c/a\u003e )\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eGPU with CC \u0026gt;= 3.0\u003c/strong\u003e: \u003ca href=\"https://en.wikipedia.org/wiki/CUDA#GPUs_supported\" rel=\"nofollow\"\u003ehttps://en.wikipedia.org/wiki/CUDA#GPUs_supported\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eon Linux \u003cstrong\u003eGCC or Clang\u003c/strong\u003e, on Windows \u003cstrong\u003eMSVC 2017/2019\u003c/strong\u003e \u003ca href=\"https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community\" rel=\"nofollow\"\u003ehttps://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eYolo v4 in other frameworks\u003c/h4\u003e\u003ca id=\"user-content-yolo-v4-in-other-frameworks\" class=\"anchor\" aria-label=\"Permalink: Yolo v4 in other frameworks\" href=\"#yolo-v4-in-other-frameworks\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003ePytorch - Scaled-YOLOv4:\u003c/strong\u003e \u003ca href=\"https://github.com/WongKinYiu/ScaledYOLOv4\"\u003ehttps://github.com/WongKinYiu/ScaledYOLOv4\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eTensorFlow:\u003c/strong\u003e \u003ccode\u003epip install yolov4\u003c/code\u003e YOLOv4 on TensorFlow 2.0 / TFlite / Andriod: \u003ca href=\"https://github.com/hunglc007/tensorflow-yolov4-tflite\"\u003ehttps://github.com/hunglc007/tensorflow-yolov4-tflite\u003c/a\u003e\nFor YOLOv3 - convert \u003ccode\u003eyolov3.weights\u003c/code\u003e/\u003ccode\u003ecfg\u003c/code\u003e files to \u003ccode\u003eyolov3.ckpt\u003c/code\u003e/\u003ccode\u003epb/meta\u003c/code\u003e: by using \u003ca href=\"https://github.com/mystic123/tensorflow-yolo-v3\"\u003emystic123\u003c/a\u003e project, and \u003ca href=\"https://www.tensorflow.org/lite/guide/get_started#2_convert_the_model_format\" rel=\"nofollow\"\u003eTensorFlow-lite\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eOpenCV-dnn\u003c/strong\u003e the fastest implementation of YOLOv4 for CPU (x86/ARM-Android), OpenCV can be compiled with \u003ca href=\"https://github.com/opencv/opencv/wiki/Intel's-Deep-Learning-Inference-Engine-backend\"\u003eOpenVINO-backend\u003c/a\u003e for running on (Myriad X / USB Neural Compute Stick / Arria FPGA), use \u003ccode\u003eyolov4.weights\u003c/code\u003e/\u003ccode\u003ecfg\u003c/code\u003e with: \u003ca href=\"https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221\"\u003eC++ example\u003c/a\u003e or \u003ca href=\"https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150\"\u003ePython example\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eIntel OpenVINO 2020 R4:\u003c/strong\u003e (NPU Myriad X / USB Neural Compute Stick / Arria FPGA): read this \u003ca href=\"https://github.com/TNTWEN/OpenVINO-YOLOV4\"\u003emanual\u003c/a\u003e (old \u003ca href=\"https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model\" rel=\"nofollow\"\u003emanual\u003c/a\u003e )\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eTencent/ncnn:\u003c/strong\u003e the fastest inference of YOLOv4 on mobile phone CPU: \u003ca href=\"https://github.com/Tencent/ncnn\"\u003ehttps://github.com/Tencent/ncnn\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ePyTorch \u0026gt; ONNX\u003c/strong\u003e:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/WongKinYiu/PyTorch_YOLOv4\"\u003eWongKinYiu/PyTorch_YOLOv4\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/maudzung/Complex-YOLOv4-Pytorch\"\u003emaudzung/3D-YOLOv4\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Tianxiaomo/pytorch-YOLOv4\"\u003eTianxiaomo/pytorch-YOLOv4\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ultralytics/yolov5\"\u003eYOLOv5\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eONNX\u003c/strong\u003e on Jetson for YOLOv4: \u003ca href=\"https://developer.nvidia.com/blog/announcing-onnx-runtime-for-jetson/\" rel=\"nofollow\"\u003ehttps://developer.nvidia.com/blog/announcing-onnx-runtime-for-jetson/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eTensorRT\u003c/strong\u003e YOLOv4 on TensorRT+tkDNN: \u003ca href=\"https://github.com/ceccocats/tkDNN\"\u003ehttps://github.com/ceccocats/tkDNN\u003c/a\u003e\nFor YOLOv3 (-70% faster inference): \u003ca href=\"https://news.developer.nvidia.com/deepstream-sdk-4-now-available/\" rel=\"nofollow\"\u003eYolo is natively supported in DeepStream 4.0\u003c/a\u003e read \u003ca href=\"https://docs.nvidia.com/metropolis/deepstream/Custom_YOLO_Model_in_the_DeepStream_YOLO_App.pdf\" rel=\"nofollow\"\u003ePDF\u003c/a\u003e. \u003ca href=\"https://github.com/wang-xinyu/tensorrtx\"\u003ewang-xinyu/tensorrtx\u003c/a\u003e implemented yolov3-spp, yolov4, etc.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDeepstream 5.0 / TensorRT for YOLOv4\u003c/strong\u003e \u003ca href=\"https://github.com/NVIDIA-AI-IOT/yolov4_deepstream\"\u003ehttps://github.com/NVIDIA-AI-IOT/yolov4_deepstream\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eAmazon Neurochip / Amazon EC2 Inf1 instances\u003c/strong\u003e 1.85 times higher throughput and 37% lower cost per image for TensorFlow based YOLOv4 model, using Keras \u003ca href=\"https://aws.amazon.com/ru/blogs/machine-learning/improving-performance-for-deep-learning-based-object-detection-with-an-aws-neuron-compiled-yolov4-model-on-aws-inferentia/\" rel=\"nofollow\"\u003eURL\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eTVM\u003c/strong\u003e - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backends (CPUs, GPUs, FPGA, and specialized accelerators): \u003ca href=\"https://tvm.ai/about\" rel=\"nofollow\"\u003ehttps://tvm.ai/about\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eOpenDataCam\u003c/strong\u003e - It detects, tracks and counts moving objects by using YOLOv4: \u003ca href=\"https://github.com/opendatacam/opendatacam#-hardware-pre-requisite\"\u003ehttps://github.com/opendatacam/opendatacam#-hardware-pre-requisite\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eNetron\u003c/strong\u003e - Visualizer for neural networks: \u003ca href=\"https://github.com/lutzroeder/netron\"\u003ehttps://github.com/lutzroeder/netron\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDatasets\u003c/h4\u003e\u003ca id=\"user-content-datasets\" class=\"anchor\" aria-label=\"Permalink: Datasets\" href=\"#datasets\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMS COCO: use \u003ccode\u003e./scripts/get_coco_dataset.sh\u003c/code\u003e to get labeled MS COCO detection dataset\u003c/li\u003e\n\u003cli\u003eOpenImages: use \u003ccode\u003epython ./scripts/get_openimages_dataset.py\u003c/code\u003e for labeling train detection dataset\u003c/li\u003e\n\u003cli\u003ePascal VOC: use \u003ccode\u003epython ./scripts/voc_label.py\u003c/code\u003e for labeling Train/Test/Val detection datasets\u003c/li\u003e\n\u003cli\u003eILSVRC2012 (ImageNet classification): use \u003ccode\u003e./scripts/get_imagenet_train.sh\u003c/code\u003e (also \u003ccode\u003eimagenet_label.sh\u003c/code\u003e for labeling valid set)\u003c/li\u003e\n\u003cli\u003eGerman/Belgium/Russian/LISA/MASTIF Traffic Sign Datasets for Detection - use this parsers: \u003ca href=\"https://github.com/angeligareta/Datasets2Darknet#detection-task\"\u003ehttps://github.com/angeligareta/Datasets2Darknet#detection-task\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eList of other datasets: \u003ca href=\"https://github.com/AlexeyAB/darknet/tree/master/scripts#datasets\"\u003ehttps://github.com/AlexeyAB/darknet/tree/master/scripts#datasets\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eImprovements in this repository\u003c/h3\u003e\u003ca id=\"user-content-improvements-in-this-repository\" class=\"anchor\" aria-label=\"Permalink: Improvements in this repository\" href=\"#improvements-in-this-repository\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003edeveloped State-of-the-Art object detector YOLOv4\u003c/li\u003e\n\u003cli\u003eadded State-of-Art models: CSP, PRN, EfficientNet\u003c/li\u003e\n\u003cli\u003eadded layers: [conv_lstm], [scale_channels] SE/ASFF/BiFPN, [local_avgpool], [sam], [Gaussian_yolo], [reorg3d] (fixed [reorg]), fixed [batchnorm]\u003c/li\u003e\n\u003cli\u003eadded the ability for training recurrent models (with layers conv-lstm\u003ccode\u003e[conv_lstm]\u003c/code\u003e/conv-rnn\u003ccode\u003e[crnn]\u003c/code\u003e) for accurate detection on video\u003c/li\u003e\n\u003cli\u003eadded data augmentation: \u003ccode\u003e[net] mixup=1 cutmix=1 mosaic=1 blur=1\u003c/code\u003e. Added activations: SWISH, MISH, NORM_CHAN, NORM_CHAN_SOFTMAX\u003c/li\u003e\n\u003cli\u003eadded the ability for training with GPU-processing using CPU-RAM to increase the mini_batch_size and increase accuracy (instead of batch-norm sync)\u003c/li\u003e\n\u003cli\u003eimproved binary neural network performance \u003cstrong\u003e2x-4x times\u003c/strong\u003e for Detection on CPU and GPU if you trained your own weights by using this XNOR-net model (bit-1 inference) : \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/master/cfg/yolov3-tiny_xnor.cfg\"\u003ehttps://github.com/AlexeyAB/darknet/blob/master/cfg/yolov3-tiny_xnor.cfg\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eimproved neural network performance \u003cstrong\u003e~7%\u003c/strong\u003e by fusing 2 layers into 1: Convolutional + Batch-norm\u003c/li\u003e\n\u003cli\u003eimproved performance: Detection \u003cstrong\u003e2x times\u003c/strong\u003e, on GPU Volta/Turing (Tesla V100, GeForce RTX, ...) using Tensor Cores if \u003ccode\u003eCUDNN_HALF\u003c/code\u003e defined in the \u003ccode\u003eMakefile\u003c/code\u003e or \u003ccode\u003edarknet.sln\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eimproved performance \u003cstrong\u003e~1.2x\u003c/strong\u003e times on FullHD, \u003cstrong\u003e~2x\u003c/strong\u003e times on 4K, for detection on the video (file/stream) using \u003ccode\u003edarknet detector demo\u003c/code\u003e...\u003c/li\u003e\n\u003cli\u003eimproved performance \u003cstrong\u003e3.5 X times\u003c/strong\u003e of data augmentation for training (using OpenCV SSE/AVX functions instead of hand-written functions) - removes bottleneck for training on multi-GPU or GPU Volta\u003c/li\u003e\n\u003cli\u003eimproved performance of detection and training on Intel CPU with AVX (Yolo v3 \u003cstrong\u003e~85%\u003c/strong\u003e)\u003c/li\u003e\n\u003cli\u003eoptimized memory allocation during network resizing when \u003ccode\u003erandom=1\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eoptimized GPU initialization for detection - we use batch=1 initially instead of re-init with batch=1\u003c/li\u003e\n\u003cli\u003eadded correct calculation of \u003cstrong\u003emAP, F1, IoU, Precision-Recall\u003c/strong\u003e using command \u003ccode\u003edarknet detector map\u003c/code\u003e...\u003c/li\u003e\n\u003cli\u003eadded drawing of chart of average-Loss and accuracy-mAP (\u003ccode\u003e-map\u003c/code\u003e flag) during training\u003c/li\u003e\n\u003cli\u003erun \u003ccode\u003e./darknet detector demo ... -json_port 8070 -mjpeg_port 8090\u003c/code\u003e as JSON and MJPEG server to get results online over the network by using your soft or Web-browser\u003c/li\u003e\n\u003cli\u003eadded calculation of anchors for training\u003c/li\u003e\n\u003cli\u003eadded example of Detection and Tracking objects: \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp\"\u003ehttps://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003erun-time tips and warnings if you use incorrect cfg-file or dataset\u003c/li\u003e\n\u003cli\u003eadded support for Windows\u003c/li\u003e\n\u003cli\u003emany other fixes of code...\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eAnd added manual - \u003ca href=\"#how-to-train-to-detect-your-custom-objects\"\u003eHow to train Yolo v4-v2 (to detect your custom objects)\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAlso, you might be interested in using a simplified repository where is implemented INT8-quantization (+30% speedup and -1% mAP reduced): \u003ca href=\"https://github.com/AlexeyAB/yolo2_light\"\u003ehttps://github.com/AlexeyAB/yolo2_light\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow to use on the command line\u003c/h4\u003e\u003ca id=\"user-content-how-to-use-on-the-command-line\" class=\"anchor\" aria-label=\"Permalink: How to use on the command line\" href=\"#how-to-use-on-the-command-line\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eOn Linux use \u003ccode\u003e./darknet\u003c/code\u003e instead of \u003ccode\u003edarknet.exe\u003c/code\u003e, like this:\u003ccode\u003e./darknet detector test ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eOn Linux find executable file \u003ccode\u003e./darknet\u003c/code\u003e in the root directory, while on Windows find it in the directory \u003ccode\u003e\\build\\darknet\\x64\u003c/code\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYolo v4 COCO - \u003cstrong\u003eimage\u003c/strong\u003e: \u003ccode\u003edarknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eOutput coordinates\u003c/strong\u003e of objects: \u003ccode\u003edarknet.exe detector test cfg/coco.data yolov4.cfg yolov4.weights -ext_output dog.jpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eYolo v4 COCO - \u003cstrong\u003evideo\u003c/strong\u003e: \u003ccode\u003edarknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output test.mp4\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eYolo v4 COCO - \u003cstrong\u003eWebCam 0\u003c/strong\u003e: \u003ccode\u003edarknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -c 0\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eYolo v4 COCO for \u003cstrong\u003enet-videocam\u003c/strong\u003e - Smart WebCam: \u003ccode\u003edarknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eYolo v4 - \u003cstrong\u003esave result videofile res.avi\u003c/strong\u003e: \u003ccode\u003edarknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -out_filename res.avi\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eYolo v3 \u003cstrong\u003eTiny\u003c/strong\u003e COCO - video: \u003ccode\u003edarknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights test.mp4\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eJSON and MJPEG server\u003c/strong\u003e that allows multiple connections from your soft or Web-browser \u003ccode\u003eip-address:8070\u003c/code\u003e and 8090: \u003ccode\u003e./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -mjpeg_port 8090 -ext_output\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eYolo v3 Tiny \u003cstrong\u003eon GPU #1\u003c/strong\u003e: \u003ccode\u003edarknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 1 test.mp4\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eAlternative method Yolo v3 COCO - image: \u003ccode\u003edarknet.exe detect cfg/yolov4.cfg yolov4.weights -i 0 -thresh 0.25\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eTrain on \u003cstrong\u003eAmazon EC2\u003c/strong\u003e, to see mAP \u0026amp; Loss-chart using URL like: \u003ccode\u003ehttp://ec2-35-160-228-91.us-west-2.compute.amazonaws.com:8090\u003c/code\u003e in the Chrome/Firefox (\u003cstrong\u003eDarknet should be compiled with OpenCV\u003c/strong\u003e):\n\u003ccode\u003e./darknet detector train cfg/coco.data yolov4.cfg yolov4.conv.137 -dont_show -mjpeg_port 8090 -map\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e186 MB Yolo9000 - image: \u003ccode\u003edarknet.exe detector test cfg/combine9k.data cfg/yolo9000.cfg yolo9000.weights\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eRemeber to put data/9k.tree and data/coco9k.map under the same folder of your app if you use the cpp api to build an app\u003c/li\u003e\n\u003cli\u003eTo process a list of images \u003ccode\u003edata/train.txt\u003c/code\u003e and save results of detection to \u003ccode\u003eresult.json\u003c/code\u003e file use:\n\u003ccode\u003edarknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output -dont_show -out result.json \u0026lt; data/train.txt\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eTo process a list of images \u003ccode\u003edata/train.txt\u003c/code\u003e and save results of detection to \u003ccode\u003eresult.txt\u003c/code\u003e use:\u003cbr\u003e\n\u003ccode\u003edarknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -dont_show -ext_output \u0026lt; data/train.txt \u0026gt; result.txt\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003ePseudo-lableing - to process a list of images \u003ccode\u003edata/new_train.txt\u003c/code\u003e and save results of detection in Yolo training format for each image as label \u003ccode\u003e\u0026lt;image_name\u0026gt;.txt\u003c/code\u003e (in this way you can increase the amount of training data) use:\n\u003ccode\u003edarknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25 -dont_show -save_labels \u0026lt; data/new_train.txt\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eTo calculate anchors: \u003ccode\u003edarknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eTo check accuracy mAP@IoU=50: \u003ccode\u003edarknet.exe detector map data/obj.data yolo-obj.cfg backup\\yolo-obj_7000.weights\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eTo check accuracy mAP@IoU=75: \u003ccode\u003edarknet.exe detector map data/obj.data yolo-obj.cfg backup\\yolo-obj_7000.weights -iou_thresh 0.75\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFor using network video-camera mjpeg-stream with any Android smartphone\u003c/h5\u003e\u003ca id=\"user-content-for-using-network-video-camera-mjpeg-stream-with-any-android-smartphone\" class=\"anchor\" aria-label=\"Permalink: For using network video-camera mjpeg-stream with any Android smartphone\" href=\"#for-using-network-video-camera-mjpeg-stream-with-any-android-smartphone\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eDownload for Android phone mjpeg-stream soft: IP Webcam / Smart WebCam\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSmart WebCam - preferably: \u003ca href=\"https://play.google.com/store/apps/details?id=com.acontech.android.SmartWebCam2\" rel=\"nofollow\"\u003ehttps://play.google.com/store/apps/details?id=com.acontech.android.SmartWebCam2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eIP Webcam: \u003ca href=\"https://play.google.com/store/apps/details?id=com.pas.webcam\" rel=\"nofollow\"\u003ehttps://play.google.com/store/apps/details?id=com.pas.webcam\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eConnect your Android phone to computer by WiFi (through a WiFi-router) or USB\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eStart Smart WebCam on your phone\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eReplace the address below, on shown in the phone application (Smart WebCam) and launch:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYolo v4 COCO-model: \u003ccode\u003edarknet.exe detector demo data/coco.data yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow to compile on Linux/macOS (using \u003ccode\u003eCMake\u003c/code\u003e)\u003c/h3\u003e\u003ca id=\"user-content-how-to-compile-on-linuxmacos-using-cmake\" class=\"anchor\" aria-label=\"Permalink: How to compile on Linux/macOS (using CMake)\" href=\"#how-to-compile-on-linuxmacos-using-cmake\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe \u003ccode\u003eCMakeLists.txt\u003c/code\u003e will attempt to find installed optional dependencies like CUDA, cudnn, ZED and build against those. It will also create a shared object library file to use \u003ccode\u003edarknet\u003c/code\u003e for code development.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eOpen a shell terminal inside the cloned repository and launch:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"./build.sh\"\u003e\u003cpre\u003e./build.sh\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow to compile on Linux (using \u003ccode\u003emake\u003c/code\u003e)\u003c/h3\u003e\u003ca id=\"user-content-how-to-compile-on-linux-using-make\" class=\"anchor\" aria-label=\"Permalink: How to compile on Linux (using make)\" href=\"#how-to-compile-on-linux-using-make\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eJust do \u003ccode\u003emake\u003c/code\u003e in the darknet directory. (You can try to compile and run it on Google Colab in cloud \u003ca href=\"https://colab.research.google.com/drive/12QusaaRj_lUwCGDvQNfICpa7kA7_a2dE\" rel=\"nofollow\"\u003elink\u003c/a\u003e (press «Open in Playground» button at the top-left corner) and watch the video \u003ca href=\"https://www.youtube.com/watch?v=mKAEGSxwOAY\" rel=\"nofollow\"\u003elink\u003c/a\u003e )\nBefore make, you can set such options in the \u003ccode\u003eMakefile\u003c/code\u003e: \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/9c1b9a2cf6363546c152251be578a21f3c3caec6/Makefile#L1\"\u003elink\u003c/a\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ccode\u003eGPU=1\u003c/code\u003e to build with CUDA to accelerate by using GPU (CUDA should be in \u003ccode\u003e/usr/local/cuda\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eCUDNN=1\u003c/code\u003e to build with cuDNN v5-v7 to accelerate training by using GPU (cuDNN should be in \u003ccode\u003e/usr/local/cudnn\u003c/code\u003e)\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eCUDNN_HALF=1\u003c/code\u003e to build for Tensor Cores (on Titan V / Tesla V100 / DGX-2 and later) speedup Detection 3x, Training 2x\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eOPENCV=1\u003c/code\u003e to build with OpenCV 4.x/3.x/2.4.x - allows to detect on video files and video streams from network cameras or web-cams\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eDEBUG=1\u003c/code\u003e to bould debug version of Yolo\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eOPENMP=1\u003c/code\u003e to build with OpenMP support to accelerate Yolo by using multi-core CPU\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eLIBSO=1\u003c/code\u003e to build a library \u003ccode\u003edarknet.so\u003c/code\u003e and binary runable file \u003ccode\u003euselib\u003c/code\u003e that uses this library. Or you can try to run so \u003ccode\u003eLD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib test.mp4\u003c/code\u003e How to use this SO-library from your own code - you can look at C++ example: \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp\"\u003ehttps://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp\u003c/a\u003e\nor use in such a way: \u003ccode\u003eLD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov4.cfg yolov4.weights test.mp4\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eZED_CAMERA=1\u003c/code\u003e to build a library with ZED-3D-camera support (should be ZED SDK installed), then run\n\u003ccode\u003eLD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov4.cfg yolov4.weights zed_camera\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eYou also need to specify for which graphics card the code is generated. This is done by setting \u003ccode\u003eARCH=\u003c/code\u003e. If you use a never version than CUDA 11 you further need to edit line 20 from Makefile and remove \u003ccode\u003e-gencode arch=compute_30,code=sm_30 \\\u003c/code\u003e as Kepler GPU support was dropped in CUDA 11. You can also drop the general \u003ccode\u003eARCH=\u003c/code\u003e and just uncomment \u003ccode\u003eARCH=\u003c/code\u003e for your graphics card.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eTo run Darknet on Linux use examples from this article, just use \u003ccode\u003e./darknet\u003c/code\u003e instead of \u003ccode\u003edarknet.exe\u003c/code\u003e, i.e. use this command: \u003ccode\u003e./darknet detector test ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow to compile on Windows (using \u003ccode\u003eCMake\u003c/code\u003e)\u003c/h3\u003e\u003ca id=\"user-content-how-to-compile-on-windows-using-cmake\" class=\"anchor\" aria-label=\"Permalink: How to compile on Windows (using CMake)\" href=\"#how-to-compile-on-windows-using-cmake\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis is the recommended approach to build Darknet on Windows.\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eInstall Visual Studio 2017 or 2019. In case you need to download it, please go here: \u003ca href=\"http://visualstudio.com\" rel=\"nofollow\"\u003eVisual Studio Community\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eInstall CUDA (at least v10.0) enabling VS Integration during installation.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eOpen Powershell (Start -\u0026gt; All programs -\u0026gt; Windows Powershell) and type these commands:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-powershell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"PS Code\\\u0026gt; git clone https://github.com/microsoft/vcpkg\nPS Code\\\u0026gt; cd vcpkg\nPS Code\\vcpkg\u0026gt; $env:VCPKG_ROOT=$PWD\nPS Code\\vcpkg\u0026gt; .\\bootstrap-vcpkg.bat\nPS Code\\vcpkg\u0026gt; .\\vcpkg install darknet[full]:x64-windows #replace with darknet[opencv-base,cuda,cudnn]:x64-windows for a quicker install of dependencies\nPS Code\\vcpkg\u0026gt; cd ..\nPS Code\\\u0026gt; git clone https://github.com/AlexeyAB/darknet\nPS Code\\\u0026gt; cd darknet\nPS Code\\darknet\u0026gt; .\\build.ps1\"\u003e\u003cpre\u003ePS Code\\\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e git clone https:\u003cspan class=\"pl-k\"\u003e//\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003egithub.com\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e/\u003c/span\u003emicrosoft\u003cspan class=\"pl-k\"\u003e/\u003c/span\u003evcpkg\nPS Code\\\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e cd vcpkg\nPS Code\\vcpkg\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e$\u003cspan class=\"pl-c1\"\u003eenv:\u003c/span\u003eVCPKG_ROOT\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e$PWD\u003c/span\u003e\nPS Code\\vcpkg\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e .\\\u003cspan class=\"pl-c1\"\u003ebootstrap-vcpkg.bat\u003c/span\u003e\nPS Code\\vcpkg\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e .\\vcpkg install darknet[\u003cspan class=\"pl-k\"\u003efull\u003c/span\u003e]:x64\u003cspan class=\"pl-k\"\u003e-\u003c/span\u003ewindows \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003ereplace with darknet[opencv-base,cuda,cudnn]:x64-windows for a quicker install of dependencies\u003c/span\u003e\nPS Code\\vcpkg\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e cd ..\nPS Code\\\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e git clone https:\u003cspan class=\"pl-k\"\u003e//\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003egithub.com\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e/\u003c/span\u003eAlexeyAB\u003cspan class=\"pl-k\"\u003e/\u003c/span\u003edarknet\nPS Code\\\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e cd darknet\nPS Code\\darknet\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e .\\build.ps1\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow to train with multi-GPU\u003c/h2\u003e\u003ca id=\"user-content-how-to-train-with-multi-gpu\" class=\"anchor\" aria-label=\"Permalink: How to train with multi-GPU\" href=\"#how-to-train-with-multi-gpu\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eTrain it first on 1 GPU for like 1000 iterations: \u003ccode\u003edarknet.exe detector train cfg/coco.data cfg/yolov4.cfg yolov4.conv.137\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eThen stop and by using partially-trained model \u003ccode\u003e/backup/yolov4_1000.weights\u003c/code\u003e run training with multigpu (up to 4 GPUs): \u003ccode\u003edarknet.exe detector train cfg/coco.data cfg/yolov4.cfg /backup/yolov4_1000.weights -gpus 0,1,2,3\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003eIf you get a Nan, then for some datasets better to decrease learning rate, for 4 GPUs set \u003ccode\u003elearning_rate = 0,00065\u003c/code\u003e (i.e. learning_rate = 0.00261 / GPUs). In this case also increase 4x times \u003ccode\u003eburn_in =\u003c/code\u003e in your cfg-file. I.e. use \u003ccode\u003eburn_in = 4000\u003c/code\u003e instead of \u003ccode\u003e1000\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ\" rel=\"nofollow\"\u003ehttps://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow to train (to detect your custom objects)\u003c/h2\u003e\u003ca id=\"user-content-how-to-train-to-detect-your-custom-objects\" class=\"anchor\" aria-label=\"Permalink: How to train (to detect your custom objects)\" href=\"#how-to-train-to-detect-your-custom-objects\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e(to train old Yolo v2 \u003ccode\u003eyolov2-voc.cfg\u003c/code\u003e, \u003ccode\u003eyolov2-tiny-voc.cfg\u003c/code\u003e, \u003ccode\u003eyolo-voc.cfg\u003c/code\u003e, \u003ccode\u003eyolo-voc.2.0.cfg\u003c/code\u003e, ... \u003ca href=\"https://github.com/AlexeyAB/darknet/tree/47c7af1cea5bbdedf1184963355e6418cb8b1b4f#how-to-train-pascal-voc-data\"\u003eclick by the link\u003c/a\u003e)\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTraining Yolo v4 (and v3):\u003c/p\u003e\n\u003col start=\"0\" dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eFor training \u003ccode\u003ecfg/yolov4-custom.cfg\u003c/code\u003e download the pre-trained weights-file (162 MB): \u003ca href=\"https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137\"\u003eyolov4.conv.137\u003c/a\u003e (Google drive mirror \u003ca href=\"https://drive.google.com/open?id=1JKF-bdIklxOOVy-2Cr5qdvjgGpmGfcbp\" rel=\"nofollow\"\u003eyolov4.conv.137\u003c/a\u003e )\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eCreate file \u003ccode\u003eyolo-obj.cfg\u003c/code\u003e with the same content as in \u003ccode\u003eyolov4-custom.cfg\u003c/code\u003e (or copy \u003ccode\u003eyolov4-custom.cfg\u003c/code\u003e to \u003ccode\u003eyolo-obj.cfg)\u003c/code\u003e and:\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003echange line batch to \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L3\"\u003e\u003ccode\u003ebatch=64\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003echange line subdivisions to \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4\"\u003e\u003ccode\u003esubdivisions=16\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003echange line max_batches to (\u003ccode\u003eclasses*2000\u003c/code\u003e but not less than number of training images, but not less than number of training images and not less than \u003ccode\u003e6000\u003c/code\u003e), f.e. \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L20\"\u003e\u003ccode\u003emax_batches=6000\u003c/code\u003e\u003c/a\u003e if you train for 3 classes\u003c/li\u003e\n\u003cli\u003echange line steps to 80% and 90% of max_batches, f.e. \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L22\"\u003e\u003ccode\u003esteps=4800,5400\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eset network size \u003ccode\u003ewidth=416 height=416\u003c/code\u003e or any value multiple of 32: \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L8-L9\"\u003ehttps://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L8-L9\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003echange line \u003ccode\u003eclasses=80\u003c/code\u003e to your number of objects in each of 3 \u003ccode\u003e[yolo]\u003c/code\u003e-layers:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L610\"\u003ehttps://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L610\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L696\"\u003ehttps://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L696\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L783\"\u003ehttps://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L783\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003echange [\u003ccode\u003efilters=255\u003c/code\u003e] to filters=(classes + 5)x3 in the 3 \u003ccode\u003e[convolutional]\u003c/code\u003e before each \u003ccode\u003e[yolo]\u003c/code\u003e layer, keep in mind that it only has to be the last \u003ccode\u003e[convolutional]\u003c/code\u003e before each of the \u003ccode\u003e[yolo]\u003c/code\u003e layers.\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L603\"\u003ehttps://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L603\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L689\"\u003ehttps://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L689\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L776\"\u003ehttps://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L776\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003ewhen using \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L608\"\u003e\u003ccode\u003e[Gaussian_yolo]\u003c/code\u003e\u003c/a\u003e layers, change [\u003ccode\u003efilters=57\u003c/code\u003e] filters=(classes + 9)x3 in the 3 \u003ccode\u003e[convolutional]\u003c/code\u003e before each \u003ccode\u003e[Gaussian_yolo]\u003c/code\u003e layer\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L604\"\u003ehttps://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L604\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L696\"\u003ehttps://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L696\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L789\"\u003ehttps://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L789\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eSo if \u003ccode\u003eclasses=1\u003c/code\u003e then should be \u003ccode\u003efilters=18\u003c/code\u003e. If \u003ccode\u003eclasses=2\u003c/code\u003e then write \u003ccode\u003efilters=21\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e(Do not write in the cfg-file: filters=(classes + 5)x3)\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e(Generally \u003ccode\u003efilters\u003c/code\u003e depends on the \u003ccode\u003eclasses\u003c/code\u003e, \u003ccode\u003ecoords\u003c/code\u003e and number of \u003ccode\u003emask\u003c/code\u003es, i.e. filters=\u003ccode\u003e(classes + coords + 1)*\u0026lt;number of mask\u0026gt;\u003c/code\u003e, where \u003ccode\u003emask\u003c/code\u003e is indices of anchors. If \u003ccode\u003emask\u003c/code\u003e is absence, then filters=\u003ccode\u003e(classes + coords + 1)*num\u003c/code\u003e)\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSo for example, for 2 objects, your file \u003ccode\u003eyolo-obj.cfg\u003c/code\u003e should differ from \u003ccode\u003eyolov4-custom.cfg\u003c/code\u003e in such lines in each of \u003cstrong\u003e3\u003c/strong\u003e [yolo]-layers:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-ini notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"[convolutional]\nfilters=21\n\n[region]\nclasses=2\"\u003e\u003cpre\u003e\u003cspan class=\"pl-en\"\u003e[convolutional]\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efilters\u003c/span\u003e=21\n\n\u003cspan class=\"pl-en\"\u003e[region]\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eclasses\u003c/span\u003e=2\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"2\" dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eCreate file \u003ccode\u003eobj.names\u003c/code\u003e in the directory \u003ccode\u003ebuild\\darknet\\x64\\data\\\u003c/code\u003e, with objects names - each in new line\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eCreate file \u003ccode\u003eobj.data\u003c/code\u003e in the directory \u003ccode\u003ebuild\\darknet\\x64\\data\\\u003c/code\u003e, containing (where \u003cstrong\u003eclasses = number of objects\u003c/strong\u003e):\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-ini notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"classes = 2\ntrain = data/train.txt\nvalid = data/test.txt\nnames = data/obj.names\nbackup = backup/\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eclasses\u003c/span\u003e = 2\n\u003cspan class=\"pl-k\"\u003etrain\u003c/span\u003e = data/train.txt\n\u003cspan class=\"pl-k\"\u003evalid\u003c/span\u003e = data/test.txt\n\u003cspan class=\"pl-k\"\u003enames\u003c/span\u003e = data/obj.names\n\u003cspan class=\"pl-k\"\u003ebackup\u003c/span\u003e = backup/\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"4\" dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003ePut image-files (.jpg) of your objects in the directory \u003ccode\u003ebuild\\darknet\\x64\\data\\obj\\\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eYou should label each object on images from your dataset. Use this visual GUI-software for marking bounded boxes of objects and generating annotation files for Yolo v2 \u0026amp; v3: \u003ca href=\"https://github.com/AlexeyAB/Yolo_mark\"\u003ehttps://github.com/AlexeyAB/Yolo_mark\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003eIt will create \u003ccode\u003e.txt\u003c/code\u003e-file for each \u003ccode\u003e.jpg\u003c/code\u003e-image-file - in the same directory and with the same name, but with \u003ccode\u003e.txt\u003c/code\u003e-extension, and put to file: object number and object coordinates on this image, for each object in new line:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003e\u0026lt;object-class\u0026gt; \u0026lt;x_center\u0026gt; \u0026lt;y_center\u0026gt; \u0026lt;width\u0026gt; \u0026lt;height\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWhere:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ccode\u003e\u0026lt;object-class\u0026gt;\u003c/code\u003e - integer object number from \u003ccode\u003e0\u003c/code\u003e to \u003ccode\u003e(classes-1)\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003e\u0026lt;x_center\u0026gt; \u0026lt;y_center\u0026gt; \u0026lt;width\u0026gt; \u0026lt;height\u0026gt;\u003c/code\u003e - float values \u003cstrong\u003erelative\u003c/strong\u003e to width and height of image, it can be equal from \u003ccode\u003e(0.0 to 1.0]\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003efor example: \u003ccode\u003e\u0026lt;x\u0026gt; = \u0026lt;absolute_x\u0026gt; / \u0026lt;image_width\u0026gt;\u003c/code\u003e or \u003ccode\u003e\u0026lt;height\u0026gt; = \u0026lt;absolute_height\u0026gt; / \u0026lt;image_height\u0026gt;\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eatention: \u003ccode\u003e\u0026lt;x_center\u0026gt; \u0026lt;y_center\u0026gt;\u003c/code\u003e - are center of rectangle (are not top-left corner)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eFor example for \u003ccode\u003eimg1.jpg\u003c/code\u003e you will be created \u003ccode\u003eimg1.txt\u003c/code\u003e containing:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"1 0.716797 0.395833 0.216406 0.147222\n0 0.687109 0.379167 0.255469 0.158333\n1 0.420312 0.395833 0.140625 0.166667\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e1 0.716797 0.395833 0.216406 0.147222\n0 0.687109 0.379167 0.255469 0.158333\n1 0.420312 0.395833 0.140625 0.166667\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"6\" dir=\"auto\"\u003e\n\u003cli\u003eCreate file \u003ccode\u003etrain.txt\u003c/code\u003e in directory \u003ccode\u003ebuild\\darknet\\x64\\data\\\u003c/code\u003e, with filenames of your images, each filename in new line, with path relative to \u003ccode\u003edarknet.exe\u003c/code\u003e, for example containing:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"data/obj/img1.jpg\ndata/obj/img2.jpg\ndata/obj/img3.jpg\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003edata/obj/img1.jpg\ndata/obj/img2.jpg\ndata/obj/img3.jpg\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"7\" dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eDownload pre-trained weights for the convolutional layers and put to the directory \u003ccode\u003ebuild\\darknet\\x64\u003c/code\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003efor \u003ccode\u003eyolov4.cfg\u003c/code\u003e, \u003ccode\u003eyolov4-custom.cfg\u003c/code\u003e (162 MB): \u003ca href=\"https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137\"\u003eyolov4.conv.137\u003c/a\u003e (Google drive mirror \u003ca href=\"https://drive.google.com/open?id=1JKF-bdIklxOOVy-2Cr5qdvjgGpmGfcbp\" rel=\"nofollow\"\u003eyolov4.conv.137\u003c/a\u003e )\u003c/li\u003e\n\u003cli\u003efor \u003ccode\u003eyolov4-tiny.cfg\u003c/code\u003e, \u003ccode\u003eyolov4-tiny-3l.cfg\u003c/code\u003e, \u003ccode\u003eyolov4-tiny-custom.cfg\u003c/code\u003e (19 MB): \u003ca href=\"https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29\"\u003eyolov4-tiny.conv.29\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003efor \u003ccode\u003ecsresnext50-panet-spp.cfg\u003c/code\u003e (133 MB): \u003ca href=\"https://drive.google.com/file/d/16yMYCLQTY_oDlCIZPfn_sab6KD3zgzGq/view?usp=sharing\" rel=\"nofollow\"\u003ecsresnext50-panet-spp.conv.112\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003efor \u003ccode\u003eyolov3.cfg, yolov3-spp.cfg\u003c/code\u003e (154 MB): \u003ca href=\"https://pjreddie.com/media/files/darknet53.conv.74\" rel=\"nofollow\"\u003edarknet53.conv.74\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003efor \u003ccode\u003eyolov3-tiny-prn.cfg , yolov3-tiny.cfg\u003c/code\u003e (6 MB): \u003ca href=\"https://drive.google.com/file/d/18v36esoXCh-PsOKwyP2GWrpYDptDY8Zf/view?usp=sharing\" rel=\"nofollow\"\u003eyolov3-tiny.conv.11\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003efor \u003ccode\u003eenet-coco.cfg (EfficientNetB0-Yolov3)\u003c/code\u003e (14 MB): \u003ca href=\"https://drive.google.com/file/d/1uhh3D6RSn0ekgmsaTcl-ZW53WBaUDo6j/view?usp=sharing\" rel=\"nofollow\"\u003eenetb0-coco.conv.132\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eStart training by using the command line: \u003ccode\u003edarknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo train on Linux use command: \u003ccode\u003e./darknet detector train data/obj.data yolo-obj.cfg yolov4.conv.137\u003c/code\u003e (just use \u003ccode\u003e./darknet\u003c/code\u003e instead of \u003ccode\u003edarknet.exe\u003c/code\u003e)\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e(file \u003ccode\u003eyolo-obj_last.weights\u003c/code\u003e will be saved to the \u003ccode\u003ebuild\\darknet\\x64\\backup\\\u003c/code\u003e for each 100 iterations)\u003c/li\u003e\n\u003cli\u003e(file \u003ccode\u003eyolo-obj_xxxx.weights\u003c/code\u003e will be saved to the \u003ccode\u003ebuild\\darknet\\x64\\backup\\\u003c/code\u003e for each 1000 iterations)\u003c/li\u003e\n\u003cli\u003e(to disable Loss-Window use \u003ccode\u003edarknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -dont_show\u003c/code\u003e, if you train on computer without monitor like a cloud Amazon EC2)\u003c/li\u003e\n\u003cli\u003e(to see the mAP \u0026amp; Loss-chart during training on remote server without GUI, use command \u003ccode\u003edarknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -dont_show -mjpeg_port 8090 -map\u003c/code\u003e then open URL \u003ccode\u003ehttp://ip-address:8090\u003c/code\u003e in Chrome/Firefox browser)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003e8.1. For training with mAP (mean average precisions) calculation for each 4 Epochs (set \u003ccode\u003evalid=valid.txt\u003c/code\u003e or \u003ccode\u003etrain.txt\u003c/code\u003e in \u003ccode\u003eobj.data\u003c/code\u003e file) and run: \u003ccode\u003edarknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -map\u003c/code\u003e\u003c/p\u003e\n\u003col start=\"9\" dir=\"auto\"\u003e\n\u003cli\u003eAfter training is complete - get result \u003ccode\u003eyolo-obj_final.weights\u003c/code\u003e from path \u003ccode\u003ebuild\\darknet\\x64\\backup\\\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eAfter each 100 iterations you can stop and later start training from this point. For example, after 2000 iterations you can stop training, and later just start training using: \u003ccode\u003edarknet.exe detector train data/obj.data yolo-obj.cfg backup\\yolo-obj_2000.weights\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e(in the original repository \u003ca href=\"https://github.com/pjreddie/darknet\"\u003ehttps://github.com/pjreddie/darknet\u003c/a\u003e the weights-file is saved only once every 10 000 iterations \u003ccode\u003eif(iterations \u0026gt; 1000)\u003c/code\u003e)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eAlso you can get result earlier than all 45000 iterations.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNote:\u003c/strong\u003e If during training you see \u003ccode\u003enan\u003c/code\u003e values for \u003ccode\u003eavg\u003c/code\u003e (loss) field - then training goes wrong, but if \u003ccode\u003enan\u003c/code\u003e is in some other lines - then training goes well.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNote:\u003c/strong\u003e If you changed width= or height= in your cfg-file, then new width and height must be divisible by 32.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNote:\u003c/strong\u003e After training use such command for detection: \u003ccode\u003edarknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNote:\u003c/strong\u003e if error \u003ccode\u003eOut of memory\u003c/code\u003e occurs then in \u003ccode\u003e.cfg\u003c/code\u003e-file you should increase \u003ccode\u003esubdivisions=16\u003c/code\u003e, 32 or 64: \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4\"\u003elink\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow to train tiny-yolo (to detect your custom objects):\u003c/h3\u003e\u003ca id=\"user-content-how-to-train-tiny-yolo-to-detect-your-custom-objects\" class=\"anchor\" aria-label=\"Permalink: How to train tiny-yolo (to detect your custom objects):\" href=\"#how-to-train-tiny-yolo-to-detect-your-custom-objects\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eDo all the same steps as for the full yolo model as described above. With the exception of:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eDownload file with the first 29-convolutional layers of yolov4-tiny: \u003ca href=\"https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29\"\u003ehttps://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29\u003c/a\u003e\n(Or get this file from yolov4-tiny.weights file by using command: \u003ccode\u003edarknet.exe partial cfg/yolov4-tiny-custom.cfg yolov4-tiny.weights yolov4-tiny.conv.29 29\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eMake your custom model \u003ccode\u003eyolov4-tiny-obj.cfg\u003c/code\u003e based on \u003ccode\u003ecfg/yolov4-tiny-custom.cfg\u003c/code\u003e instead of \u003ccode\u003eyolov4.cfg\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eStart training: \u003ccode\u003edarknet.exe detector train data/obj.data yolov4-tiny-obj.cfg yolov4-tiny.conv.29\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eFor training Yolo based on other models (\u003ca href=\"https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/densenet201_yolo.cfg\"\u003eDenseNet201-Yolo\u003c/a\u003e or \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/resnet50_yolo.cfg\"\u003eResNet50-Yolo\u003c/a\u003e), you can download and get pre-trained weights as showed in this file: \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/partial.cmd\"\u003ehttps://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/partial.cmd\u003c/a\u003e\nIf you made you custom model that isn't based on other models, then you can train it without pre-trained weights, then will be used random initial weights.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWhen should I stop training:\u003c/h2\u003e\u003ca id=\"user-content-when-should-i-stop-training\" class=\"anchor\" aria-label=\"Permalink: When should I stop training:\" href=\"#when-should-i-stop-training\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eUsually sufficient 2000 iterations for each class(object), but not less than number of training images and not less than 6000 iterations in total. But for a more precise definition when you should stop training, use the following manual:\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003eDuring training, you will see varying indicators of error, and you should stop when no longer decreases \u003cstrong\u003e0.XXXXXXX avg\u003c/strong\u003e:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eRegion Avg IOU: 0.798363, Class: 0.893232, Obj: 0.700808, No Obj: 0.004567, Avg Recall: 1.000000, count: 8\nRegion Avg IOU: 0.800677, Class: 0.892181, Obj: 0.701590, No Obj: 0.004574, Avg Recall: 1.000000, count: 8\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e9002\u003c/strong\u003e: 0.211667, \u003cstrong\u003e0.60730 avg\u003c/strong\u003e, 0.001000 rate, 3.868000 seconds, 576128 images\nLoaded: 0.000000 seconds\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003e9002\u003c/strong\u003e - iteration number (number of batch)\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e0.60730 avg\u003c/strong\u003e - average loss (error) - \u003cstrong\u003ethe lower, the better\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eWhen you see that average loss \u003cstrong\u003e0.xxxxxx avg\u003c/strong\u003e no longer decreases at many iterations then you should stop training. The final avgerage loss can be from \u003ccode\u003e0.05\u003c/code\u003e (for a small model and easy dataset) to \u003ccode\u003e3.0\u003c/code\u003e (for a big model and a difficult dataset).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eOr if you train with flag \u003ccode\u003e-map\u003c/code\u003e then you will see mAP indicator \u003ccode\u003eLast accuracy mAP@0.5 = 18.50%\u003c/code\u003e in the console - this indicator is better than Loss, so train while mAP increases.\u003c/p\u003e\n\u003col start=\"2\" dir=\"auto\"\u003e\n\u003cli\u003eOnce training is stopped, you should take some of last \u003ccode\u003e.weights\u003c/code\u003e-files from \u003ccode\u003edarknet\\build\\darknet\\x64\\backup\u003c/code\u003e and choose the best of them:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003eFor example, you stopped training after 9000 iterations, but the best result can give one of previous weights (7000, 8000, 9000). It can happen due to overfitting. \u003cstrong\u003eOverfitting\u003c/strong\u003e - is case when you can detect objects on images from training-dataset, but can't detect objects on any others images. You should get weights from \u003cstrong\u003eEarly Stopping Point\u003c/strong\u003e:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7d0a8add32568ac1d03f424be135f05ed3dc6616c1680bb26cb808a95a940377/68747470733a2f2f6873746f2e6f72672f66696c65732f3564632f3761652f3766612f35646337616537666164396434653365623361343834633538626663316666352e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7d0a8add32568ac1d03f424be135f05ed3dc6616c1680bb26cb808a95a940377/68747470733a2f2f6873746f2e6f72672f66696c65732f3564632f3761652f3766612f35646337616537666164396434653365623361343834633538626663316666352e706e67\" alt=\"Overfitting\" data-canonical-src=\"https://hsto.org/files/5dc/7ae/7fa/5dc7ae7fad9d4e3eb3a484c58bfc1ff5.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo get weights from Early Stopping Point:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e2.1. At first, in your file \u003ccode\u003eobj.data\u003c/code\u003e you must specify the path to the validation dataset \u003ccode\u003evalid = valid.txt\u003c/code\u003e (format of \u003ccode\u003evalid.txt\u003c/code\u003e as in \u003ccode\u003etrain.txt\u003c/code\u003e), and if you haven't validation images, just copy \u003ccode\u003edata\\train.txt\u003c/code\u003e to \u003ccode\u003edata\\valid.txt\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e2.2 If training is stopped after 9000 iterations, to validate some of previous weights use this commands:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e(If you use another GitHub repository, then use \u003ccode\u003edarknet.exe detector recall\u003c/code\u003e... instead of \u003ccode\u003edarknet.exe detector map\u003c/code\u003e...)\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ccode\u003edarknet.exe detector map data/obj.data yolo-obj.cfg backup\\yolo-obj_7000.weights\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003edarknet.exe detector map data/obj.data yolo-obj.cfg backup\\yolo-obj_8000.weights\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003edarknet.exe detector map data/obj.data yolo-obj.cfg backup\\yolo-obj_9000.weights\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eAnd comapre last output lines for each weights (7000, 8000, 9000):\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eChoose weights-file \u003cstrong\u003ewith the highest mAP (mean average precision)\u003c/strong\u003e or IoU (intersect over union)\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor example, \u003cstrong\u003ebigger mAP\u003c/strong\u003e gives weights \u003ccode\u003eyolo-obj_8000.weights\u003c/code\u003e - then \u003cstrong\u003euse this weights for detection\u003c/strong\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eOr just train with \u003ccode\u003e-map\u003c/code\u003e flag:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003edarknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -map\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSo you will see mAP-chart (red-line) in the Loss-chart Window. mAP will be calculated for each 4 Epochs using \u003ccode\u003evalid=valid.txt\u003c/code\u003e file that is specified in \u003ccode\u003eobj.data\u003c/code\u003e file (\u003ccode\u003e1 Epoch = images_in_train_txt / batch\u003c/code\u003e iterations)\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e(to change the max x-axis value - change \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L20\"\u003e\u003ccode\u003emax_batches=\u003c/code\u003e\u003c/a\u003e parameter to \u003ccode\u003e2000*classes\u003c/code\u003e, f.e. \u003ccode\u003emax_batches=6000\u003c/code\u003e for 3 classes)\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/cbb13e27b93952d5bb294eaaf5fc078fc54082f5882bf19d08b181affdb8555a/68747470733a2f2f6873746f2e6f72672f776562742f79642f766c2f61672f7964766c616775746f66327a636e6a6f64737467726f656e3861632e6a706567\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cbb13e27b93952d5bb294eaaf5fc078fc54082f5882bf19d08b181affdb8555a/68747470733a2f2f6873746f2e6f72672f776562742f79642f766c2f61672f7964766c616775746f66327a636e6a6f64737467726f656e3861632e6a706567\" alt=\"loss_chart_map_chart\" data-canonical-src=\"https://hsto.org/webt/yd/vl/ag/ydvlagutof2zcnjodstgroen8ac.jpeg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eExample of custom object detection: \u003ccode\u003edarknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights\u003c/code\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eIoU\u003c/strong\u003e (intersect over union) - average instersect over union of objects and detections for a certain threshold = 0.24\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003emAP\u003c/strong\u003e (mean average precision) - mean value of \u003ccode\u003eaverage precisions\u003c/code\u003e for each class, where \u003ccode\u003eaverage precision\u003c/code\u003e is average value of 11 points on PR-curve for each possible threshold (each probability of detection) for the same class (Precision-Recall in terms of PascalVOC, where Precision=TP/(TP+FP) and Recall=TP/(TP+FN) ), page-11: \u003ca href=\"http://homepages.inf.ed.ac.uk/ckiw/postscript/ijcv_voc09.pdf\" rel=\"nofollow\"\u003ehttp://homepages.inf.ed.ac.uk/ckiw/postscript/ijcv_voc09.pdf\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003emAP\u003c/strong\u003e is default metric of precision in the PascalVOC competition, \u003cstrong\u003ethis is the same as AP50\u003c/strong\u003e metric in the MS COCO competition.\nIn terms of Wiki, indicators Precision and Recall have a slightly different meaning than in the PascalVOC competition, but \u003cstrong\u003eIoU always has the same meaning\u003c/strong\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/c0aeed32ee93ce4ad00cadb1aee8a38cdf94a5e3a8156d73fba735731acb4685/68747470733a2f2f6873746f2e6f72672f66696c65732f6361382f3836362f6437362f63613838363664373666623834303232383934306462663434326137663036612e6a7067\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c0aeed32ee93ce4ad00cadb1aee8a38cdf94a5e3a8156d73fba735731acb4685/68747470733a2f2f6873746f2e6f72672f66696c65732f6361382f3836362f6437362f63613838363664373666623834303232383934306462663434326137663036612e6a7067\" alt=\"precision_recall_iou\" data-canonical-src=\"https://hsto.org/files/ca8/866/d76/ca8866d76fb840228940dbf442a7f06a.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCustom object detection:\u003c/h3\u003e\u003ca id=\"user-content-custom-object-detection\" class=\"anchor\" aria-label=\"Permalink: Custom object detection:\" href=\"#custom-object-detection\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eExample of custom object detection: \u003ccode\u003edarknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights\u003c/code\u003e\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/fcf258e7152f8cd43fb21d3d48b035c706d4d9ab1e68863407bcf1993b13986e/68747470733a2f2f6873746f2e6f72672f66696c65732f6431322f3165372f3531352f64313231653735313566366134656236393439313366313064653566326236312e6a7067\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fcf258e7152f8cd43fb21d3d48b035c706d4d9ab1e68863407bcf1993b13986e/68747470733a2f2f6873746f2e6f72672f66696c65732f6431322f3165372f3531352f64313231653735313566366134656236393439313366313064653566326236312e6a7067\" alt=\"Yolo_v2_training\" data-canonical-src=\"https://hsto.org/files/d12/1e7/515/d121e7515f6a4eb694913f10de5f2b61.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/d717c6a3bfa2de3a74059e81925da710b4bdfc5bb6fc33120a03abcf22fdfa93/68747470733a2f2f6873746f2e6f72672f66696c65732f3732372f6337652f3565392f37323763376535653939626634643461613334303237626236613565346261622e6a7067\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/d717c6a3bfa2de3a74059e81925da710b4bdfc5bb6fc33120a03abcf22fdfa93/68747470733a2f2f6873746f2e6f72672f66696c65732f3732372f6337652f3565392f37323763376535653939626634643461613334303237626236613565346261622e6a7067\" alt=\"Yolo_v2_training\" data-canonical-src=\"https://hsto.org/files/727/c7e/5e9/727c7e5e99bf4d4aa34027bb6a5e4bab.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow to improve object detection:\u003c/h2\u003e\u003ca id=\"user-content-how-to-improve-object-detection\" class=\"anchor\" aria-label=\"Permalink: How to improve object detection:\" href=\"#how-to-improve-object-detection\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003eBefore training:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eset flag \u003ccode\u003erandom=1\u003c/code\u003e in your \u003ccode\u003e.cfg\u003c/code\u003e-file - it will increase precision by training Yolo for different resolutions: \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L788\"\u003elink\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eincrease network resolution in your \u003ccode\u003e.cfg\u003c/code\u003e-file (\u003ccode\u003eheight=608\u003c/code\u003e, \u003ccode\u003ewidth=608\u003c/code\u003e or any value multiple of 32) - it will increase precision\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003echeck that each object that you want to detect is mandatory labeled in your dataset - no one object in your data set should not be without label. In the most training issues - there are wrong labels in your dataset (got labels by using some conversion script, marked with a third-party tool, ...). Always check your dataset by using: \u003ca href=\"https://github.com/AlexeyAB/Yolo_mark\"\u003ehttps://github.com/AlexeyAB/Yolo_mark\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003emy Loss is very high and mAP is very low, is training wrong? Run training with \u003ccode\u003e -show_imgs\u003c/code\u003e flag at the end of training command, do you see correct bounded boxes of objects (in windows or in files \u003ccode\u003eaug_...jpg\u003c/code\u003e)? If no - your training dataset is wrong.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003efor each object which you want to detect - there must be at least 1 similar object in the Training dataset with about the same: shape, side of object, relative size, angle of rotation, tilt, illumination. So desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 different images for each class or more, and you should train \u003ccode\u003e2000*classes\u003c/code\u003e iterations or more\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003edesirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box (empty \u003ccode\u003e.txt\u003c/code\u003e files) - use as many images of negative samples as there are images with objects\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eWhat is the best way to mark objects: label only the visible part of the object, or label the visible and overlapped part of the object, or label a little more than the entire object (with a little gap)? Mark as you like - how would you like it to be detected.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003efor training with a large number of objects in each image, add the parameter \u003ccode\u003emax=200\u003c/code\u003e or higher value in the last \u003ccode\u003e[yolo]\u003c/code\u003e-layer or \u003ccode\u003e[region]\u003c/code\u003e-layer in your cfg-file (the global maximum number of objects that can be detected by YoloV3 is \u003ccode\u003e0,0615234375*(width*height)\u003c/code\u003e where are width and height are parameters from \u003ccode\u003e[net]\u003c/code\u003e section in cfg-file)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003efor training for small objects (smaller than 16x16 after the image is resized to 416x416) - set \u003ccode\u003elayers = 23\u003c/code\u003e instead of \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L895\"\u003ehttps://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L895\u003c/a\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eset \u003ccode\u003estride=4\u003c/code\u003e instead of \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L892\"\u003ehttps://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L892\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eset \u003ccode\u003estride=4\u003c/code\u003e instead of \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L989\"\u003ehttps://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L989\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003efor training for both small and large objects use modified models:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFull-model: 5 yolo layers: \u003ca href=\"https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3_5l.cfg\" rel=\"nofollow\"\u003ehttps://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3_5l.cfg\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eTiny-model: 3 yolo layers: \u003ca href=\"https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny_3l.cfg\" rel=\"nofollow\"\u003ehttps://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny_3l.cfg\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eYOLOv4: 3 yolo layers: \u003ca href=\"https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-custom.cfg\" rel=\"nofollow\"\u003ehttps://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-custom.cfg\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eIf you train the model to distinguish Left and Right objects as separate classes (left/right hand, left/right-turn on road signs, ...) then for disabling flip data augmentation - add \u003ccode\u003eflip=0\u003c/code\u003e here: \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/3d2d0a7c98dbc8923d9ff705b81ff4f7940ea6ff/cfg/yolov3.cfg#L17\"\u003ehttps://github.com/AlexeyAB/darknet/blob/3d2d0a7c98dbc8923d9ff705b81ff4f7940ea6ff/cfg/yolov3.cfg#L17\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eGeneral rule - your training dataset should include such a set of relative sizes of objects that you want to detect:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ccode\u003etrain_network_width * train_obj_width / train_image_width ~= detection_network_width * detection_obj_width / detection_image_width\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003etrain_network_height * train_obj_height / train_image_height ~= detection_network_height * detection_obj_height / detection_image_height\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eI.e. for each object from Test dataset there must be at least 1 object in the Training dataset with the same class_id and about the same relative size:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eobject width in percent from Training dataset\u003c/code\u003e ~= \u003ccode\u003eobject width in percent from Test dataset\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThat is, if only objects that occupied 80-90% of the image were present in the training set, then the trained network will not be able to detect objects that occupy 1-10% of the image.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eto speedup training (with decreasing detection accuracy) set param \u003ccode\u003estopbackward=1\u003c/code\u003e for layer-136 in cfg-file\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eeach: \u003ccode\u003emodel of object, side, illimination, scale, each 30 grad\u003c/code\u003e of the turn and inclination angles - these are \u003cem\u003edifferent objects\u003c/em\u003e from an internal perspective of the neural network. So the more \u003cem\u003edifferent objects\u003c/em\u003e you want to detect, the more complex network model should be used.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eto make the detected bounded boxes more accurate, you can add 3 parameters \u003ccode\u003eignore_thresh = .9 iou_normalizer=0.5 iou_loss=giou\u003c/code\u003e to each \u003ccode\u003e[yolo]\u003c/code\u003e layer and train, it will increase mAP@0.9, but decrease mAP@0.5.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eOnly if you are an \u003cstrong\u003eexpert\u003c/strong\u003e in neural detection networks - recalculate anchors for your dataset for \u003ccode\u003ewidth\u003c/code\u003e and \u003ccode\u003eheight\u003c/code\u003e from cfg-file:\n\u003ccode\u003edarknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416\u003c/code\u003e\nthen set the same 9 \u003ccode\u003eanchors\u003c/code\u003e in each of 3 \u003ccode\u003e[yolo]\u003c/code\u003e-layers in your cfg-file. But you should change indexes of anchors \u003ccode\u003emasks=\u003c/code\u003e for each [yolo]-layer, so for YOLOv4 the 1st-[yolo]-layer has anchors smaller than 30x30, 2nd smaller than 60x60, 3rd remaining, and vice versa for YOLOv3. Also you should change the \u003ccode\u003efilters=(classes + 5)*\u0026lt;number of mask\u0026gt;\u003c/code\u003e before each [yolo]-layer. If many of the calculated anchors do not fit under the appropriate layers - then just try using all the default anchors.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"2\" dir=\"auto\"\u003e\n\u003cli\u003eAfter training - for detection:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eIncrease network-resolution by set in your \u003ccode\u003e.cfg\u003c/code\u003e-file (\u003ccode\u003eheight=608\u003c/code\u003e and \u003ccode\u003ewidth=608\u003c/code\u003e) or (\u003ccode\u003eheight=832\u003c/code\u003e and \u003ccode\u003ewidth=832\u003c/code\u003e) or (any value multiple of 32) - this increases the precision and makes it possible to detect small objects: \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L8-L9\"\u003elink\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eit is not necessary to train the network again, just use \u003ccode\u003e.weights\u003c/code\u003e-file already trained for 416x416 resolution\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eto get even greater accuracy you should train with higher resolution 608x608 or 832x832, note: if error \u003ccode\u003eOut of memory\u003c/code\u003e occurs then in \u003ccode\u003e.cfg\u003c/code\u003e-file you should increase \u003ccode\u003esubdivisions=16\u003c/code\u003e, 32 or 64: \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4\"\u003elink\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow to mark bounded boxes of objects and create annotation files:\u003c/h2\u003e\u003ca id=\"user-content-how-to-mark-bounded-boxes-of-objects-and-create-annotation-files\" class=\"anchor\" aria-label=\"Permalink: How to mark bounded boxes of objects and create annotation files:\" href=\"#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eHere you can find repository with GUI-software for marking bounded boxes of objects and generating annotation files for Yolo v2 - v4: \u003ca href=\"https://github.com/AlexeyAB/Yolo_mark\"\u003ehttps://github.com/AlexeyAB/Yolo_mark\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWith example of: \u003ccode\u003etrain.txt\u003c/code\u003e, \u003ccode\u003eobj.names\u003c/code\u003e, \u003ccode\u003eobj.data\u003c/code\u003e, \u003ccode\u003eyolo-obj.cfg\u003c/code\u003e, \u003ccode\u003eair\u003c/code\u003e1-6\u003ccode\u003e.txt\u003c/code\u003e, \u003ccode\u003ebird\u003c/code\u003e1-4\u003ccode\u003e.txt\u003c/code\u003e for 2 classes of objects (air, bird) and \u003ccode\u003etrain_obj.cmd\u003c/code\u003e with example how to train this image-set with Yolo v2 - v4\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eDifferent tools for marking objects in images:\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003ein C++: \u003ca href=\"https://github.com/AlexeyAB/Yolo_mark\"\u003ehttps://github.com/AlexeyAB/Yolo_mark\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ein Python: \u003ca href=\"https://github.com/tzutalin/labelImg\"\u003ehttps://github.com/tzutalin/labelImg\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ein Python: \u003ca href=\"https://github.com/Cartucho/OpenLabeling\"\u003ehttps://github.com/Cartucho/OpenLabeling\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ein C++: \u003ca href=\"https://www.ccoderun.ca/darkmark/\" rel=\"nofollow\"\u003ehttps://www.ccoderun.ca/darkmark/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ein JavaScript: \u003ca href=\"https://github.com/opencv/cvat\"\u003ehttps://github.com/opencv/cvat\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ein C++: \u003ca href=\"https://github.com/jveitchmichaelis/deeplabel\"\u003ehttps://github.com/jveitchmichaelis/deeplabel\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ein C#: \u003ca href=\"https://github.com/BMW-InnovationLab/BMW-Labeltool-Lite\"\u003ehttps://github.com/BMW-InnovationLab/BMW-Labeltool-Lite\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eDL-Annotator for Windows ($30): \u003ca href=\"https://www.microsoft.com/en-us/p/dlannotator/9nsx79m7t8fn?activetab=pivot:overviewtab\" rel=\"nofollow\"\u003eurl\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ev7labs - the greatest cloud labeling tool ($1.5 per hour): \u003ca href=\"https://www.v7labs.com/\" rel=\"nofollow\"\u003ehttps://www.v7labs.com/\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow to use Yolo as DLL and SO libraries\u003c/h2\u003e\u003ca id=\"user-content-how-to-use-yolo-as-dll-and-so-libraries\" class=\"anchor\" aria-label=\"Permalink: How to use Yolo as DLL and SO libraries\" href=\"#how-to-use-yolo-as-dll-and-so-libraries\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eon Linux\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eusing \u003ccode\u003ebuild.sh\u003c/code\u003e or\u003c/li\u003e\n\u003cli\u003ebuild \u003ccode\u003edarknet\u003c/code\u003e using \u003ccode\u003ecmake\u003c/code\u003e or\u003c/li\u003e\n\u003cli\u003eset \u003ccode\u003eLIBSO=1\u003c/code\u003e in the \u003ccode\u003eMakefile\u003c/code\u003e and do \u003ccode\u003emake\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eon Windows\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eusing \u003ccode\u003ebuild.ps1\u003c/code\u003e or\u003c/li\u003e\n\u003cli\u003ebuild \u003ccode\u003edarknet\u003c/code\u003e using \u003ccode\u003ecmake\u003c/code\u003e or\u003c/li\u003e\n\u003cli\u003ecompile \u003ccode\u003ebuild\\darknet\\yolo_cpp_dll.sln\u003c/code\u003e solution or \u003ccode\u003ebuild\\darknet\\yolo_cpp_dll_no_gpu.sln\u003c/code\u003e solution\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eThere are 2 APIs:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eC API: \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/master/include/darknet.h\"\u003ehttps://github.com/AlexeyAB/darknet/blob/master/include/darknet.h\u003c/a\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePython examples using the C API:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/AlexeyAB/darknet/blob/master/darknet.py\"\u003ehttps://github.com/AlexeyAB/darknet/blob/master/darknet.py\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/AlexeyAB/darknet/blob/master/darknet_video.py\"\u003ehttps://github.com/AlexeyAB/darknet/blob/master/darknet_video.py\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eC++ API: \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/master/include/yolo_v2_class.hpp\"\u003ehttps://github.com/AlexeyAB/darknet/blob/master/include/yolo_v2_class.hpp\u003c/a\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eC++ example that uses C++ API: \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp\"\u003ehttps://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eTo compile Yolo as C++ DLL-file \u003ccode\u003eyolo_cpp_dll.dll\u003c/code\u003e - open the solution \u003ccode\u003ebuild\\darknet\\yolo_cpp_dll.sln\u003c/code\u003e, set \u003cstrong\u003ex64\u003c/strong\u003e and \u003cstrong\u003eRelease\u003c/strong\u003e, and do the: Build -\u0026gt; Build yolo_cpp_dll\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYou should have installed \u003cstrong\u003eCUDA 10.0\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003eTo use cuDNN do: (right click on project) -\u0026gt; properties -\u0026gt; C/C++ -\u0026gt; Preprocessor -\u0026gt; Preprocessor Definitions, and add at the beginning of line: \u003ccode\u003eCUDNN;\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eTo use Yolo as DLL-file in your C++ console application - open the solution \u003ccode\u003ebuild\\darknet\\yolo_console_dll.sln\u003c/code\u003e, set \u003cstrong\u003ex64\u003c/strong\u003e and \u003cstrong\u003eRelease\u003c/strong\u003e, and do the: Build -\u0026gt; Build yolo_console_dll\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eyou can run your console application from Windows Explorer \u003ccode\u003ebuild\\darknet\\x64\\yolo_console_dll.exe\u003c/code\u003e\n\u003cstrong\u003euse this command\u003c/strong\u003e: \u003ccode\u003eyolo_console_dll.exe data/coco.names yolov4.cfg yolov4.weights test.mp4\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eafter launching your console application and entering the image file name - you will see info for each object:\n\u003ccode\u003e\u0026lt;obj_id\u0026gt; \u0026lt;left_x\u0026gt; \u0026lt;top_y\u0026gt; \u0026lt;width\u0026gt; \u0026lt;height\u0026gt; \u0026lt;probability\u0026gt;\u003c/code\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eto use simple OpenCV-GUI you should uncomment line \u003ccode\u003e//#define OPENCV\u003c/code\u003e in \u003ccode\u003eyolo_console_dll.cpp\u003c/code\u003e-file: \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/a6cbaeecde40f91ddc3ea09aa26a03ab5bbf8ba8/src/yolo_console_dll.cpp#L5\"\u003elink\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eyou can see source code of simple example for detection on the video file: \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/ab1c5f9e57b4175f29a6ef39e7e68987d3e98704/src/yolo_console_dll.cpp#L75\"\u003elink\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eyolo_cpp_dll.dll\u003c/code\u003e-API: \u003ca href=\"https://github.com/AlexeyAB/darknet/blob/master/src/yolo_v2_class.hpp#L42\"\u003elink\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-c++ notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"struct bbox_t {\n unsigned int x, y, w, h; // (x,y) - top-left corner, (w, h) - width \u0026amp; height of bounded box\n float prob; // confidence - probability that the object was found correctly\n unsigned int obj_id; // class of object - from range [0, classes-1]\n unsigned int track_id; // tracking id for video (0 - untracked, 1 - inf - tracked object)\n unsigned int frames_counter;// counter of frames on which the object was detected\n};\n\nclass Detector {\npublic:\n Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0);\n ~Detector();\n\n std::vector\u0026lt;bbox_t\u0026gt; detect(std::string image_filename, float thresh = 0.2, bool use_mean = false);\n std::vector\u0026lt;bbox_t\u0026gt; detect(image_t img, float thresh = 0.2, bool use_mean = false);\n static image_t load_image(std::string image_filename);\n static void free_image(image_t m);\n\n#ifdef OPENCV\n std::vector\u0026lt;bbox_t\u0026gt; detect(cv::Mat mat, float thresh = 0.2, bool use_mean = false);\n std::shared_ptr\u0026lt;image_t\u0026gt; mat_to_image_resize(cv::Mat mat) const;\n#endif\n};\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003estruct\u003c/span\u003e \u003cspan class=\"pl-en\"\u003ebbox_t\u003c/span\u003e {\n \u003cspan class=\"pl-k\"\u003eunsigned\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eint\u003c/span\u003e x, y, w, h; \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e (x,y) - top-left corner, (w, h) - width \u0026amp; height of bounded box\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003efloat\u003c/span\u003e prob; \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e confidence - probability that the object was found correctly\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eunsigned\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eint\u003c/span\u003e obj_id; \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e class of object - from range [0, classes-1]\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eunsigned\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eint\u003c/span\u003e track_id; \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e tracking id for video (0 - untracked, 1 - inf - tracked object)\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eunsigned\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eint\u003c/span\u003e frames_counter;\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e counter of frames on which the object was detected\u003c/span\u003e\n};\n\n\u003cspan class=\"pl-k\"\u003eclass\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eDetector\u003c/span\u003e {\n\u003cspan class=\"pl-k\"\u003epublic:\u003c/span\u003e\n \u003cspan class=\"pl-en\"\u003eDetector\u003c/span\u003e(std::string cfg_filename, std::string weight_filename, \u003cspan class=\"pl-k\"\u003eint\u003c/span\u003e gpu_id = \u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e);\n \u003cspan class=\"pl-en\"\u003e~Detector\u003c/span\u003e();\n\n std::vector\u0026lt;\u003cspan class=\"pl-c1\"\u003ebbox_t\u003c/span\u003e\u0026gt; \u003cspan class=\"pl-en\"\u003edetect\u003c/span\u003e(std::string image_filename, \u003cspan class=\"pl-k\"\u003efloat\u003c/span\u003e thresh = \u003cspan class=\"pl-c1\"\u003e0.2\u003c/span\u003e, \u003cspan class=\"pl-k\"\u003ebool\u003c/span\u003e use_mean = \u003cspan class=\"pl-c1\"\u003efalse\u003c/span\u003e);\n std::vector\u0026lt;\u003cspan class=\"pl-c1\"\u003ebbox_t\u003c/span\u003e\u0026gt; \u003cspan class=\"pl-en\"\u003edetect\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003eimage_t\u003c/span\u003e img, \u003cspan class=\"pl-k\"\u003efloat\u003c/span\u003e thresh = \u003cspan class=\"pl-c1\"\u003e0.2\u003c/span\u003e, \u003cspan class=\"pl-k\"\u003ebool\u003c/span\u003e use_mean = \u003cspan class=\"pl-c1\"\u003efalse\u003c/span\u003e);\n \u003cspan class=\"pl-k\"\u003estatic\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eimage_t\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eload_image\u003c/span\u003e(std::string image_filename);\n \u003cspan class=\"pl-k\"\u003estatic\u003c/span\u003e \u003cspan class=\"pl-k\"\u003evoid\u003c/span\u003e \u003cspan class=\"pl-en\"\u003efree_image\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003eimage_t\u003c/span\u003e m);\n\n#\u003cspan class=\"pl-k\"\u003eifdef\u003c/span\u003e OPENCV\n std::vector\u0026lt;\u003cspan class=\"pl-c1\"\u003ebbox_t\u003c/span\u003e\u0026gt; \u003cspan class=\"pl-en\"\u003edetect\u003c/span\u003e(cv::Mat mat, \u003cspan class=\"pl-k\"\u003efloat\u003c/span\u003e thresh = \u003cspan class=\"pl-c1\"\u003e0.2\u003c/span\u003e, \u003cspan class=\"pl-k\"\u003ebool\u003c/span\u003e use_mean = \u003cspan class=\"pl-c1\"\u003efalse\u003c/span\u003e);\n std::shared_ptr\u0026lt;\u003cspan class=\"pl-c1\"\u003eimage_t\u003c/span\u003e\u0026gt; \u003cspan class=\"pl-en\"\u003emat_to_image_resize\u003c/span\u003e(cv::Mat mat) \u003cspan class=\"pl-k\"\u003econst\u003c/span\u003e;\n#\u003cspan class=\"pl-k\"\u003eendif\u003c/span\u003e\n};\u003c/pre\u003e\u003c/div\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"Yolo v4, v3 and v2 for Windows and Linux","anchor":"yolo-v4-v3-and-v2-for-windows-and-linux","htmlText":"Yolo v4, v3 and v2 for Windows and Linux"},{"level":2,"text":"(neural networks for object detection)","anchor":"neural-networks-for-object-detection","htmlText":"(neural networks for object detection)"},{"level":4,"text":"GeForce RTX 2080 Ti:","anchor":"geforce-rtx-2080-ti","htmlText":"GeForce RTX 2080 Ti:"},{"level":4,"text":"Youtube video of results","anchor":"youtube-video-of-results","htmlText":"Youtube video of results"},{"level":4,"text":"How to evaluate AP of YOLOv4 on the MS COCO evaluation server","anchor":"how-to-evaluate-ap-of-yolov4-on-the-ms-coco-evaluation-server","htmlText":"How to evaluate AP of YOLOv4 on the MS COCO evaluation server"},{"level":4,"text":"How to evaluate FPS of YOLOv4 on GPU","anchor":"how-to-evaluate-fps-of-yolov4-on-gpu","htmlText":"How to evaluate FPS of YOLOv4 on GPU"},{"level":4,"text":"Pre-trained models","anchor":"pre-trained-models","htmlText":"Pre-trained models"},{"level":3,"text":"Requirements","anchor":"requirements","htmlText":"Requirements"},{"level":4,"text":"Yolo v4 in other frameworks","anchor":"yolo-v4-in-other-frameworks","htmlText":"Yolo v4 in other frameworks"},{"level":4,"text":"Datasets","anchor":"datasets","htmlText":"Datasets"},{"level":3,"text":"Improvements in this repository","anchor":"improvements-in-this-repository","htmlText":"Improvements in this repository"},{"level":4,"text":"How to use on the command line","anchor":"how-to-use-on-the-command-line","htmlText":"How to use on the command line"},{"level":5,"text":"For using network video-camera mjpeg-stream with any Android smartphone","anchor":"for-using-network-video-camera-mjpeg-stream-with-any-android-smartphone","htmlText":"For using network video-camera mjpeg-stream with any Android smartphone"},{"level":3,"text":"How to compile on Linux/macOS (using CMake)","anchor":"how-to-compile-on-linuxmacos-using-cmake","htmlText":"How to compile on Linux/macOS (using CMake)"},{"level":3,"text":"How to compile on Linux (using make)","anchor":"how-to-compile-on-linux-using-make","htmlText":"How to compile on Linux (using make)"},{"level":3,"text":"How to compile on Windows (using CMake)","anchor":"how-to-compile-on-windows-using-cmake","htmlText":"How to compile on Windows (using CMake)"},{"level":2,"text":"How to train with multi-GPU","anchor":"how-to-train-with-multi-gpu","htmlText":"How to train with multi-GPU"},{"level":2,"text":"How to train (to detect your custom objects)","anchor":"how-to-train-to-detect-your-custom-objects","htmlText":"How to train (to detect your custom objects)"},{"level":3,"text":"How to train tiny-yolo (to detect your custom objects):","anchor":"how-to-train-tiny-yolo-to-detect-your-custom-objects","htmlText":"How to train tiny-yolo (to detect your custom objects):"},{"level":2,"text":"When should I stop training:","anchor":"when-should-i-stop-training","htmlText":"When should I stop training:"},{"level":3,"text":"Custom object detection:","anchor":"custom-object-detection","htmlText":"Custom object detection:"},{"level":2,"text":"How to improve object detection:","anchor":"how-to-improve-object-detection","htmlText":"How to improve object detection:"},{"level":2,"text":"How to mark bounded boxes of objects and create annotation files:","anchor":"how-to-mark-bounded-boxes-of-objects-and-create-annotation-files","htmlText":"How to mark bounded boxes of objects and create annotation files:"},{"level":2,"text":"How to use Yolo as DLL and SO libraries","anchor":"how-to-use-yolo-as-dll-and-so-libraries","htmlText":"How to use Yolo as DLL and SO libraries"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fiberganzo%2Fdarknet"}},{"displayName":"LICENSE","repoName":"darknet","refName":"master","path":"LICENSE","preferredFileType":"license","tabName":"License","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fiberganzo%2Fdarknet"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-1583894afd38.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-67668e8c2caa.js","githubDevUrl":null,"enabled_features":{"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"copilot_workspace":false,"accessible_code_button":true}}}}</script> <div data-target="react-partial.reactRoot"><style 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width="16" height="16" fill="currentColor" style="display:inline-block;user-select:none;vertical-align:text-bottom;overflow:visible"><path d="M5.75 2.5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Yolo v4, v3 and v2 for Windows and Linux</h1><a id="user-content-yolo-v4-v3-and-v2-for-windows-and-linux" class="anchor" aria-label="Permalink: Yolo v4, v3 and v2 for Windows and Linux" href="#yolo-v4-v3-and-v2-for-windows-and-linux"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">(neural networks for object detection)</h2><a id="user-content-neural-networks-for-object-detection" class="anchor" aria-label="Permalink: (neural networks for object detection)" href="#neural-networks-for-object-detection"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Paper YOLO v4: <a href="https://arxiv.org/abs/2004.10934" rel="nofollow">https://arxiv.org/abs/2004.10934</a></p> <p dir="auto">Paper Scaled YOLO v4: <a href="https://arxiv.org/abs/2011.08036" rel="nofollow">https://arxiv.org/abs/2011.08036</a></p> <p dir="auto">More details: <a href="https://medium.com/@alexeyab84/yolov4-the-most-accurate-real-time-neural-network-on-ms-coco-dataset-73adfd3602fe?source=friends_link&sk=6039748846bbcf1d960c3061542591d7" rel="nofollow">medium link</a></p> <p dir="auto">Manual: <a href="https://github.com/AlexeyAB/darknet/wiki">https://github.com/AlexeyAB/darknet/wiki</a></p> <p dir="auto">Discussion:</p> <ul dir="auto"> <li><a href="https://www.reddit.com/r/MachineLearning/comments/gydxzd/p_yolov4_the_most_accurate_realtime_neural/" rel="nofollow">Reddit</a></li> <li><a href="https://groups.google.com/forum/#!forum/darknet" rel="nofollow">Google-groups</a></li> <li><a href="https://discord.gg/zSq8rtW" rel="nofollow">Discord</a></li> </ul> <p dir="auto">About Darknet framework: <a href="http://pjreddie.com/darknet/" rel="nofollow">http://pjreddie.com/darknet/</a></p> <p dir="auto"><a href="https://github.com/AlexeyAB/darknet/actions?query=workflow%3A%22Darknet+Continuous+Integration%22"><img src="https://github.com/AlexeyAB/darknet/workflows/Darknet%20Continuous%20Integration/badge.svg" alt="Darknet Continuous Integration" style="max-width: 100%;"></a> <a href="https://circleci.com/gh/AlexeyAB/darknet" rel="nofollow"><img src="https://camo.githubusercontent.com/2a61971b36eda7db271b4e5452c78a9687c202537326c377f2fe022119e68a13/68747470733a2f2f636972636c6563692e636f6d2f67682f416c6578657941422f6461726b6e65742e7376673f7374796c653d737667" alt="CircleCI" data-canonical-src="https://circleci.com/gh/AlexeyAB/darknet.svg?style=svg" style="max-width: 100%;"></a> <a href="https://travis-ci.org/AlexeyAB/darknet" rel="nofollow"><img src="https://camo.githubusercontent.com/f5ac6f898829b2c820a5ed146577c36d5268515cbad3d2ff955b28e1bc70e6cf/68747470733a2f2f7472617669732d63692e6f72672f416c6578657941422f6461726b6e65742e7376673f6272616e63683d6d6173746572" alt="TravisCI" data-canonical-src="https://travis-ci.org/AlexeyAB/darknet.svg?branch=master" style="max-width: 100%;"></a> <a href="https://github.com/AlexeyAB/darknet/graphs/contributors"><img src="https://camo.githubusercontent.com/b634abea55c448d288f07d6a300a27e3ac29996fa2c12cb9bb190765d4fb4bb8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f636f6e7472696275746f72732f416c6578657941422f4461726b6e65742e737667" alt="Contributors" data-canonical-src="https://img.shields.io/github/contributors/AlexeyAB/Darknet.svg" style="max-width: 100%;"></a> <a href="https://github.com/AlexeyAB/darknet/blob/master/LICENSE"><img src="https://camo.githubusercontent.com/b4c74d90cf40e8e768fcacf6d423cb23f849b05dd7fa01bd6bfd441291f0c53c/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d556e6c6963656e73652d626c75652e737667" alt="License: Unlicense" data-canonical-src="https://img.shields.io/badge/license-Unlicense-blue.svg" style="max-width: 100%;"></a> <a href="https://zenodo.org/badge/latestdoi/75388965" rel="nofollow"><img src="https://camo.githubusercontent.com/ef0a402c8c6fb5952d2fb54b6859ff3c32941c7e112d45d69f0f067e578e68af/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f37353338383936352e737667" alt="DOI" data-canonical-src="https://zenodo.org/badge/75388965.svg" style="max-width: 100%;"></a> <a href="https://arxiv.org/abs/2004.10934" rel="nofollow"><img src="https://camo.githubusercontent.com/896881fe1ae407dcc8bb09df37e83b70feba9f7aa611fe1a298e4d0ac6852612/687474703a2f2f696d672e736869656c64732e696f2f62616467652f63732e43562d6172586976253341323030342e31303933342d4233314231422e737667" alt="arxiv.org" data-canonical-src="http://img.shields.io/badge/cs.CV-arXiv%3A2004.10934-B31B1B.svg" style="max-width: 100%;"></a> <a href="https://colab.research.google.com/drive/12QusaaRj_lUwCGDvQNfICpa7kA7_a2dE" rel="nofollow"><img src="https://user-images.githubusercontent.com/4096485/86174089-b2709f80-bb29-11ea-9faf-3d8dc668a1a5.png" alt="colab" style="max-width: 100%;"></a> <a href="https://colab.research.google.com/drive/1_GdoqCJWXsChrOiY8sZMr_zbr_fH-0Fg" rel="nofollow"><img src="https://user-images.githubusercontent.com/4096485/86174097-b56b9000-bb29-11ea-9240-c17f6bacfc34.png" alt="colab" style="max-width: 100%;"></a></p> <ul dir="auto"> <li><a href="https://github.com/AlexeyAB/darknet/wiki/YOLOv4-model-zoo">YOLOv4 model zoo</a></li> <li><a href="#requirements">Requirements (and how to install dependecies)</a></li> <li><a href="#pre-trained-models">Pre-trained models</a></li> <li><a href="https://github.com/AlexeyAB/darknet/wiki/FAQ---frequently-asked-questions">FAQ - frequently asked questions</a></li> <li><a href="https://github.com/AlexeyAB/darknet/issues?q=is%3Aopen+is%3Aissue+label%3AExplanations">Explanations in issues</a></li> <li><a href="#yolo-v4-in-other-frameworks">Yolo v4 in other frameworks (TensorRT, TensorFlow, PyTorch, OpenVINO, OpenCV-dnn, TVM,...)</a></li> <li><a href="#datasets">Datasets</a></li> </ul> <ol start="0" dir="auto"> <li><a href="#improvements-in-this-repository">Improvements in this repository</a></li> <li><a href="#how-to-use-on-the-command-line">How to use</a></li> <li>How to compile on Linux <ul dir="auto"> <li><a href="#how-to-compile-on-linux-using-cmake">Using cmake</a></li> <li><a href="#how-to-compile-on-linux-using-make">Using make</a></li> </ul> </li> <li>How to compile on Windows <ul dir="auto"> <li><a href="#how-to-compile-on-windows-using-cmake">Using cmake</a></li> <li><a href="#how-to-compile-on-windows-using-vcpkg">Using vcpkg</a></li> <li><a href="#how-to-compile-on-windows-legacy-way">Legacy way</a></li> </ul> </li> <li><a href="https://github.com/AlexeyAB/darknet/wiki#training-and-evaluation-of-speed-and-accuracy-on-ms-coco">Training and Evaluation of speed and accuracy on MS COCO</a></li> <li><a href="#how-to-train-with-multi-gpu">How to train with multi-GPU:</a></li> <li><a href="#how-to-train-to-detect-your-custom-objects">How to train (to detect your custom objects)</a></li> <li><a href="#how-to-train-tiny-yolo-to-detect-your-custom-objects">How to train tiny-yolo (to detect your custom objects)</a></li> <li><a href="#when-should-i-stop-training">When should I stop training</a></li> <li><a href="#how-to-improve-object-detection">How to improve object detection</a></li> <li><a href="#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files">How to mark bounded boxes of objects and create annotation files</a></li> <li><a href="#how-to-use-yolo-as-dll-and-so-libraries">How to use Yolo as DLL and SO libraries</a></li> </ol> <p dir="auto"><a target="_blank" rel="noopener noreferrer nofollow" href="https://camo.githubusercontent.com/fe9d157cc2c42d6b70dec5a1273c7e1be99e2cc371437d28ddf4b8ce64e44bf5/687474703a2f2f706a7265646469652e636f6d2f6d656469612f66696c65732f6461726b6e65742d626c61636b2d736d616c6c2e706e67"><img src="https://camo.githubusercontent.com/fe9d157cc2c42d6b70dec5a1273c7e1be99e2cc371437d28ddf4b8ce64e44bf5/687474703a2f2f706a7265646469652e636f6d2f6d656469612f66696c65732f6461726b6e65742d626c61636b2d736d616c6c2e706e67" alt="Darknet Logo" data-canonical-src="http://pjreddie.com/media/files/darknet-black-small.png" style="max-width: 100%;"></a></p> <p dir="auto"><a target="_blank" rel="noopener noreferrer nofollow" href="https://user-images.githubusercontent.com/4096485/101356322-f1f5a180-38a8-11eb-9907-4fe4f188d887.png"><img src="https://user-images.githubusercontent.com/4096485/101356322-f1f5a180-38a8-11eb-9907-4fe4f188d887.png" alt="scaled_yolov4" style="max-width: 100%;"></a> AP50:95 - FPS (Tesla V100) Paper: <a href="https://arxiv.org/abs/2011.08036" rel="nofollow">https://arxiv.org/abs/2011.08036</a></p> <hr> <p dir="auto"><a target="_blank" rel="noopener noreferrer nofollow" href="https://user-images.githubusercontent.com/4096485/82835867-f1c62380-9ecd-11ea-9134-1598ed2abc4b.png"><img src="https://user-images.githubusercontent.com/4096485/82835867-f1c62380-9ecd-11ea-9134-1598ed2abc4b.png" alt="modern_gpus" style="max-width: 100%;"></a> AP50:95 / AP50 - FPS (Tesla V100) Paper: <a href="https://arxiv.org/abs/2004.10934" rel="nofollow">https://arxiv.org/abs/2004.10934</a></p> <p dir="auto">tkDNN-TensorRT accelerates YOLOv4 <strong>~2x</strong> times for batch=1 and <strong>3x-4x</strong> times for batch=4.</p> <ul dir="auto"> <li>tkDNN: <a href="https://github.com/ceccocats/tkDNN">https://github.com/ceccocats/tkDNN</a></li> <li>OpenCV: <a href="https://gist.github.com/YashasSamaga/48bdb167303e10f4d07b754888ddbdcf">https://gist.github.com/YashasSamaga/48bdb167303e10f4d07b754888ddbdcf</a></li> </ul> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">GeForce RTX 2080 Ti:</h4><a id="user-content-geforce-rtx-2080-ti" class="anchor" aria-label="Permalink: GeForce RTX 2080 Ti:" href="#geforce-rtx-2080-ti"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th align="center">Network Size</th> <th align="center">Darknet, FPS (avg)</th> <th align="right">tkDNN TensorRT FP32, FPS</th> <th align="right">tkDNN TensorRT FP16, FPS</th> <th align="right">OpenCV FP16, FPS</th> <th align="right">tkDNN TensorRT FP16 batch=4, FPS</th> <th align="right">OpenCV FP16 batch=4, FPS</th> <th align="right">tkDNN Speedup</th> </tr> </thead> <tbody> <tr> <td align="center">320</td> <td align="center">100</td> <td align="right">116</td> <td align="right"><strong>202</strong></td> <td align="right">183</td> <td align="right">423</td> <td align="right"><strong>430</strong></td> <td align="right"><strong>4.3x</strong></td> </tr> <tr> <td align="center">416</td> <td align="center">82</td> <td align="right">103</td> <td align="right"><strong>162</strong></td> <td align="right">159</td> <td align="right">284</td> <td align="right"><strong>294</strong></td> <td align="right"><strong>3.6x</strong></td> </tr> <tr> <td align="center">512</td> <td align="center">69</td> <td align="right">91</td> <td align="right">134</td> <td align="right"><strong>138</strong></td> <td align="right">206</td> <td align="right"><strong>216</strong></td> <td align="right"><strong>3.1x</strong></td> </tr> <tr> <td align="center">608</td> <td align="center">53</td> <td align="right">62</td> <td align="right">103</td> <td align="right"><strong>115</strong></td> <td align="right">150</td> <td align="right"><strong>150</strong></td> <td align="right"><strong>2.8x</strong></td> </tr> <tr> <td align="center">Tiny 416</td> <td align="center">443</td> <td align="right">609</td> <td align="right"><strong>790</strong></td> <td align="right">773</td> <td align="right"><strong>1774</strong></td> <td align="right">1353</td> <td align="right"><strong>3.5x</strong></td> </tr> <tr> <td align="center">Tiny 416 CPU Core i7 7700HQ</td> <td align="center">3.4</td> <td align="right">-</td> <td align="right">-</td> <td align="right">42</td> <td align="right">-</td> <td align="right">39</td> <td align="right"><strong>12x</strong></td> </tr> </tbody> </table></markdown-accessiblity-table> <ul dir="auto"> <li>Yolo v4 Full comparison: <a href="https://user-images.githubusercontent.com/4096485/80283279-0e303e00-871f-11ea-814c-870967d77fd1.png" rel="nofollow">map_fps</a></li> <li>Yolo v4 tiny comparison: <a href="https://user-images.githubusercontent.com/4096485/85734112-6e366700-b705-11ea-95d1-fcba0de76d72.png" rel="nofollow">tiny_fps</a></li> <li>CSPNet: <a href="https://arxiv.org/abs/1911.11929" rel="nofollow">paper</a> and <a href="https://user-images.githubusercontent.com/4096485/71702416-6645dc00-2de0-11ea-8d65-de7d4b604021.png" rel="nofollow">map_fps</a> comparison: <a href="https://github.com/WongKinYiu/CrossStagePartialNetworks">https://github.com/WongKinYiu/CrossStagePartialNetworks</a></li> <li>Yolo v3 on MS COCO: <a href="https://user-images.githubusercontent.com/4096485/52151356-e5d4a380-2683-11e9-9d7d-ac7bc192c477.jpg" rel="nofollow">Speed / Accuracy (mAP@0.5) chart</a></li> <li>Yolo v3 on MS COCO (Yolo v3 vs RetinaNet) - Figure 3: <a href="https://arxiv.org/pdf/1804.02767v1.pdf" rel="nofollow">https://arxiv.org/pdf/1804.02767v1.pdf</a></li> <li>Yolo v2 on Pascal VOC 2007: <a href="https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg" rel="nofollow">https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg</a></li> <li>Yolo v2 on Pascal VOC 2012 (comp4): <a href="https://hsto.org/files/3a6/fdf/b53/3a6fdfb533f34cee9b52bdd9bb0b19d9.jpg" rel="nofollow">https://hsto.org/files/3a6/fdf/b53/3a6fdfb533f34cee9b52bdd9bb0b19d9.jpg</a></li> </ul> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Youtube video of results</h4><a id="user-content-youtube-video-of-results" class="anchor" aria-label="Permalink: Youtube video of results" href="#youtube-video-of-results"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th><a href="https://youtu.be/1_SiUOYUoOI" title="Yolo v4" rel="nofollow"><img src="https://user-images.githubusercontent.com/4096485/101360000-1a33cf00-38ae-11eb-9e5e-b29c5fb0afbe.png" alt="Yolo v4" style="max-width: 100%;"></a></th> <th><a href="https://youtu.be/YDFf-TqJOFE" title="Scaled Yolo v4" rel="nofollow"><img src="https://user-images.githubusercontent.com/4096485/101359389-43a02b00-38ad-11eb-866c-f813e96bf61a.png" alt="Scaled Yolo v4" style="max-width: 100%;"></a></th> </tr> </thead> </table></markdown-accessiblity-table> <p dir="auto">Others: <a href="https://www.youtube.com/user/pjreddie/videos" rel="nofollow">https://www.youtube.com/user/pjreddie/videos</a></p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">How to evaluate AP of YOLOv4 on the MS COCO evaluation server</h4><a id="user-content-how-to-evaluate-ap-of-yolov4-on-the-ms-coco-evaluation-server" class="anchor" aria-label="Permalink: How to evaluate AP of YOLOv4 on the MS COCO evaluation server" href="#how-to-evaluate-ap-of-yolov4-on-the-ms-coco-evaluation-server"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li>Download and unzip test-dev2017 dataset from MS COCO server: <a href="http://images.cocodataset.org/zips/test2017.zip" rel="nofollow">http://images.cocodataset.org/zips/test2017.zip</a></li> <li>Download list of images for Detection taks and replace the paths with yours: <a href="https://raw.githubusercontent.com/AlexeyAB/darknet/master/scripts/testdev2017.txt" rel="nofollow">https://raw.githubusercontent.com/AlexeyAB/darknet/master/scripts/testdev2017.txt</a></li> <li>Download <code>yolov4.weights</code> file 245 MB: <a href="https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights">yolov4.weights</a> (Google-drive mirror <a href="https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT" rel="nofollow">yolov4.weights</a> )</li> <li>Content of the file <code>cfg/coco.data</code> should be</li> </ol> <div class="highlight highlight-source-ini notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="classes= 80 train = <replace with your path>/trainvalno5k.txt valid = <replace with your path>/testdev2017.txt names = data/coco.names backup = backup eval=coco"><pre><span class="pl-k">classes</span>= 80 <span class="pl-k">train</span> = <replace with your path>/trainvalno5k.txt <span class="pl-k">valid</span> = <replace with your path>/testdev2017.txt <span class="pl-k">names</span> = data/coco.names <span class="pl-k">backup</span> = backup <span class="pl-k">eval</span>=coco</pre></div> <ol start="5" dir="auto"> <li>Create <code>/results/</code> folder near with <code>./darknet</code> executable file</li> <li>Run validation: <code>./darknet detector valid cfg/coco.data cfg/yolov4.cfg yolov4.weights</code></li> <li>Rename the file <code>/results/coco_results.json</code> to <code>detections_test-dev2017_yolov4_results.json</code> and compress it to <code>detections_test-dev2017_yolov4_results.zip</code></li> <li>Submit file <code>detections_test-dev2017_yolov4_results.zip</code> to the MS COCO evaluation server for the <code>test-dev2019 (bbox)</code></li> </ol> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">How to evaluate FPS of YOLOv4 on GPU</h4><a id="user-content-how-to-evaluate-fps-of-yolov4-on-gpu" class="anchor" aria-label="Permalink: How to evaluate FPS of YOLOv4 on GPU" href="#how-to-evaluate-fps-of-yolov4-on-gpu"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li>Compile Darknet with <code>GPU=1 CUDNN=1 CUDNN_HALF=1 OPENCV=1</code> in the <code>Makefile</code></li> <li>Download <code>yolov4.weights</code> file 245 MB: <a href="https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights">yolov4.weights</a> (Google-drive mirror <a href="https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT" rel="nofollow">yolov4.weights</a> )</li> <li>Get any .avi/.mp4 video file (preferably not more than 1920x1080 to avoid bottlenecks in CPU performance)</li> <li>Run one of two commands and look at the AVG FPS:</li> </ol> <ul dir="auto"> <li>include video_capturing + NMS + drawing_bboxes: <code>./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -dont_show -ext_output</code></li> <li>exclude video_capturing + NMS + drawing_bboxes: <code>./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -benchmark</code></li> </ul> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Pre-trained models</h4><a id="user-content-pre-trained-models" class="anchor" aria-label="Permalink: Pre-trained models" href="#pre-trained-models"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">There are weights-file for different cfg-files (trained for MS COCO dataset):</p> <p dir="auto">FPS on RTX 2070 (R) and Tesla V100 (V):</p> <ul dir="auto"> <li> <p dir="auto"><a href="https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4x-mish.cfg" rel="nofollow">yolov4x-mish.cfg</a> - <strong>67.9% mAP@0.5 (49.4% AP@0.5:0.95) - 23(R) FPS / 50(V) FPS</strong> - 221 BFlops (110 FMA) - 381 MB: <a href="https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.weights">yolov4x-mish.weights</a></p> <ul dir="auto"> <li>pre-trained weights for training: <a href="https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.conv.166">https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.conv.166</a></li> </ul> </li> <li> <p dir="auto"><a href="https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-csp.cfg" rel="nofollow">yolov4-csp.cfg</a> - 202 MB: <a href="https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.weights">yolov4-csp.weights</a> paper <a href="https://arxiv.org/abs/2011.08036" rel="nofollow">Scaled Yolo v4</a></p> <p dir="auto">just change <code>width=</code> and <code>height=</code> parameters in <code>yolov4-csp.cfg</code> file and use the same <code>yolov4-csp.weights</code> file for all cases:</p> <ul dir="auto"> <li><code>width=608 height=608</code> in cfg: <strong>66.2% mAP@0.5 (47.5% AP@0.5:0.95) - 70(V) FPS</strong> - 120 (60 FMA) BFlops</li> <li><code>width=512 height=512</code> in cfg: <strong>64.8% mAP@0.5 (46.2% AP@0.5:0.95) - 93(V) FPS</strong> - 77 (39 FMA) BFlops</li> <li>pre-trained weights for training: <a href="https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.conv.142">https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.conv.142</a></li> </ul> </li> <li> <p dir="auto"><a href="https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg" rel="nofollow">yolov4.cfg</a> - 245 MB: <a href="https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights">yolov4.weights</a> (Google-drive mirror <a href="https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT" rel="nofollow">yolov4.weights</a> ) paper <a href="https://arxiv.org/abs/2004.10934" rel="nofollow">Yolo v4</a> just change <code>width=</code> and <code>height=</code> parameters in <code>yolov4.cfg</code> file and use the same <code>yolov4.weights</code> file for all cases:</p> <ul dir="auto"> <li><code>width=608 height=608</code> in cfg: <strong>65.7% mAP@0.5 (43.5% AP@0.5:0.95) - 34(R) FPS / 62(V) FPS</strong> - 128.5 BFlops</li> <li><code>width=512 height=512</code> in cfg: <strong>64.9% mAP@0.5 (43.0% AP@0.5:0.95) - 45(R) FPS / 83(V) FPS</strong> - 91.1 BFlops</li> <li><code>width=416 height=416</code> in cfg: <strong>62.8% mAP@0.5 (41.2% AP@0.5:0.95) - 55(R) FPS / 96(V) FPS</strong> - 60.1 BFlops</li> <li><code>width=320 height=320</code> in cfg: <strong>60% mAP@0.5 ( 38% AP@0.5:0.95) - 63(R) FPS / 123(V) FPS</strong> - 35.5 BFlops</li> </ul> </li> <li> <p dir="auto"><a href="https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny.cfg" rel="nofollow">yolov4-tiny.cfg</a> - <strong>40.2% mAP@0.5 - 371(1080Ti) FPS / 330(RTX2070) FPS</strong> - 6.9 BFlops - 23.1 MB: <a href="https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights">yolov4-tiny.weights</a></p> </li> <li> <p dir="auto"><a href="https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/enet-coco.cfg" rel="nofollow">enet-coco.cfg (EfficientNetB0-Yolov3)</a> - <strong>45.5% mAP@0.5 - 55(R) FPS</strong> - 3.7 BFlops - 18.3 MB: <a href="https://drive.google.com/file/d/1FlHeQjWEQVJt0ay1PVsiuuMzmtNyv36m/view" rel="nofollow">enetb0-coco_final.weights</a></p> </li> <li> <p dir="auto"><a href="https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-openimages.cfg" rel="nofollow">yolov3-openimages.cfg</a> - 247 MB - 18(R) FPS - OpenImages dataset: <a href="https://pjreddie.com/media/files/yolov3-openimages.weights" rel="nofollow">yolov3-openimages.weights</a></p> </li> </ul> <details><summary><b>CLICK ME</b> - Yolo v3 models</summary> <ul dir="auto"> <li> <p dir="auto"><a href="https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/csresnext50-panet-spp-original-optimal.cfg" rel="nofollow">csresnext50-panet-spp-original-optimal.cfg</a> - <strong>65.4% mAP@0.5 (43.2% AP@0.5:0.95) - 32(R) FPS</strong> - 100.5 BFlops - 217 MB: <a href="https://drive.google.com/open?id=1_NnfVgj0EDtb_WLNoXV8Mo7WKgwdYZCc" rel="nofollow">csresnext50-panet-spp-original-optimal_final.weights</a></p> </li> <li> <p dir="auto"><a href="https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-spp.cfg" rel="nofollow">yolov3-spp.cfg</a> - <strong>60.6% mAP@0.5 - 38(R) FPS</strong> - 141.5 BFlops - 240 MB: <a href="https://pjreddie.com/media/files/yolov3-spp.weights" rel="nofollow">yolov3-spp.weights</a></p> </li> <li> <p dir="auto"><a href="https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/csresnext50-panet-spp.cfg" rel="nofollow">csresnext50-panet-spp.cfg</a> - <strong>60.0% mAP@0.5 - 44 FPS</strong> - 71.3 BFlops - 217 MB: <a href="https://drive.google.com/file/d/1aNXdM8qVy11nqTcd2oaVB3mf7ckr258-/view?usp=sharing" rel="nofollow">csresnext50-panet-spp_final.weights</a></p> </li> <li> <p dir="auto"><a href="https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3.cfg" rel="nofollow">yolov3.cfg</a> - <strong>55.3% mAP@0.5 - 66(R) FPS</strong> - 65.9 BFlops - 236 MB: <a href="https://pjreddie.com/media/files/yolov3.weights" rel="nofollow">yolov3.weights</a></p> </li> <li> <p dir="auto"><a href="https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny.cfg" rel="nofollow">yolov3-tiny.cfg</a> - <strong>33.1% mAP@0.5 - 345(R) FPS</strong> - 5.6 BFlops - 33.7 MB: <a href="https://pjreddie.com/media/files/yolov3-tiny.weights" rel="nofollow">yolov3-tiny.weights</a></p> </li> <li> <p dir="auto"><a href="https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny-prn.cfg" rel="nofollow">yolov3-tiny-prn.cfg</a> - <strong>33.1% mAP@0.5 - 370(R) FPS</strong> - 3.5 BFlops - 18.8 MB: <a href="https://drive.google.com/file/d/18yYZWyKbo4XSDVyztmsEcF9B_6bxrhUY/view?usp=sharing" rel="nofollow">yolov3-tiny-prn.weights</a></p> </li> </ul> </details> <details><summary><b>CLICK ME</b> - Yolo v2 models</summary> <ul dir="auto"> <li><code>yolov2.cfg</code> (194 MB COCO Yolo v2) - requires 4 GB GPU-RAM: <a href="https://pjreddie.com/media/files/yolov2.weights" rel="nofollow">https://pjreddie.com/media/files/yolov2.weights</a></li> <li><code>yolo-voc.cfg</code> (194 MB VOC Yolo v2) - requires 4 GB GPU-RAM: <a href="http://pjreddie.com/media/files/yolo-voc.weights" rel="nofollow">http://pjreddie.com/media/files/yolo-voc.weights</a></li> <li><code>yolov2-tiny.cfg</code> (43 MB COCO Yolo v2) - requires 1 GB GPU-RAM: <a href="https://pjreddie.com/media/files/yolov2-tiny.weights" rel="nofollow">https://pjreddie.com/media/files/yolov2-tiny.weights</a></li> <li><code>yolov2-tiny-voc.cfg</code> (60 MB VOC Yolo v2) - requires 1 GB GPU-RAM: <a href="http://pjreddie.com/media/files/yolov2-tiny-voc.weights" rel="nofollow">http://pjreddie.com/media/files/yolov2-tiny-voc.weights</a></li> <li><code>yolo9000.cfg</code> (186 MB Yolo9000-model) - requires 4 GB GPU-RAM: <a href="http://pjreddie.com/media/files/yolo9000.weights" rel="nofollow">http://pjreddie.com/media/files/yolo9000.weights</a></li> </ul> </details> <p dir="auto">Put it near compiled: darknet.exe</p> <p dir="auto">You can get cfg-files by path: <code>darknet/cfg/</code></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Requirements</h3><a id="user-content-requirements" class="anchor" aria-label="Permalink: Requirements" href="#requirements"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Windows or Linux</li> <li><strong>CMake >= 3.12</strong>: <a href="https://cmake.org/download/" rel="nofollow">https://cmake.org/download/</a></li> <li><strong>CUDA >= 10.0</strong>: <a href="https://developer.nvidia.com/cuda-toolkit-archive" rel="nofollow">https://developer.nvidia.com/cuda-toolkit-archive</a> (on Linux do <a href="https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions" rel="nofollow">Post-installation Actions</a>)</li> <li><strong>OpenCV >= 2.4</strong>: use your preferred package manager (brew, apt), build from source using <a href="https://github.com/Microsoft/vcpkg">vcpkg</a> or download from <a href="https://opencv.org/releases.html" rel="nofollow">OpenCV official site</a> (on Windows set system variable <code>OpenCV_DIR</code> = <code>C:\opencv\build</code> - where are the <code>include</code> and <code>x64</code> folders <a href="https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png" rel="nofollow">image</a>)</li> <li><strong>cuDNN >= 7.0</strong> <a href="https://developer.nvidia.com/rdp/cudnn-archive" rel="nofollow">https://developer.nvidia.com/rdp/cudnn-archive</a> (on <strong>Linux</strong> copy <code>cudnn.h</code>,<code>libcudnn.so</code>... as desribed here <a href="https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar" rel="nofollow">https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar</a> , on <strong>Windows</strong> copy <code>cudnn.h</code>,<code>cudnn64_7.dll</code>, <code>cudnn64_7.lib</code> as desribed here <a href="https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows" rel="nofollow">https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows</a> )</li> <li><strong>GPU with CC >= 3.0</strong>: <a href="https://en.wikipedia.org/wiki/CUDA#GPUs_supported" rel="nofollow">https://en.wikipedia.org/wiki/CUDA#GPUs_supported</a></li> <li>on Linux <strong>GCC or Clang</strong>, on Windows <strong>MSVC 2017/2019</strong> <a href="https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community" rel="nofollow">https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community</a></li> </ul> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Yolo v4 in other frameworks</h4><a id="user-content-yolo-v4-in-other-frameworks" class="anchor" aria-label="Permalink: Yolo v4 in other frameworks" href="#yolo-v4-in-other-frameworks"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><strong>Pytorch - Scaled-YOLOv4:</strong> <a href="https://github.com/WongKinYiu/ScaledYOLOv4">https://github.com/WongKinYiu/ScaledYOLOv4</a></li> <li><strong>TensorFlow:</strong> <code>pip install yolov4</code> YOLOv4 on TensorFlow 2.0 / TFlite / Andriod: <a href="https://github.com/hunglc007/tensorflow-yolov4-tflite">https://github.com/hunglc007/tensorflow-yolov4-tflite</a> For YOLOv3 - convert <code>yolov3.weights</code>/<code>cfg</code> files to <code>yolov3.ckpt</code>/<code>pb/meta</code>: by using <a href="https://github.com/mystic123/tensorflow-yolo-v3">mystic123</a> project, and <a href="https://www.tensorflow.org/lite/guide/get_started#2_convert_the_model_format" rel="nofollow">TensorFlow-lite</a></li> <li><strong>OpenCV-dnn</strong> the fastest implementation of YOLOv4 for CPU (x86/ARM-Android), OpenCV can be compiled with <a href="https://github.com/opencv/opencv/wiki/Intel's-Deep-Learning-Inference-Engine-backend">OpenVINO-backend</a> for running on (Myriad X / USB Neural Compute Stick / Arria FPGA), use <code>yolov4.weights</code>/<code>cfg</code> with: <a href="https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221">C++ example</a> or <a href="https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150">Python example</a></li> <li><strong>Intel OpenVINO 2020 R4:</strong> (NPU Myriad X / USB Neural Compute Stick / Arria FPGA): read this <a href="https://github.com/TNTWEN/OpenVINO-YOLOV4">manual</a> (old <a href="https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model" rel="nofollow">manual</a> )</li> <li><strong>Tencent/ncnn:</strong> the fastest inference of YOLOv4 on mobile phone CPU: <a href="https://github.com/Tencent/ncnn">https://github.com/Tencent/ncnn</a></li> <li><strong>PyTorch > ONNX</strong>: <ul dir="auto"> <li><a href="https://github.com/WongKinYiu/PyTorch_YOLOv4">WongKinYiu/PyTorch_YOLOv4</a></li> <li><a href="https://github.com/maudzung/Complex-YOLOv4-Pytorch">maudzung/3D-YOLOv4</a></li> <li><a href="https://github.com/Tianxiaomo/pytorch-YOLOv4">Tianxiaomo/pytorch-YOLOv4</a></li> <li><a href="https://github.com/ultralytics/yolov5">YOLOv5</a></li> </ul> </li> <li><strong>ONNX</strong> on Jetson for YOLOv4: <a href="https://developer.nvidia.com/blog/announcing-onnx-runtime-for-jetson/" rel="nofollow">https://developer.nvidia.com/blog/announcing-onnx-runtime-for-jetson/</a></li> <li><strong>TensorRT</strong> YOLOv4 on TensorRT+tkDNN: <a href="https://github.com/ceccocats/tkDNN">https://github.com/ceccocats/tkDNN</a> For YOLOv3 (-70% faster inference): <a href="https://news.developer.nvidia.com/deepstream-sdk-4-now-available/" rel="nofollow">Yolo is natively supported in DeepStream 4.0</a> read <a href="https://docs.nvidia.com/metropolis/deepstream/Custom_YOLO_Model_in_the_DeepStream_YOLO_App.pdf" rel="nofollow">PDF</a>. <a href="https://github.com/wang-xinyu/tensorrtx">wang-xinyu/tensorrtx</a> implemented yolov3-spp, yolov4, etc.</li> <li><strong>Deepstream 5.0 / TensorRT for YOLOv4</strong> <a href="https://github.com/NVIDIA-AI-IOT/yolov4_deepstream">https://github.com/NVIDIA-AI-IOT/yolov4_deepstream</a></li> <li><strong>Amazon Neurochip / Amazon EC2 Inf1 instances</strong> 1.85 times higher throughput and 37% lower cost per image for TensorFlow based YOLOv4 model, using Keras <a href="https://aws.amazon.com/ru/blogs/machine-learning/improving-performance-for-deep-learning-based-object-detection-with-an-aws-neuron-compiled-yolov4-model-on-aws-inferentia/" rel="nofollow">URL</a></li> <li><strong>TVM</strong> - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backends (CPUs, GPUs, FPGA, and specialized accelerators): <a href="https://tvm.ai/about" rel="nofollow">https://tvm.ai/about</a></li> <li><strong>OpenDataCam</strong> - It detects, tracks and counts moving objects by using YOLOv4: <a href="https://github.com/opendatacam/opendatacam#-hardware-pre-requisite">https://github.com/opendatacam/opendatacam#-hardware-pre-requisite</a></li> <li><strong>Netron</strong> - Visualizer for neural networks: <a href="https://github.com/lutzroeder/netron">https://github.com/lutzroeder/netron</a></li> </ul> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Datasets</h4><a id="user-content-datasets" class="anchor" aria-label="Permalink: Datasets" href="#datasets"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>MS COCO: use <code>./scripts/get_coco_dataset.sh</code> to get labeled MS COCO detection dataset</li> <li>OpenImages: use <code>python ./scripts/get_openimages_dataset.py</code> for labeling train detection dataset</li> <li>Pascal VOC: use <code>python ./scripts/voc_label.py</code> for labeling Train/Test/Val detection datasets</li> <li>ILSVRC2012 (ImageNet classification): use <code>./scripts/get_imagenet_train.sh</code> (also <code>imagenet_label.sh</code> for labeling valid set)</li> <li>German/Belgium/Russian/LISA/MASTIF Traffic Sign Datasets for Detection - use this parsers: <a href="https://github.com/angeligareta/Datasets2Darknet#detection-task">https://github.com/angeligareta/Datasets2Darknet#detection-task</a></li> <li>List of other datasets: <a href="https://github.com/AlexeyAB/darknet/tree/master/scripts#datasets">https://github.com/AlexeyAB/darknet/tree/master/scripts#datasets</a></li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Improvements in this repository</h3><a id="user-content-improvements-in-this-repository" class="anchor" aria-label="Permalink: Improvements in this repository" href="#improvements-in-this-repository"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>developed State-of-the-Art object detector YOLOv4</li> <li>added State-of-Art models: CSP, PRN, EfficientNet</li> <li>added layers: [conv_lstm], [scale_channels] SE/ASFF/BiFPN, [local_avgpool], [sam], [Gaussian_yolo], [reorg3d] (fixed [reorg]), fixed [batchnorm]</li> <li>added the ability for training recurrent models (with layers conv-lstm<code>[conv_lstm]</code>/conv-rnn<code>[crnn]</code>) for accurate detection on video</li> <li>added data augmentation: <code>[net] mixup=1 cutmix=1 mosaic=1 blur=1</code>. Added activations: SWISH, MISH, NORM_CHAN, NORM_CHAN_SOFTMAX</li> <li>added the ability for training with GPU-processing using CPU-RAM to increase the mini_batch_size and increase accuracy (instead of batch-norm sync)</li> <li>improved binary neural network performance <strong>2x-4x times</strong> for Detection on CPU and GPU if you trained your own weights by using this XNOR-net model (bit-1 inference) : <a href="https://github.com/AlexeyAB/darknet/blob/master/cfg/yolov3-tiny_xnor.cfg">https://github.com/AlexeyAB/darknet/blob/master/cfg/yolov3-tiny_xnor.cfg</a></li> <li>improved neural network performance <strong>~7%</strong> by fusing 2 layers into 1: Convolutional + Batch-norm</li> <li>improved performance: Detection <strong>2x times</strong>, on GPU Volta/Turing (Tesla V100, GeForce RTX, ...) using Tensor Cores if <code>CUDNN_HALF</code> defined in the <code>Makefile</code> or <code>darknet.sln</code></li> <li>improved performance <strong>~1.2x</strong> times on FullHD, <strong>~2x</strong> times on 4K, for detection on the video (file/stream) using <code>darknet detector demo</code>...</li> <li>improved performance <strong>3.5 X times</strong> of data augmentation for training (using OpenCV SSE/AVX functions instead of hand-written functions) - removes bottleneck for training on multi-GPU or GPU Volta</li> <li>improved performance of detection and training on Intel CPU with AVX (Yolo v3 <strong>~85%</strong>)</li> <li>optimized memory allocation during network resizing when <code>random=1</code></li> <li>optimized GPU initialization for detection - we use batch=1 initially instead of re-init with batch=1</li> <li>added correct calculation of <strong>mAP, F1, IoU, Precision-Recall</strong> using command <code>darknet detector map</code>...</li> <li>added drawing of chart of average-Loss and accuracy-mAP (<code>-map</code> flag) during training</li> <li>run <code>./darknet detector demo ... -json_port 8070 -mjpeg_port 8090</code> as JSON and MJPEG server to get results online over the network by using your soft or Web-browser</li> <li>added calculation of anchors for training</li> <li>added example of Detection and Tracking objects: <a href="https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp">https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp</a></li> <li>run-time tips and warnings if you use incorrect cfg-file or dataset</li> <li>added support for Windows</li> <li>many other fixes of code...</li> </ul> <p dir="auto">And added manual - <a href="#how-to-train-to-detect-your-custom-objects">How to train Yolo v4-v2 (to detect your custom objects)</a></p> <p dir="auto">Also, you might be interested in using a simplified repository where is implemented INT8-quantization (+30% speedup and -1% mAP reduced): <a href="https://github.com/AlexeyAB/yolo2_light">https://github.com/AlexeyAB/yolo2_light</a></p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">How to use on the command line</h4><a id="user-content-how-to-use-on-the-command-line" class="anchor" aria-label="Permalink: How to use on the command line" href="#how-to-use-on-the-command-line"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">On Linux use <code>./darknet</code> instead of <code>darknet.exe</code>, like this:<code>./darknet detector test ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights</code></p> <p dir="auto">On Linux find executable file <code>./darknet</code> in the root directory, while on Windows find it in the directory <code>\build\darknet\x64</code></p> <ul dir="auto"> <li>Yolo v4 COCO - <strong>image</strong>: <code>darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25</code></li> <li><strong>Output coordinates</strong> of objects: <code>darknet.exe detector test cfg/coco.data yolov4.cfg yolov4.weights -ext_output dog.jpg</code></li> <li>Yolo v4 COCO - <strong>video</strong>: <code>darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output test.mp4</code></li> <li>Yolo v4 COCO - <strong>WebCam 0</strong>: <code>darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -c 0</code></li> <li>Yolo v4 COCO for <strong>net-videocam</strong> - Smart WebCam: <code>darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg</code></li> <li>Yolo v4 - <strong>save result videofile res.avi</strong>: <code>darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -out_filename res.avi</code></li> <li>Yolo v3 <strong>Tiny</strong> COCO - video: <code>darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights test.mp4</code></li> <li><strong>JSON and MJPEG server</strong> that allows multiple connections from your soft or Web-browser <code>ip-address:8070</code> and 8090: <code>./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -mjpeg_port 8090 -ext_output</code></li> <li>Yolo v3 Tiny <strong>on GPU #1</strong>: <code>darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 1 test.mp4</code></li> <li>Alternative method Yolo v3 COCO - image: <code>darknet.exe detect cfg/yolov4.cfg yolov4.weights -i 0 -thresh 0.25</code></li> <li>Train on <strong>Amazon EC2</strong>, to see mAP & Loss-chart using URL like: <code>http://ec2-35-160-228-91.us-west-2.compute.amazonaws.com:8090</code> in the Chrome/Firefox (<strong>Darknet should be compiled with OpenCV</strong>): <code>./darknet detector train cfg/coco.data yolov4.cfg yolov4.conv.137 -dont_show -mjpeg_port 8090 -map</code></li> <li>186 MB Yolo9000 - image: <code>darknet.exe detector test cfg/combine9k.data cfg/yolo9000.cfg yolo9000.weights</code></li> <li>Remeber to put data/9k.tree and data/coco9k.map under the same folder of your app if you use the cpp api to build an app</li> <li>To process a list of images <code>data/train.txt</code> and save results of detection to <code>result.json</code> file use: <code>darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output -dont_show -out result.json < data/train.txt</code></li> <li>To process a list of images <code>data/train.txt</code> and save results of detection to <code>result.txt</code> use:<br> <code>darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -dont_show -ext_output < data/train.txt > result.txt</code></li> <li>Pseudo-lableing - to process a list of images <code>data/new_train.txt</code> and save results of detection in Yolo training format for each image as label <code><image_name>.txt</code> (in this way you can increase the amount of training data) use: <code>darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25 -dont_show -save_labels < data/new_train.txt</code></li> <li>To calculate anchors: <code>darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416</code></li> <li>To check accuracy mAP@IoU=50: <code>darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights</code></li> <li>To check accuracy mAP@IoU=75: <code>darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights -iou_thresh 0.75</code></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">For using network video-camera mjpeg-stream with any Android smartphone</h5><a id="user-content-for-using-network-video-camera-mjpeg-stream-with-any-android-smartphone" class="anchor" aria-label="Permalink: For using network video-camera mjpeg-stream with any Android smartphone" href="#for-using-network-video-camera-mjpeg-stream-with-any-android-smartphone"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li> <p dir="auto">Download for Android phone mjpeg-stream soft: IP Webcam / Smart WebCam</p> <ul dir="auto"> <li>Smart WebCam - preferably: <a href="https://play.google.com/store/apps/details?id=com.acontech.android.SmartWebCam2" rel="nofollow">https://play.google.com/store/apps/details?id=com.acontech.android.SmartWebCam2</a></li> <li>IP Webcam: <a href="https://play.google.com/store/apps/details?id=com.pas.webcam" rel="nofollow">https://play.google.com/store/apps/details?id=com.pas.webcam</a></li> </ul> </li> <li> <p dir="auto">Connect your Android phone to computer by WiFi (through a WiFi-router) or USB</p> </li> <li> <p dir="auto">Start Smart WebCam on your phone</p> </li> <li> <p dir="auto">Replace the address below, on shown in the phone application (Smart WebCam) and launch:</p> </li> </ol> <ul dir="auto"> <li>Yolo v4 COCO-model: <code>darknet.exe detector demo data/coco.data yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0</code></li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">How to compile on Linux/macOS (using <code>CMake</code>)</h3><a id="user-content-how-to-compile-on-linuxmacos-using-cmake" class="anchor" aria-label="Permalink: How to compile on Linux/macOS (using CMake)" href="#how-to-compile-on-linuxmacos-using-cmake"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The <code>CMakeLists.txt</code> will attempt to find installed optional dependencies like CUDA, cudnn, ZED and build against those. It will also create a shared object library file to use <code>darknet</code> for code development.</p> <p dir="auto">Open a shell terminal inside the cloned repository and launch:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="./build.sh"><pre>./build.sh</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">How to compile on Linux (using <code>make</code>)</h3><a id="user-content-how-to-compile-on-linux-using-make" class="anchor" aria-label="Permalink: How to compile on Linux (using make)" href="#how-to-compile-on-linux-using-make"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Just do <code>make</code> in the darknet directory. (You can try to compile and run it on Google Colab in cloud <a href="https://colab.research.google.com/drive/12QusaaRj_lUwCGDvQNfICpa7kA7_a2dE" rel="nofollow">link</a> (press «Open in Playground» button at the top-left corner) and watch the video <a href="https://www.youtube.com/watch?v=mKAEGSxwOAY" rel="nofollow">link</a> ) Before make, you can set such options in the <code>Makefile</code>: <a href="https://github.com/AlexeyAB/darknet/blob/9c1b9a2cf6363546c152251be578a21f3c3caec6/Makefile#L1">link</a></p> <ul dir="auto"> <li><code>GPU=1</code> to build with CUDA to accelerate by using GPU (CUDA should be in <code>/usr/local/cuda</code>)</li> <li><code>CUDNN=1</code> to build with cuDNN v5-v7 to accelerate training by using GPU (cuDNN should be in <code>/usr/local/cudnn</code>)</li> <li><code>CUDNN_HALF=1</code> to build for Tensor Cores (on Titan V / Tesla V100 / DGX-2 and later) speedup Detection 3x, Training 2x</li> <li><code>OPENCV=1</code> to build with OpenCV 4.x/3.x/2.4.x - allows to detect on video files and video streams from network cameras or web-cams</li> <li><code>DEBUG=1</code> to bould debug version of Yolo</li> <li><code>OPENMP=1</code> to build with OpenMP support to accelerate Yolo by using multi-core CPU</li> <li><code>LIBSO=1</code> to build a library <code>darknet.so</code> and binary runable file <code>uselib</code> that uses this library. Or you can try to run so <code>LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib test.mp4</code> How to use this SO-library from your own code - you can look at C++ example: <a href="https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp">https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp</a> or use in such a way: <code>LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov4.cfg yolov4.weights test.mp4</code></li> <li><code>ZED_CAMERA=1</code> to build a library with ZED-3D-camera support (should be ZED SDK installed), then run <code>LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov4.cfg yolov4.weights zed_camera</code></li> <li>You also need to specify for which graphics card the code is generated. This is done by setting <code>ARCH=</code>. If you use a never version than CUDA 11 you further need to edit line 20 from Makefile and remove <code>-gencode arch=compute_30,code=sm_30 \</code> as Kepler GPU support was dropped in CUDA 11. You can also drop the general <code>ARCH=</code> and just uncomment <code>ARCH=</code> for your graphics card.</li> </ul> <p dir="auto">To run Darknet on Linux use examples from this article, just use <code>./darknet</code> instead of <code>darknet.exe</code>, i.e. use this command: <code>./darknet detector test ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights</code></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">How to compile on Windows (using <code>CMake</code>)</h3><a id="user-content-how-to-compile-on-windows-using-cmake" class="anchor" aria-label="Permalink: How to compile on Windows (using CMake)" href="#how-to-compile-on-windows-using-cmake"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This is the recommended approach to build Darknet on Windows.</p> <ol dir="auto"> <li> <p dir="auto">Install Visual Studio 2017 or 2019. In case you need to download it, please go here: <a href="http://visualstudio.com" rel="nofollow">Visual Studio Community</a></p> </li> <li> <p dir="auto">Install CUDA (at least v10.0) enabling VS Integration during installation.</p> </li> <li> <p dir="auto">Open Powershell (Start -> All programs -> Windows Powershell) and type these commands:</p> </li> </ol> <div class="highlight highlight-source-powershell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="PS Code\> git clone https://github.com/microsoft/vcpkg PS Code\> cd vcpkg PS Code\vcpkg> $env:VCPKG_ROOT=$PWD PS Code\vcpkg> .\bootstrap-vcpkg.bat PS Code\vcpkg> .\vcpkg install darknet[full]:x64-windows #replace with darknet[opencv-base,cuda,cudnn]:x64-windows for a quicker install of dependencies PS Code\vcpkg> cd .. PS Code\> git clone https://github.com/AlexeyAB/darknet PS Code\> cd darknet PS Code\darknet> .\build.ps1"><pre>PS Code\<span class="pl-k">></span> git clone https:<span class="pl-k">//</span><span class="pl-c1">github.com</span><span class="pl-k">/</span>microsoft<span class="pl-k">/</span>vcpkg PS Code\<span class="pl-k">></span> cd vcpkg PS Code\vcpkg<span class="pl-k">></span> <span class="pl-smi">$<span class="pl-c1">env:</span>VCPKG_ROOT</span><span class="pl-k">=</span><span class="pl-c1">$PWD</span> PS Code\vcpkg<span class="pl-k">></span> .\<span class="pl-c1">bootstrap-vcpkg.bat</span> PS Code\vcpkg<span class="pl-k">></span> .\vcpkg install darknet[<span class="pl-k">full</span>]:x64<span class="pl-k">-</span>windows <span class="pl-c"><span class="pl-c">#</span>replace with darknet[opencv-base,cuda,cudnn]:x64-windows for a quicker install of dependencies</span> PS Code\vcpkg<span class="pl-k">></span> cd .. PS Code\<span class="pl-k">></span> git clone https:<span class="pl-k">//</span><span class="pl-c1">github.com</span><span class="pl-k">/</span>AlexeyAB<span class="pl-k">/</span>darknet PS Code\<span class="pl-k">></span> cd darknet PS Code\darknet<span class="pl-k">></span> .\build.ps1</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">How to train with multi-GPU</h2><a id="user-content-how-to-train-with-multi-gpu" class="anchor" aria-label="Permalink: How to train with multi-GPU" href="#how-to-train-with-multi-gpu"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li> <p dir="auto">Train it first on 1 GPU for like 1000 iterations: <code>darknet.exe detector train cfg/coco.data cfg/yolov4.cfg yolov4.conv.137</code></p> </li> <li> <p dir="auto">Then stop and by using partially-trained model <code>/backup/yolov4_1000.weights</code> run training with multigpu (up to 4 GPUs): <code>darknet.exe detector train cfg/coco.data cfg/yolov4.cfg /backup/yolov4_1000.weights -gpus 0,1,2,3</code></p> </li> </ol> <p dir="auto">If you get a Nan, then for some datasets better to decrease learning rate, for 4 GPUs set <code>learning_rate = 0,00065</code> (i.e. learning_rate = 0.00261 / GPUs). In this case also increase 4x times <code>burn_in =</code> in your cfg-file. I.e. use <code>burn_in = 4000</code> instead of <code>1000</code>.</p> <p dir="auto"><a href="https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ" rel="nofollow">https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">How to train (to detect your custom objects)</h2><a id="user-content-how-to-train-to-detect-your-custom-objects" class="anchor" aria-label="Permalink: How to train (to detect your custom objects)" href="#how-to-train-to-detect-your-custom-objects"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">(to train old Yolo v2 <code>yolov2-voc.cfg</code>, <code>yolov2-tiny-voc.cfg</code>, <code>yolo-voc.cfg</code>, <code>yolo-voc.2.0.cfg</code>, ... <a href="https://github.com/AlexeyAB/darknet/tree/47c7af1cea5bbdedf1184963355e6418cb8b1b4f#how-to-train-pascal-voc-data">click by the link</a>)</p> <p dir="auto">Training Yolo v4 (and v3):</p> <ol start="0" dir="auto"> <li> <p dir="auto">For training <code>cfg/yolov4-custom.cfg</code> download the pre-trained weights-file (162 MB): <a href="https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137">yolov4.conv.137</a> (Google drive mirror <a href="https://drive.google.com/open?id=1JKF-bdIklxOOVy-2Cr5qdvjgGpmGfcbp" rel="nofollow">yolov4.conv.137</a> )</p> </li> <li> <p dir="auto">Create file <code>yolo-obj.cfg</code> with the same content as in <code>yolov4-custom.cfg</code> (or copy <code>yolov4-custom.cfg</code> to <code>yolo-obj.cfg)</code> and:</p> </li> </ol> <ul dir="auto"> <li>change line batch to <a href="https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L3"><code>batch=64</code></a></li> <li>change line subdivisions to <a href="https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4"><code>subdivisions=16</code></a></li> <li>change line max_batches to (<code>classes*2000</code> but not less than number of training images, but not less than number of training images and not less than <code>6000</code>), f.e. <a href="https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L20"><code>max_batches=6000</code></a> if you train for 3 classes</li> <li>change line steps to 80% and 90% of max_batches, f.e. <a href="https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L22"><code>steps=4800,5400</code></a></li> <li>set network size <code>width=416 height=416</code> or any value multiple of 32: <a href="https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L8-L9">https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L8-L9</a></li> <li>change line <code>classes=80</code> to your number of objects in each of 3 <code>[yolo]</code>-layers: <ul dir="auto"> <li><a href="https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L610">https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L610</a></li> <li><a href="https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L696">https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L696</a></li> <li><a href="https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L783">https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L783</a></li> </ul> </li> <li>change [<code>filters=255</code>] to filters=(classes + 5)x3 in the 3 <code>[convolutional]</code> before each <code>[yolo]</code> layer, keep in mind that it only has to be the last <code>[convolutional]</code> before each of the <code>[yolo]</code> layers. <ul dir="auto"> <li><a href="https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L603">https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L603</a></li> <li><a href="https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L689">https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L689</a></li> <li><a href="https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L776">https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L776</a></li> </ul> </li> <li>when using <a href="https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L608"><code>[Gaussian_yolo]</code></a> layers, change [<code>filters=57</code>] filters=(classes + 9)x3 in the 3 <code>[convolutional]</code> before each <code>[Gaussian_yolo]</code> layer <ul dir="auto"> <li><a href="https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L604">https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L604</a></li> <li><a href="https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L696">https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L696</a></li> <li><a href="https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L789">https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L789</a></li> </ul> </li> </ul> <p dir="auto">So if <code>classes=1</code> then should be <code>filters=18</code>. If <code>classes=2</code> then write <code>filters=21</code>.</p> <p dir="auto"><strong>(Do not write in the cfg-file: filters=(classes + 5)x3)</strong></p> <p dir="auto">(Generally <code>filters</code> depends on the <code>classes</code>, <code>coords</code> and number of <code>mask</code>s, i.e. filters=<code>(classes + coords + 1)*<number of mask></code>, where <code>mask</code> is indices of anchors. If <code>mask</code> is absence, then filters=<code>(classes + coords + 1)*num</code>)</p> <p dir="auto">So for example, for 2 objects, your file <code>yolo-obj.cfg</code> should differ from <code>yolov4-custom.cfg</code> in such lines in each of <strong>3</strong> [yolo]-layers:</p> <div class="highlight highlight-source-ini notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="[convolutional] filters=21 [region] classes=2"><pre><span class="pl-en">[convolutional]</span> <span class="pl-k">filters</span>=21 <span class="pl-en">[region]</span> <span class="pl-k">classes</span>=2</pre></div> <ol start="2" dir="auto"> <li> <p dir="auto">Create file <code>obj.names</code> in the directory <code>build\darknet\x64\data\</code>, with objects names - each in new line</p> </li> <li> <p dir="auto">Create file <code>obj.data</code> in the directory <code>build\darknet\x64\data\</code>, containing (where <strong>classes = number of objects</strong>):</p> </li> </ol> <div class="highlight highlight-source-ini notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="classes = 2 train = data/train.txt valid = data/test.txt names = data/obj.names backup = backup/"><pre><span class="pl-k">classes</span> = 2 <span class="pl-k">train</span> = data/train.txt <span class="pl-k">valid</span> = data/test.txt <span class="pl-k">names</span> = data/obj.names <span class="pl-k">backup</span> = backup/</pre></div> <ol start="4" dir="auto"> <li> <p dir="auto">Put image-files (.jpg) of your objects in the directory <code>build\darknet\x64\data\obj\</code></p> </li> <li> <p dir="auto">You should label each object on images from your dataset. Use this visual GUI-software for marking bounded boxes of objects and generating annotation files for Yolo v2 & v3: <a href="https://github.com/AlexeyAB/Yolo_mark">https://github.com/AlexeyAB/Yolo_mark</a></p> </li> </ol> <p dir="auto">It will create <code>.txt</code>-file for each <code>.jpg</code>-image-file - in the same directory and with the same name, but with <code>.txt</code>-extension, and put to file: object number and object coordinates on this image, for each object in new line:</p> <p dir="auto"><code><object-class> <x_center> <y_center> <width> <height></code></p> <p dir="auto">Where:</p> <ul dir="auto"> <li><code><object-class></code> - integer object number from <code>0</code> to <code>(classes-1)</code></li> <li><code><x_center> <y_center> <width> <height></code> - float values <strong>relative</strong> to width and height of image, it can be equal from <code>(0.0 to 1.0]</code></li> <li>for example: <code><x> = <absolute_x> / <image_width></code> or <code><height> = <absolute_height> / <image_height></code></li> <li>atention: <code><x_center> <y_center></code> - are center of rectangle (are not top-left corner)</li> </ul> <p dir="auto">For example for <code>img1.jpg</code> you will be created <code>img1.txt</code> containing:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="1 0.716797 0.395833 0.216406 0.147222 0 0.687109 0.379167 0.255469 0.158333 1 0.420312 0.395833 0.140625 0.166667"><pre class="notranslate"><code>1 0.716797 0.395833 0.216406 0.147222 0 0.687109 0.379167 0.255469 0.158333 1 0.420312 0.395833 0.140625 0.166667 </code></pre></div> <ol start="6" dir="auto"> <li>Create file <code>train.txt</code> in directory <code>build\darknet\x64\data\</code>, with filenames of your images, each filename in new line, with path relative to <code>darknet.exe</code>, for example containing:</li> </ol> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="data/obj/img1.jpg data/obj/img2.jpg data/obj/img3.jpg"><pre class="notranslate"><code>data/obj/img1.jpg data/obj/img2.jpg data/obj/img3.jpg </code></pre></div> <ol start="7" dir="auto"> <li> <p dir="auto">Download pre-trained weights for the convolutional layers and put to the directory <code>build\darknet\x64</code></p> <ul dir="auto"> <li>for <code>yolov4.cfg</code>, <code>yolov4-custom.cfg</code> (162 MB): <a href="https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137">yolov4.conv.137</a> (Google drive mirror <a href="https://drive.google.com/open?id=1JKF-bdIklxOOVy-2Cr5qdvjgGpmGfcbp" rel="nofollow">yolov4.conv.137</a> )</li> <li>for <code>yolov4-tiny.cfg</code>, <code>yolov4-tiny-3l.cfg</code>, <code>yolov4-tiny-custom.cfg</code> (19 MB): <a href="https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29">yolov4-tiny.conv.29</a></li> <li>for <code>csresnext50-panet-spp.cfg</code> (133 MB): <a href="https://drive.google.com/file/d/16yMYCLQTY_oDlCIZPfn_sab6KD3zgzGq/view?usp=sharing" rel="nofollow">csresnext50-panet-spp.conv.112</a></li> <li>for <code>yolov3.cfg, yolov3-spp.cfg</code> (154 MB): <a href="https://pjreddie.com/media/files/darknet53.conv.74" rel="nofollow">darknet53.conv.74</a></li> <li>for <code>yolov3-tiny-prn.cfg , yolov3-tiny.cfg</code> (6 MB): <a href="https://drive.google.com/file/d/18v36esoXCh-PsOKwyP2GWrpYDptDY8Zf/view?usp=sharing" rel="nofollow">yolov3-tiny.conv.11</a></li> <li>for <code>enet-coco.cfg (EfficientNetB0-Yolov3)</code> (14 MB): <a href="https://drive.google.com/file/d/1uhh3D6RSn0ekgmsaTcl-ZW53WBaUDo6j/view?usp=sharing" rel="nofollow">enetb0-coco.conv.132</a></li> </ul> </li> <li> <p dir="auto">Start training by using the command line: <code>darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137</code></p> <p dir="auto">To train on Linux use command: <code>./darknet detector train data/obj.data yolo-obj.cfg yolov4.conv.137</code> (just use <code>./darknet</code> instead of <code>darknet.exe</code>)</p> <ul dir="auto"> <li>(file <code>yolo-obj_last.weights</code> will be saved to the <code>build\darknet\x64\backup\</code> for each 100 iterations)</li> <li>(file <code>yolo-obj_xxxx.weights</code> will be saved to the <code>build\darknet\x64\backup\</code> for each 1000 iterations)</li> <li>(to disable Loss-Window use <code>darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -dont_show</code>, if you train on computer without monitor like a cloud Amazon EC2)</li> <li>(to see the mAP & Loss-chart during training on remote server without GUI, use command <code>darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -dont_show -mjpeg_port 8090 -map</code> then open URL <code>http://ip-address:8090</code> in Chrome/Firefox browser)</li> </ul> </li> </ol> <p dir="auto">8.1. For training with mAP (mean average precisions) calculation for each 4 Epochs (set <code>valid=valid.txt</code> or <code>train.txt</code> in <code>obj.data</code> file) and run: <code>darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -map</code></p> <ol start="9" dir="auto"> <li>After training is complete - get result <code>yolo-obj_final.weights</code> from path <code>build\darknet\x64\backup\</code></li> </ol> <ul dir="auto"> <li> <p dir="auto">After each 100 iterations you can stop and later start training from this point. For example, after 2000 iterations you can stop training, and later just start training using: <code>darknet.exe detector train data/obj.data yolo-obj.cfg backup\yolo-obj_2000.weights</code></p> <p dir="auto">(in the original repository <a href="https://github.com/pjreddie/darknet">https://github.com/pjreddie/darknet</a> the weights-file is saved only once every 10 000 iterations <code>if(iterations > 1000)</code>)</p> </li> <li> <p dir="auto">Also you can get result earlier than all 45000 iterations.</p> </li> </ul> <p dir="auto"><strong>Note:</strong> If during training you see <code>nan</code> values for <code>avg</code> (loss) field - then training goes wrong, but if <code>nan</code> is in some other lines - then training goes well.</p> <p dir="auto"><strong>Note:</strong> If you changed width= or height= in your cfg-file, then new width and height must be divisible by 32.</p> <p dir="auto"><strong>Note:</strong> After training use such command for detection: <code>darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights</code></p> <p dir="auto"><strong>Note:</strong> if error <code>Out of memory</code> occurs then in <code>.cfg</code>-file you should increase <code>subdivisions=16</code>, 32 or 64: <a href="https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4">link</a></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">How to train tiny-yolo (to detect your custom objects):</h3><a id="user-content-how-to-train-tiny-yolo-to-detect-your-custom-objects" class="anchor" aria-label="Permalink: How to train tiny-yolo (to detect your custom objects):" href="#how-to-train-tiny-yolo-to-detect-your-custom-objects"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Do all the same steps as for the full yolo model as described above. With the exception of:</p> <ul dir="auto"> <li>Download file with the first 29-convolutional layers of yolov4-tiny: <a href="https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29">https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29</a> (Or get this file from yolov4-tiny.weights file by using command: <code>darknet.exe partial cfg/yolov4-tiny-custom.cfg yolov4-tiny.weights yolov4-tiny.conv.29 29</code></li> <li>Make your custom model <code>yolov4-tiny-obj.cfg</code> based on <code>cfg/yolov4-tiny-custom.cfg</code> instead of <code>yolov4.cfg</code></li> <li>Start training: <code>darknet.exe detector train data/obj.data yolov4-tiny-obj.cfg yolov4-tiny.conv.29</code></li> </ul> <p dir="auto">For training Yolo based on other models (<a href="https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/densenet201_yolo.cfg">DenseNet201-Yolo</a> or <a href="https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/resnet50_yolo.cfg">ResNet50-Yolo</a>), you can download and get pre-trained weights as showed in this file: <a href="https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/partial.cmd">https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/partial.cmd</a> If you made you custom model that isn't based on other models, then you can train it without pre-trained weights, then will be used random initial weights.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">When should I stop training:</h2><a id="user-content-when-should-i-stop-training" class="anchor" aria-label="Permalink: When should I stop training:" href="#when-should-i-stop-training"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Usually sufficient 2000 iterations for each class(object), but not less than number of training images and not less than 6000 iterations in total. But for a more precise definition when you should stop training, use the following manual:</p> <ol dir="auto"> <li>During training, you will see varying indicators of error, and you should stop when no longer decreases <strong>0.XXXXXXX avg</strong>:</li> </ol> <blockquote> <p dir="auto">Region Avg IOU: 0.798363, Class: 0.893232, Obj: 0.700808, No Obj: 0.004567, Avg Recall: 1.000000, count: 8 Region Avg IOU: 0.800677, Class: 0.892181, Obj: 0.701590, No Obj: 0.004574, Avg Recall: 1.000000, count: 8</p> <p dir="auto"><strong>9002</strong>: 0.211667, <strong>0.60730 avg</strong>, 0.001000 rate, 3.868000 seconds, 576128 images Loaded: 0.000000 seconds</p> </blockquote> <ul dir="auto"> <li><strong>9002</strong> - iteration number (number of batch)</li> <li><strong>0.60730 avg</strong> - average loss (error) - <strong>the lower, the better</strong></li> </ul> <p dir="auto">When you see that average loss <strong>0.xxxxxx avg</strong> no longer decreases at many iterations then you should stop training. The final avgerage loss can be from <code>0.05</code> (for a small model and easy dataset) to <code>3.0</code> (for a big model and a difficult dataset).</p> <p dir="auto">Or if you train with flag <code>-map</code> then you will see mAP indicator <code>Last accuracy mAP@0.5 = 18.50%</code> in the console - this indicator is better than Loss, so train while mAP increases.</p> <ol start="2" dir="auto"> <li>Once training is stopped, you should take some of last <code>.weights</code>-files from <code>darknet\build\darknet\x64\backup</code> and choose the best of them:</li> </ol> <p dir="auto">For example, you stopped training after 9000 iterations, but the best result can give one of previous weights (7000, 8000, 9000). It can happen due to overfitting. <strong>Overfitting</strong> - is case when you can detect objects on images from training-dataset, but can't detect objects on any others images. You should get weights from <strong>Early Stopping Point</strong>:</p> <p dir="auto"><a target="_blank" rel="noopener noreferrer nofollow" href="https://camo.githubusercontent.com/7d0a8add32568ac1d03f424be135f05ed3dc6616c1680bb26cb808a95a940377/68747470733a2f2f6873746f2e6f72672f66696c65732f3564632f3761652f3766612f35646337616537666164396434653365623361343834633538626663316666352e706e67"><img src="https://camo.githubusercontent.com/7d0a8add32568ac1d03f424be135f05ed3dc6616c1680bb26cb808a95a940377/68747470733a2f2f6873746f2e6f72672f66696c65732f3564632f3761652f3766612f35646337616537666164396434653365623361343834633538626663316666352e706e67" alt="Overfitting" data-canonical-src="https://hsto.org/files/5dc/7ae/7fa/5dc7ae7fad9d4e3eb3a484c58bfc1ff5.png" style="max-width: 100%;"></a></p> <p dir="auto">To get weights from Early Stopping Point:</p> <p dir="auto">2.1. At first, in your file <code>obj.data</code> you must specify the path to the validation dataset <code>valid = valid.txt</code> (format of <code>valid.txt</code> as in <code>train.txt</code>), and if you haven't validation images, just copy <code>data\train.txt</code> to <code>data\valid.txt</code>.</p> <p dir="auto">2.2 If training is stopped after 9000 iterations, to validate some of previous weights use this commands:</p> <p dir="auto">(If you use another GitHub repository, then use <code>darknet.exe detector recall</code>... instead of <code>darknet.exe detector map</code>...)</p> <ul dir="auto"> <li><code>darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights</code></li> <li><code>darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_8000.weights</code></li> <li><code>darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_9000.weights</code></li> </ul> <p dir="auto">And comapre last output lines for each weights (7000, 8000, 9000):</p> <p dir="auto">Choose weights-file <strong>with the highest mAP (mean average precision)</strong> or IoU (intersect over union)</p> <p dir="auto">For example, <strong>bigger mAP</strong> gives weights <code>yolo-obj_8000.weights</code> - then <strong>use this weights for detection</strong>.</p> <p dir="auto">Or just train with <code>-map</code> flag:</p> <p dir="auto"><code>darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -map</code></p> <p dir="auto">So you will see mAP-chart (red-line) in the Loss-chart Window. mAP will be calculated for each 4 Epochs using <code>valid=valid.txt</code> file that is specified in <code>obj.data</code> file (<code>1 Epoch = images_in_train_txt / batch</code> iterations)</p> <p dir="auto">(to change the max x-axis value - change <a href="https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L20"><code>max_batches=</code></a> parameter to <code>2000*classes</code>, f.e. <code>max_batches=6000</code> for 3 classes)</p> <p dir="auto"><a target="_blank" rel="noopener noreferrer nofollow" href="https://camo.githubusercontent.com/cbb13e27b93952d5bb294eaaf5fc078fc54082f5882bf19d08b181affdb8555a/68747470733a2f2f6873746f2e6f72672f776562742f79642f766c2f61672f7964766c616775746f66327a636e6a6f64737467726f656e3861632e6a706567"><img src="https://camo.githubusercontent.com/cbb13e27b93952d5bb294eaaf5fc078fc54082f5882bf19d08b181affdb8555a/68747470733a2f2f6873746f2e6f72672f776562742f79642f766c2f61672f7964766c616775746f66327a636e6a6f64737467726f656e3861632e6a706567" alt="loss_chart_map_chart" data-canonical-src="https://hsto.org/webt/yd/vl/ag/ydvlagutof2zcnjodstgroen8ac.jpeg" style="max-width: 100%;"></a></p> <p dir="auto">Example of custom object detection: <code>darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights</code></p> <ul dir="auto"> <li> <p dir="auto"><strong>IoU</strong> (intersect over union) - average instersect over union of objects and detections for a certain threshold = 0.24</p> </li> <li> <p dir="auto"><strong>mAP</strong> (mean average precision) - mean value of <code>average precisions</code> for each class, where <code>average precision</code> is average value of 11 points on PR-curve for each possible threshold (each probability of detection) for the same class (Precision-Recall in terms of PascalVOC, where Precision=TP/(TP+FP) and Recall=TP/(TP+FN) ), page-11: <a href="http://homepages.inf.ed.ac.uk/ckiw/postscript/ijcv_voc09.pdf" rel="nofollow">http://homepages.inf.ed.ac.uk/ckiw/postscript/ijcv_voc09.pdf</a></p> </li> </ul> <p dir="auto"><strong>mAP</strong> is default metric of precision in the PascalVOC competition, <strong>this is the same as AP50</strong> metric in the MS COCO competition. In terms of Wiki, indicators Precision and Recall have a slightly different meaning than in the PascalVOC competition, but <strong>IoU always has the same meaning</strong>.</p> <p dir="auto"><a target="_blank" rel="noopener noreferrer nofollow" href="https://camo.githubusercontent.com/c0aeed32ee93ce4ad00cadb1aee8a38cdf94a5e3a8156d73fba735731acb4685/68747470733a2f2f6873746f2e6f72672f66696c65732f6361382f3836362f6437362f63613838363664373666623834303232383934306462663434326137663036612e6a7067"><img src="https://camo.githubusercontent.com/c0aeed32ee93ce4ad00cadb1aee8a38cdf94a5e3a8156d73fba735731acb4685/68747470733a2f2f6873746f2e6f72672f66696c65732f6361382f3836362f6437362f63613838363664373666623834303232383934306462663434326137663036612e6a7067" alt="precision_recall_iou" data-canonical-src="https://hsto.org/files/ca8/866/d76/ca8866d76fb840228940dbf442a7f06a.jpg" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Custom object detection:</h3><a id="user-content-custom-object-detection" class="anchor" aria-label="Permalink: Custom object detection:" href="#custom-object-detection"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Example of custom object detection: <code>darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights</code></p> <markdown-accessiblity-table><table> <thead> <tr> <th><a target="_blank" rel="noopener noreferrer nofollow" href="https://camo.githubusercontent.com/fcf258e7152f8cd43fb21d3d48b035c706d4d9ab1e68863407bcf1993b13986e/68747470733a2f2f6873746f2e6f72672f66696c65732f6431322f3165372f3531352f64313231653735313566366134656236393439313366313064653566326236312e6a7067"><img src="https://camo.githubusercontent.com/fcf258e7152f8cd43fb21d3d48b035c706d4d9ab1e68863407bcf1993b13986e/68747470733a2f2f6873746f2e6f72672f66696c65732f6431322f3165372f3531352f64313231653735313566366134656236393439313366313064653566326236312e6a7067" alt="Yolo_v2_training" data-canonical-src="https://hsto.org/files/d12/1e7/515/d121e7515f6a4eb694913f10de5f2b61.jpg" style="max-width: 100%;"></a></th> <th><a target="_blank" rel="noopener noreferrer nofollow" href="https://camo.githubusercontent.com/d717c6a3bfa2de3a74059e81925da710b4bdfc5bb6fc33120a03abcf22fdfa93/68747470733a2f2f6873746f2e6f72672f66696c65732f3732372f6337652f3565392f37323763376535653939626634643461613334303237626236613565346261622e6a7067"><img src="https://camo.githubusercontent.com/d717c6a3bfa2de3a74059e81925da710b4bdfc5bb6fc33120a03abcf22fdfa93/68747470733a2f2f6873746f2e6f72672f66696c65732f3732372f6337652f3565392f37323763376535653939626634643461613334303237626236613565346261622e6a7067" alt="Yolo_v2_training" data-canonical-src="https://hsto.org/files/727/c7e/5e9/727c7e5e99bf4d4aa34027bb6a5e4bab.jpg" style="max-width: 100%;"></a></th> </tr> </thead> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">How to improve object detection:</h2><a id="user-content-how-to-improve-object-detection" class="anchor" aria-label="Permalink: How to improve object detection:" href="#how-to-improve-object-detection"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li>Before training:</li> </ol> <ul dir="auto"> <li> <p dir="auto">set flag <code>random=1</code> in your <code>.cfg</code>-file - it will increase precision by training Yolo for different resolutions: <a href="https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L788">link</a></p> </li> <li> <p dir="auto">increase network resolution in your <code>.cfg</code>-file (<code>height=608</code>, <code>width=608</code> or any value multiple of 32) - it will increase precision</p> </li> <li> <p dir="auto">check that each object that you want to detect is mandatory labeled in your dataset - no one object in your data set should not be without label. In the most training issues - there are wrong labels in your dataset (got labels by using some conversion script, marked with a third-party tool, ...). Always check your dataset by using: <a href="https://github.com/AlexeyAB/Yolo_mark">https://github.com/AlexeyAB/Yolo_mark</a></p> </li> <li> <p dir="auto">my Loss is very high and mAP is very low, is training wrong? Run training with <code> -show_imgs</code> flag at the end of training command, do you see correct bounded boxes of objects (in windows or in files <code>aug_...jpg</code>)? If no - your training dataset is wrong.</p> </li> <li> <p dir="auto">for each object which you want to detect - there must be at least 1 similar object in the Training dataset with about the same: shape, side of object, relative size, angle of rotation, tilt, illumination. So desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 different images for each class or more, and you should train <code>2000*classes</code> iterations or more</p> </li> <li> <p dir="auto">desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box (empty <code>.txt</code> files) - use as many images of negative samples as there are images with objects</p> </li> <li> <p dir="auto">What is the best way to mark objects: label only the visible part of the object, or label the visible and overlapped part of the object, or label a little more than the entire object (with a little gap)? Mark as you like - how would you like it to be detected.</p> </li> <li> <p dir="auto">for training with a large number of objects in each image, add the parameter <code>max=200</code> or higher value in the last <code>[yolo]</code>-layer or <code>[region]</code>-layer in your cfg-file (the global maximum number of objects that can be detected by YoloV3 is <code>0,0615234375*(width*height)</code> where are width and height are parameters from <code>[net]</code> section in cfg-file)</p> </li> <li> <p dir="auto">for training for small objects (smaller than 16x16 after the image is resized to 416x416) - set <code>layers = 23</code> instead of <a href="https://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L895">https://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L895</a></p> <ul dir="auto"> <li>set <code>stride=4</code> instead of <a href="https://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L892">https://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L892</a></li> <li>set <code>stride=4</code> instead of <a href="https://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L989">https://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L989</a></li> </ul> </li> <li> <p dir="auto">for training for both small and large objects use modified models:</p> <ul dir="auto"> <li>Full-model: 5 yolo layers: <a href="https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3_5l.cfg" rel="nofollow">https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3_5l.cfg</a></li> <li>Tiny-model: 3 yolo layers: <a href="https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny_3l.cfg" rel="nofollow">https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny_3l.cfg</a></li> <li>YOLOv4: 3 yolo layers: <a href="https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-custom.cfg" rel="nofollow">https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-custom.cfg</a></li> </ul> </li> <li> <p dir="auto">If you train the model to distinguish Left and Right objects as separate classes (left/right hand, left/right-turn on road signs, ...) then for disabling flip data augmentation - add <code>flip=0</code> here: <a href="https://github.com/AlexeyAB/darknet/blob/3d2d0a7c98dbc8923d9ff705b81ff4f7940ea6ff/cfg/yolov3.cfg#L17">https://github.com/AlexeyAB/darknet/blob/3d2d0a7c98dbc8923d9ff705b81ff4f7940ea6ff/cfg/yolov3.cfg#L17</a></p> </li> <li> <p dir="auto">General rule - your training dataset should include such a set of relative sizes of objects that you want to detect:</p> <ul dir="auto"> <li><code>train_network_width * train_obj_width / train_image_width ~= detection_network_width * detection_obj_width / detection_image_width</code></li> <li><code>train_network_height * train_obj_height / train_image_height ~= detection_network_height * detection_obj_height / detection_image_height</code></li> </ul> <p dir="auto">I.e. for each object from Test dataset there must be at least 1 object in the Training dataset with the same class_id and about the same relative size:</p> <p dir="auto"><code>object width in percent from Training dataset</code> ~= <code>object width in percent from Test dataset</code></p> <p dir="auto">That is, if only objects that occupied 80-90% of the image were present in the training set, then the trained network will not be able to detect objects that occupy 1-10% of the image.</p> </li> <li> <p dir="auto">to speedup training (with decreasing detection accuracy) set param <code>stopbackward=1</code> for layer-136 in cfg-file</p> </li> <li> <p dir="auto">each: <code>model of object, side, illimination, scale, each 30 grad</code> of the turn and inclination angles - these are <em>different objects</em> from an internal perspective of the neural network. So the more <em>different objects</em> you want to detect, the more complex network model should be used.</p> </li> <li> <p dir="auto">to make the detected bounded boxes more accurate, you can add 3 parameters <code>ignore_thresh = .9 iou_normalizer=0.5 iou_loss=giou</code> to each <code>[yolo]</code> layer and train, it will increase mAP@0.9, but decrease mAP@0.5.</p> </li> <li> <p dir="auto">Only if you are an <strong>expert</strong> in neural detection networks - recalculate anchors for your dataset for <code>width</code> and <code>height</code> from cfg-file: <code>darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416</code> then set the same 9 <code>anchors</code> in each of 3 <code>[yolo]</code>-layers in your cfg-file. But you should change indexes of anchors <code>masks=</code> for each [yolo]-layer, so for YOLOv4 the 1st-[yolo]-layer has anchors smaller than 30x30, 2nd smaller than 60x60, 3rd remaining, and vice versa for YOLOv3. Also you should change the <code>filters=(classes + 5)*<number of mask></code> before each [yolo]-layer. If many of the calculated anchors do not fit under the appropriate layers - then just try using all the default anchors.</p> </li> </ul> <ol start="2" dir="auto"> <li>After training - for detection:</li> </ol> <ul dir="auto"> <li> <p dir="auto">Increase network-resolution by set in your <code>.cfg</code>-file (<code>height=608</code> and <code>width=608</code>) or (<code>height=832</code> and <code>width=832</code>) or (any value multiple of 32) - this increases the precision and makes it possible to detect small objects: <a href="https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L8-L9">link</a></p> </li> <li> <p dir="auto">it is not necessary to train the network again, just use <code>.weights</code>-file already trained for 416x416 resolution</p> </li> <li> <p dir="auto">to get even greater accuracy you should train with higher resolution 608x608 or 832x832, note: if error <code>Out of memory</code> occurs then in <code>.cfg</code>-file you should increase <code>subdivisions=16</code>, 32 or 64: <a href="https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4">link</a></p> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">How to mark bounded boxes of objects and create annotation files:</h2><a id="user-content-how-to-mark-bounded-boxes-of-objects-and-create-annotation-files" class="anchor" aria-label="Permalink: How to mark bounded boxes of objects and create annotation files:" href="#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Here you can find repository with GUI-software for marking bounded boxes of objects and generating annotation files for Yolo v2 - v4: <a href="https://github.com/AlexeyAB/Yolo_mark">https://github.com/AlexeyAB/Yolo_mark</a></p> <p dir="auto">With example of: <code>train.txt</code>, <code>obj.names</code>, <code>obj.data</code>, <code>yolo-obj.cfg</code>, <code>air</code>1-6<code>.txt</code>, <code>bird</code>1-4<code>.txt</code> for 2 classes of objects (air, bird) and <code>train_obj.cmd</code> with example how to train this image-set with Yolo v2 - v4</p> <p dir="auto">Different tools for marking objects in images:</p> <ol dir="auto"> <li>in C++: <a href="https://github.com/AlexeyAB/Yolo_mark">https://github.com/AlexeyAB/Yolo_mark</a></li> <li>in Python: <a href="https://github.com/tzutalin/labelImg">https://github.com/tzutalin/labelImg</a></li> <li>in Python: <a href="https://github.com/Cartucho/OpenLabeling">https://github.com/Cartucho/OpenLabeling</a></li> <li>in C++: <a href="https://www.ccoderun.ca/darkmark/" rel="nofollow">https://www.ccoderun.ca/darkmark/</a></li> <li>in JavaScript: <a href="https://github.com/opencv/cvat">https://github.com/opencv/cvat</a></li> <li>in C++: <a href="https://github.com/jveitchmichaelis/deeplabel">https://github.com/jveitchmichaelis/deeplabel</a></li> <li>in C#: <a href="https://github.com/BMW-InnovationLab/BMW-Labeltool-Lite">https://github.com/BMW-InnovationLab/BMW-Labeltool-Lite</a></li> <li>DL-Annotator for Windows ($30): <a href="https://www.microsoft.com/en-us/p/dlannotator/9nsx79m7t8fn?activetab=pivot:overviewtab" rel="nofollow">url</a></li> <li>v7labs - the greatest cloud labeling tool ($1.5 per hour): <a href="https://www.v7labs.com/" rel="nofollow">https://www.v7labs.com/</a></li> </ol> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">How to use Yolo as DLL and SO libraries</h2><a id="user-content-how-to-use-yolo-as-dll-and-so-libraries" class="anchor" aria-label="Permalink: How to use Yolo as DLL and SO libraries" href="#how-to-use-yolo-as-dll-and-so-libraries"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>on Linux <ul dir="auto"> <li>using <code>build.sh</code> or</li> <li>build <code>darknet</code> using <code>cmake</code> or</li> <li>set <code>LIBSO=1</code> in the <code>Makefile</code> and do <code>make</code></li> </ul> </li> <li>on Windows <ul dir="auto"> <li>using <code>build.ps1</code> or</li> <li>build <code>darknet</code> using <code>cmake</code> or</li> <li>compile <code>build\darknet\yolo_cpp_dll.sln</code> solution or <code>build\darknet\yolo_cpp_dll_no_gpu.sln</code> solution</li> </ul> </li> </ul> <p dir="auto">There are 2 APIs:</p> <ul dir="auto"> <li> <p dir="auto">C API: <a href="https://github.com/AlexeyAB/darknet/blob/master/include/darknet.h">https://github.com/AlexeyAB/darknet/blob/master/include/darknet.h</a></p> <ul dir="auto"> <li>Python examples using the C API: <ul dir="auto"> <li><a href="https://github.com/AlexeyAB/darknet/blob/master/darknet.py">https://github.com/AlexeyAB/darknet/blob/master/darknet.py</a></li> <li><a href="https://github.com/AlexeyAB/darknet/blob/master/darknet_video.py">https://github.com/AlexeyAB/darknet/blob/master/darknet_video.py</a></li> </ul> </li> </ul> </li> <li> <p dir="auto">C++ API: <a href="https://github.com/AlexeyAB/darknet/blob/master/include/yolo_v2_class.hpp">https://github.com/AlexeyAB/darknet/blob/master/include/yolo_v2_class.hpp</a></p> <ul dir="auto"> <li>C++ example that uses C++ API: <a href="https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp">https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp</a></li> </ul> </li> </ul> <hr> <ol dir="auto"> <li> <p dir="auto">To compile Yolo as C++ DLL-file <code>yolo_cpp_dll.dll</code> - open the solution <code>build\darknet\yolo_cpp_dll.sln</code>, set <strong>x64</strong> and <strong>Release</strong>, and do the: Build -> Build yolo_cpp_dll</p> <ul dir="auto"> <li>You should have installed <strong>CUDA 10.0</strong></li> <li>To use cuDNN do: (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add at the beginning of line: <code>CUDNN;</code></li> </ul> </li> <li> <p dir="auto">To use Yolo as DLL-file in your C++ console application - open the solution <code>build\darknet\yolo_console_dll.sln</code>, set <strong>x64</strong> and <strong>Release</strong>, and do the: Build -> Build yolo_console_dll</p> <ul dir="auto"> <li> <p dir="auto">you can run your console application from Windows Explorer <code>build\darknet\x64\yolo_console_dll.exe</code> <strong>use this command</strong>: <code>yolo_console_dll.exe data/coco.names yolov4.cfg yolov4.weights test.mp4</code></p> </li> <li> <p dir="auto">after launching your console application and entering the image file name - you will see info for each object: <code><obj_id> <left_x> <top_y> <width> <height> <probability></code></p> </li> <li> <p dir="auto">to use simple OpenCV-GUI you should uncomment line <code>//#define OPENCV</code> in <code>yolo_console_dll.cpp</code>-file: <a href="https://github.com/AlexeyAB/darknet/blob/a6cbaeecde40f91ddc3ea09aa26a03ab5bbf8ba8/src/yolo_console_dll.cpp#L5">link</a></p> </li> <li> <p dir="auto">you can see source code of simple example for detection on the video file: <a href="https://github.com/AlexeyAB/darknet/blob/ab1c5f9e57b4175f29a6ef39e7e68987d3e98704/src/yolo_console_dll.cpp#L75">link</a></p> </li> </ul> </li> </ol> <p dir="auto"><code>yolo_cpp_dll.dll</code>-API: <a href="https://github.com/AlexeyAB/darknet/blob/master/src/yolo_v2_class.hpp#L42">link</a></p> <div class="highlight highlight-source-c++ notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="struct bbox_t { unsigned int x, y, w, h; // (x,y) - top-left corner, (w, h) - width & height of bounded box float prob; // confidence - probability that the object was found correctly unsigned int obj_id; // class of object - from range [0, classes-1] unsigned int track_id; // tracking id for video (0 - untracked, 1 - inf - tracked object) unsigned int frames_counter;// counter of frames on which the object was detected }; class Detector { public: Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0); ~Detector(); std::vector<bbox_t> detect(std::string image_filename, float thresh = 0.2, bool use_mean = false); std::vector<bbox_t> detect(image_t img, float thresh = 0.2, bool use_mean = false); static image_t load_image(std::string image_filename); static void free_image(image_t m); #ifdef OPENCV std::vector<bbox_t> detect(cv::Mat mat, float thresh = 0.2, bool use_mean = false); std::shared_ptr<image_t> mat_to_image_resize(cv::Mat mat) const; #endif };"><pre><span class="pl-k">struct</span> <span class="pl-en">bbox_t</span> { <span class="pl-k">unsigned</span> <span class="pl-k">int</span> x, y, w, h; <span class="pl-c"><span class="pl-c">//</span> (x,y) - top-left corner, (w, h) - width & height of bounded box</span> <span class="pl-k">float</span> prob; <span class="pl-c"><span class="pl-c">//</span> confidence - probability that the object was found correctly</span> <span class="pl-k">unsigned</span> <span class="pl-k">int</span> obj_id; <span class="pl-c"><span class="pl-c">//</span> class of object - from range [0, classes-1]</span> <span class="pl-k">unsigned</span> <span class="pl-k">int</span> track_id; <span class="pl-c"><span class="pl-c">//</span> tracking id for video (0 - untracked, 1 - inf - tracked object)</span> <span class="pl-k">unsigned</span> <span class="pl-k">int</span> frames_counter;<span class="pl-c"><span class="pl-c">//</span> counter of frames on which the object was detected</span> }; <span class="pl-k">class</span> <span class="pl-en">Detector</span> { <span class="pl-k">public:</span> <span class="pl-en">Detector</span>(std::string cfg_filename, std::string weight_filename, <span class="pl-k">int</span> gpu_id = <span class="pl-c1">0</span>); <span class="pl-en">~Detector</span>(); std::vector<<span class="pl-c1">bbox_t</span>> <span class="pl-en">detect</span>(std::string image_filename, <span class="pl-k">float</span> thresh = <span class="pl-c1">0.2</span>, <span class="pl-k">bool</span> use_mean = <span class="pl-c1">false</span>); std::vector<<span class="pl-c1">bbox_t</span>> <span class="pl-en">detect</span>(<span class="pl-c1">image_t</span> img, <span class="pl-k">float</span> thresh = <span class="pl-c1">0.2</span>, <span class="pl-k">bool</span> use_mean = <span class="pl-c1">false</span>); <span class="pl-k">static</span> <span class="pl-c1">image_t</span> <span class="pl-en">load_image</span>(std::string image_filename); <span class="pl-k">static</span> <span class="pl-k">void</span> <span class="pl-en">free_image</span>(<span class="pl-c1">image_t</span> m); #<span class="pl-k">ifdef</span> OPENCV std::vector<<span class="pl-c1">bbox_t</span>> <span class="pl-en">detect</span>(cv::Mat mat, <span class="pl-k">float</span> thresh = <span class="pl-c1">0.2</span>, <span class="pl-k">bool</span> use_mean = <span class="pl-c1">false</span>); std::shared_ptr<<span class="pl-c1">image_t</span>> <span class="pl-en">mat_to_image_resize</span>(cv::Mat mat) <span class="pl-k">const</span>; #<span class="pl-k">endif</span> };</pre></div> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="srMIvZbaqmSPxqxNsI+BqBPic/PHvpZYe0dRJdRWpYs2fVlbYdmFyj+yGrOQcGIoB0P3EMRlhG3OcUVfU5z6pQ==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> Hybrid MSRM-based deep learning and multitemporal Sentinel-2-based machine learning algorithm detects near 10k archaeological tumuli in North-western Iberia </p> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:machine-learning" href="/topics/machine-learning" title="Topic: machine-learning" data-view-component="true" class="topic-tag topic-tag-link"> machine-learning </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:deep-learning" href="/topics/deep-learning" title="Topic: deep-learning" data-view-component="true" class="topic-tag topic-tag-link"> deep-learning </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:archaeology" href="/topics/archaeology" title="Topic: archaeology" data-view-component="true" class="topic-tag topic-tag-link"> archaeology </a> <a 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9.576.43 8.898a1.62 1.62 0 0 1 0-1.798c.45-.677 1.367-1.931 2.637-3.022C4.33 2.992 6.019 2 8 2ZM1.679 7.932a.12.12 0 0 0 0 .136c.411.622 1.241 1.75 2.366 2.717C5.176 11.758 6.527 12.5 8 12.5c1.473 0 2.825-.742 3.955-1.715 1.124-.967 1.954-2.096 2.366-2.717a.12.12 0 0 0 0-.136c-.412-.621-1.242-1.75-2.366-2.717C10.824 4.242 9.473 3.5 8 3.5c-1.473 0-2.825.742-3.955 1.715-1.124.967-1.954 2.096-2.366 2.717ZM8 10a2 2 0 1 1-.001-3.999A2 2 0 0 1 8 10Z"></path> </svg> <strong>1</strong> watching </a> </div> <h3 class="sr-only">Forks</h3> <div class="mt-2"> <a href="/iberganzo/darknet/forks" data-view-component="true" class="Link Link--muted"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo-forked mr-2"> <path d="M5 5.372v.878c0 .414.336.75.75.75h4.5a.75.75 0 0 0 .75-.75v-.878a2.25 2.25 0 1 1 1.5 0v.878a2.25 2.25 0 0 1-2.25 2.25h-1.5v2.128a2.251 2.251 0 1 1-1.5 0V8.5h-1.5A2.25 2.25 0 0 1 3.5 6.25v-.878a2.25 2.25 0 1 1 1.5 0ZM5 3.25a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Zm6.75.75a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5Zm-3 8.75a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Z"></path> </svg> <strong>2</strong> forks </a> </div> <div class="mt-2"> <a class="Link--muted" href="/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2Fiberganzo%2Fdarknet&report=iberganzo+%28user%29"> Report repository </a> </div> </div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame"> <a href="/iberganzo/darknet/releases" data-view-component="true" class="Link--primary no-underline Link"> Releases </a></h2> <div class="text-small color-fg-muted">No releases published</div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3">Sponsor this project</h2> <include-fragment src="/iberganzo/darknet/sponsors_list?block_button=false&current_repository=darknet" aria-busy="true" aria-label="Loading sponsorable links"> <ul class="list-style-none"> </ul> </include-fragment> <ul class="list-style-none"> <li class="mb-2 d-flex"> <span class="mr-2 d-flex flex-items-center flex-justify-center" style="min-width:32px;height:32px;"> <svg class="octicon octicon-link color-fg-muted" alt="custom" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg> </span> <span class="flex-self-center flex-auto min-width-0 css-truncate css-truncate-target width-fit"> <a target="_blank" data-ga-click="Dashboard, click, Nav menu - item:org-profile context:organization" data-hydro-click="{"event_type":"sponsors.repo_funding_links_link_click","payload":{"platform":{"platform_type":"CUSTOM","platform_url":"https://paypal.me/alexeyab84"},"platforms":[{"platform_type":"CUSTOM","platform_url":"https://paypal.me/alexeyab84"},{"platform_type":"CUSTOM","platform_url":"https://blockchain.coinmarketcap.com/address/bitcoin/36La9T7DoLVMrUQzm6rBDGsxutyvDzbHnp"},{"platform_type":"CUSTOM","platform_url":"https://etherscan.io/address/0x193d56BE3C65e3Fb8f48c291B17C0702e211A588#"},{"platform_type":"CUSTOM","platform_url":"https://explorer.zcha.in/accounts/t1PzwJ28Prb7Nk8fgfT3RXCr6Xtw54tgjoy"}],"repo_id":320218744,"owner_id":75735764,"user_id":null,"originating_url":"https://github.com/iberganzo/darknet"}}" data-hydro-click-hmac="d82b0e6895d549bbb6f1038079b988d4cbc6d7322e6a36a0df68cddde039ba0e" rel="noopener noreferrer" href="https://paypal.me/alexeyab84">https://paypal.me/alexeyab84</a> </span> </li> <li class="mb-2 d-flex"> <span class="mr-2 d-flex flex-items-center flex-justify-center" style="min-width:32px;height:32px;"> <svg class="octicon octicon-link color-fg-muted" alt="custom" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg> </span> <span class="flex-self-center flex-auto min-width-0 css-truncate css-truncate-target width-fit"> <a target="_blank" data-ga-click="Dashboard, click, Nav menu - item:org-profile context:organization" data-hydro-click="{"event_type":"sponsors.repo_funding_links_link_click","payload":{"platform":{"platform_type":"CUSTOM","platform_url":"https://blockchain.coinmarketcap.com/address/bitcoin/36La9T7DoLVMrUQzm6rBDGsxutyvDzbHnp"},"platforms":[{"platform_type":"CUSTOM","platform_url":"https://paypal.me/alexeyab84"},{"platform_type":"CUSTOM","platform_url":"https://blockchain.coinmarketcap.com/address/bitcoin/36La9T7DoLVMrUQzm6rBDGsxutyvDzbHnp"},{"platform_type":"CUSTOM","platform_url":"https://etherscan.io/address/0x193d56BE3C65e3Fb8f48c291B17C0702e211A588#"},{"platform_type":"CUSTOM","platform_url":"https://explorer.zcha.in/accounts/t1PzwJ28Prb7Nk8fgfT3RXCr6Xtw54tgjoy"}],"repo_id":320218744,"owner_id":75735764,"user_id":null,"originating_url":"https://github.com/iberganzo/darknet"}}" data-hydro-click-hmac="e7dd28c11a90f473f80c9b6c1429d474f73f7d8a47d563702342774dd66eecc7" rel="noopener noreferrer" href="https://blockchain.coinmarketcap.com/address/bitcoin/36La9T7DoLVMrUQzm6rBDGsxutyvDzbHnp">https://blockchain.coinmarketcap.com/address/bitcoin/36La9T7DoLVMrUQzm6rBDGsxutyvDzbHnp</a> </span> </li> <li class="mb-2 d-flex"> <span class="mr-2 d-flex flex-items-center flex-justify-center" style="min-width:32px;height:32px;"> <svg class="octicon octicon-link color-fg-muted" alt="custom" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg> </span> <span class="flex-self-center flex-auto min-width-0 css-truncate css-truncate-target width-fit"> <a target="_blank" data-ga-click="Dashboard, click, Nav menu - item:org-profile context:organization" data-hydro-click="{"event_type":"sponsors.repo_funding_links_link_click","payload":{"platform":{"platform_type":"CUSTOM","platform_url":"https://etherscan.io/address/0x193d56BE3C65e3Fb8f48c291B17C0702e211A588#"},"platforms":[{"platform_type":"CUSTOM","platform_url":"https://paypal.me/alexeyab84"},{"platform_type":"CUSTOM","platform_url":"https://blockchain.coinmarketcap.com/address/bitcoin/36La9T7DoLVMrUQzm6rBDGsxutyvDzbHnp"},{"platform_type":"CUSTOM","platform_url":"https://etherscan.io/address/0x193d56BE3C65e3Fb8f48c291B17C0702e211A588#"},{"platform_type":"CUSTOM","platform_url":"https://explorer.zcha.in/accounts/t1PzwJ28Prb7Nk8fgfT3RXCr6Xtw54tgjoy"}],"repo_id":320218744,"owner_id":75735764,"user_id":null,"originating_url":"https://github.com/iberganzo/darknet"}}" data-hydro-click-hmac="8e3df09647f19972ddfdd1a5f749d9639274eada607e833d922acd6af6af9a14" rel="noopener noreferrer" href="https://etherscan.io/address/0x193d56BE3C65e3Fb8f48c291B17C0702e211A588#">https://etherscan.io/address/0x193d56BE3C65e3Fb8f48c291B17C0702e211A588#</a> </span> </li> <li class="mb-2 d-flex"> <span class="mr-2 d-flex flex-items-center flex-justify-center" style="min-width:32px;height:32px;"> <svg class="octicon octicon-link color-fg-muted" alt="custom" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg> </span> <span class="flex-self-center flex-auto min-width-0 css-truncate css-truncate-target width-fit"> <a target="_blank" data-ga-click="Dashboard, click, Nav menu - item:org-profile context:organization" data-hydro-click="{"event_type":"sponsors.repo_funding_links_link_click","payload":{"platform":{"platform_type":"CUSTOM","platform_url":"https://explorer.zcha.in/accounts/t1PzwJ28Prb7Nk8fgfT3RXCr6Xtw54tgjoy"},"platforms":[{"platform_type":"CUSTOM","platform_url":"https://paypal.me/alexeyab84"},{"platform_type":"CUSTOM","platform_url":"https://blockchain.coinmarketcap.com/address/bitcoin/36La9T7DoLVMrUQzm6rBDGsxutyvDzbHnp"},{"platform_type":"CUSTOM","platform_url":"https://etherscan.io/address/0x193d56BE3C65e3Fb8f48c291B17C0702e211A588#"},{"platform_type":"CUSTOM","platform_url":"https://explorer.zcha.in/accounts/t1PzwJ28Prb7Nk8fgfT3RXCr6Xtw54tgjoy"}],"repo_id":320218744,"owner_id":75735764,"user_id":null,"originating_url":"https://github.com/iberganzo/darknet"}}" data-hydro-click-hmac="92ffd06beb0fc9d06f862beaf6807ef661ec36b573ad0e371e28d71207675d86" rel="noopener noreferrer" href="https://explorer.zcha.in/accounts/t1PzwJ28Prb7Nk8fgfT3RXCr6Xtw54tgjoy">https://explorer.zcha.in/accounts/t1PzwJ28Prb7Nk8fgfT3RXCr6Xtw54tgjoy</a> </span> </li> </ul> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/users/iberganzo/packages?repo_name=darknet" data-view-component="true" class="Link--primary no-underline Link d-flex flex-items-center"> Packages <span title="0" hidden="hidden" data-view-component="true" class="Counter ml-1">0</span> </a></h2> <div class="text-small color-fg-muted" > No packages published <br> </div> </div> </div> <div class="BorderGrid-row" hidden> <div class="BorderGrid-cell"> <include-fragment src="/iberganzo/darknet/used_by_list" accept="text/fragment+html"> </include-fragment> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3">Languages</h2> <div class="mb-2"> <span data-view-component="true" class="Progress"> <span style="background-color:#555555 !important;;width: 64.4%;" itemprop="keywords" aria-label="C 64.4" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#3A4E3A !important;;width: 14.9%;" itemprop="keywords" aria-label="Cuda 14.9" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#f34b7d !important;;width: 12.8%;" itemprop="keywords" aria-label="C++ 12.8" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#3572A5 !important;;width: 4.5%;" itemprop="keywords" aria-label="Python 4.5" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#DA3434 !important;;width: 1.4%;" itemprop="keywords" aria-label="CMake 1.4" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#C1F12E !important;;width: 0.6%;" itemprop="keywords" aria-label="Batchfile 0.6" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#ededed !important;;width: 1.4%;" itemprop="keywords" aria-label="Other 1.4" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> </span></div> <ul class="list-style-none"> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/iberganzo/darknet/search?l=c" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#555555;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">C</span> <span>64.4%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/iberganzo/darknet/search?l=cuda" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#3A4E3A;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Cuda</span> <span>14.9%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/iberganzo/darknet/search?l=c%2B%2B" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#f34b7d;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">C++</span> <span>12.8%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/iberganzo/darknet/search?l=python" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#3572A5;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Python</span> <span>4.5%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/iberganzo/darknet/search?l=cmake" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#DA3434;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">CMake</span> <span>1.4%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/iberganzo/darknet/search?l=batchfile" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#C1F12E;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Batchfile</span> <span>0.6%</span> </a> </li> <li class="d-inline"> <span class="d-inline-flex flex-items-center flex-nowrap text-small mr-3"> <svg style="color:#ededed;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Other</span> <span>1.4%</span> </span> </li> </ul> </div> </div> </div> </div> </div></div> </div> </div> </turbo-frame> </main> </div> </div> <footer class="footer pt-8 pb-6 f6 color-fg-muted p-responsive" role="contentinfo" > <h2 class='sr-only'>Footer</h2> <div class="d-flex flex-justify-center flex-items-center flex-column-reverse flex-lg-row flex-wrap flex-lg-nowrap"> <div class="d-flex flex-items-center flex-shrink-0 mx-2"> <a aria-label="Homepage" title="GitHub" class="footer-octicon mr-2" href="https://github.com"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-mark-github"> <path d="M12.5.75C6.146.75 1 5.896 1 12.25c0 5.089 3.292 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1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>