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GitHub - LSH9832/edgeyolo: an edge-real-time anchor-free object detector with decent performance
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0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{"location":"navbar","action":"github_skills","context":"product","tag":"link","label":"github_skills_link_product_navbar"}" href="https://skills.github.com"> GitHub Skills <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 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</div> </modal-dialog></div> </div> <div data-action="click:qbsearch-input#retract" class="dark-backdrop position-fixed" hidden data-target="qbsearch-input.darkBackdrop"></div> <div class="color-fg-default"> <dialog-helper> <dialog data-target="qbsearch-input.feedbackDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="feedback-dialog" aria-modal="true" aria-labelledby="feedback-dialog-title" aria-describedby="feedback-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="feedback-dialog-title"> Provide feedback </h1> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="feedback-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button 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seriously.</p> <textarea name="feedback" class="form-control width-full mb-2" style="height: 120px" id="feedback"></textarea> <input name="include_email" id="include_email" aria-label="Include my email address so I can be contacted" class="form-control mr-2" type="checkbox"> <label for="include_email" style="font-weight: normal">Include my email address so I can be contacted</label> </form></div> </scrollable-region> <div data-view-component="true" class="Overlay-footer Overlay-footer--alignEnd"> <button data-close-dialog-id="feedback-dialog" type="button" data-view-component="true" class="btn"> Cancel </button> <button form="code-search-feedback-form" data-action="click:qbsearch-input#submitFeedback" type="submit" data-view-component="true" class="btn-primary btn"> Submit feedback </button> </div> </dialog></dialog-helper> <custom-scopes data-target="qbsearch-input.customScopesManager"> <dialog-helper> <dialog data-target="custom-scopes.customScopesModalDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="custom-scopes-dialog" aria-modal="true" aria-labelledby="custom-scopes-dialog-title" aria-describedby="custom-scopes-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header Overlay-header--divided"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="custom-scopes-dialog-title"> Saved searches </h1> <h2 id="custom-scopes-dialog-description" class="Overlay-description">Use saved searches to filter your results more quickly</h2> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="custom-scopes-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button Overlay-closeButton"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg></button> </div> </div> </div> <scrollable-region data-labelled-by="custom-scopes-dialog-title"> <div data-view-component="true" class="Overlay-body"> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <div hidden class="create-custom-scope-form" data-target="custom-scopes.createCustomScopeForm"> <!-- '"` --><!-- </textarea></xmp> --></option></form><form id="custom-scopes-dialog-form" data-turbo="false" action="/search/custom_scopes" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token" 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href=\"/LSH9832/edgeyolo/blob/main/assets/visdrone.jpg\"\u003e\u003cimg src=\"/LSH9832/edgeyolo/raw/main/assets/visdrone.jpg\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e\u003ca href=\"https://www.bit.edu.cn\" rel=\"nofollow\"\u003e\u003cimg src=\"/LSH9832/edgeyolo/raw/main/assets/bit.png\" align=\"left\" height=\"60\" width=\"60\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e EdgeYOLO: 边缘设备友好的无锚框检测器\u003c/h1\u003e\u003ca id=\"user-content--edgeyolo-边缘设备友好的无锚框检测器\" class=\"anchor\" aria-label=\"Permalink: EdgeYOLO: 边缘设备友好的无锚框检测器\" href=\"#-edgeyolo-边缘设备友好的无锚框检测器\"\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 align=\"center\" dir=\"auto\"\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"/LSH9832/edgeyolo/blob/main/README_EN.md\"\u003eEnglish\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://arxiv.org/abs/2302.07483\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c552f6035d2cd66bb8b6b8c3e0dad0ecac7aecf3676c741ff79c977d09c399d4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323330322e30373438332d253343434f4c4f522533452e737667\" alt=\"arXiv\" data-canonical-src=\"https://img.shields.io/badge/arXiv-2302.07483-%3CCOLOR%3E.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e工具推荐: 使用SAM(\u003ca href=\"https://github.com/facebookresearch/segment-anything\"\u003eSegment Anything Model\u003c/a\u003e) 辅助的图像标注工具: \u003ca href=\"https://github.com/LSH9832/SAMLabelerPro\"\u003eSAMLabeler Pro\u003c/a\u003e，支持多人同时远程标注\u003c/h4\u003e\u003ca id=\"user-content-工具推荐-使用samsegment-anything-model-辅助的图像标注工具-samlabeler-pro支持多人同时远程标注\" class=\"anchor\" aria-label=\"Permalink: 工具推荐: 使用SAM(Segment Anything Model) 辅助的图像标注工具: SAMLabeler Pro，支持多人同时远程标注\" href=\"#工具推荐-使用samsegment-anything-model-辅助的图像标注工具-samlabeler-pro支持多人同时远程标注\"\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简介\u003c/h2\u003e\u003ca id=\"user-content-简介\" class=\"anchor\" aria-label=\"Permalink: 简介\" href=\"#简介\"\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\u003eEdgeYOLO 在嵌入式设备 Nvidia Jetson AGX Xavier 上达到了34FPS，在COCO2017数据集上有\u003cstrong\u003e50.6\u003c/strong\u003e% AP的准确度，在VisDrone2019-DET数据集上有\u003cstrong\u003e25.9\u003c/strong\u003e% AP的准确度 \u003cstrong\u003e(图像输入大小为640x640, 批大小为16, 包含后处理时间)\u003c/strong\u003e。更小的模型EdgeYOLO-S在COCO2017数据集上以\u003cstrong\u003e44.1\u003c/strong\u003e% AP、\u003cstrong\u003e63.3\u003c/strong\u003e% AP\u003csup\u003e0.5\u003c/sup\u003e（目前单阶段P5小模型中最好的）准度达到了53FPS的速度。\u003c/li\u003e\n\u003cli\u003e我们提供了更加强大的数据增强方法，可以在数据集标签稀疏时起到更好的效果。\u003c/li\u003e\n\u003cli\u003e在训练末尾阶段使用RH损失函数，中小模型的检测效果有所提升。\u003c/li\u003e\n\u003cli\u003e我们的论文（预印版）已在\u003ca href=\"https://arxiv.org/abs/2302.07483\" rel=\"nofollow\"\u003e\u003cstrong\u003earxiv\u003c/strong\u003e\u003c/a\u003e公布\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e更新\u003c/h2\u003e\u003ca id=\"user-content-更新\" class=\"anchor\" aria-label=\"Permalink: 更新\" href=\"#更新\"\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\u003cstrong\u003e[2024/12/7] 重大更新\u003c/strong\u003e \u003cbr\u003e\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e上传了多平台统一化部署项目，无需针对不同平台修改代码，详见目录\u003ca href=\"/LSH9832/edgeyolo/blob/main/deployment\"\u003edeployment\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e[2024/3/16]\u003c/strong\u003e \u003cbr\u003e\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e上传了\u003ca href=\"https://github.com/LSH9832/edgeyolo/blob/main/demo/amct_onnx2om.py\"\u003edemo/amct_onnx2om.py\u003c/a\u003e 该脚本用于将onnx模型转换为华为昇腾系列设备上支持的om模型(比如昇腾310)，并且发布\u003ca href=\"https://github.com/LSH9832/edgeyolo/blob/main/cpp/ascend\"\u003ec++部署代码示例\u003c/a\u003e. （注意，你必须拥有华为官网提供的相应依赖库和工具，部分依赖仅购买相应硬件的用户才有下载权限）\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e[2024/3/6]\u003c/strong\u003e \u003cbr\u003e\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e我们上传了用于方便训练和模型导出的\u003ca href=\"#%E5%A6%82%E6%9E%9C%E4%BD%A0%E6%83%B3%E4%BD%BF%E7%94%A8docker%E9%82%A3%E4%B9%88\"\u003edocker镜像\u003c/a\u003e，可以方便地将模型一键导出为RKNN，MNN，地平线J5，Jetson等平台支持的部署模型\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e[2023/12/6]\u003c/strong\u003e \u003cbr\u003e\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/LSH9832/edgeyolo/tree/main/cpp/rknn\"\u003eRKNN模型推理部署代码(仅用于RK3588芯片)\u003c/a\u003e已发布。 \u003cbr\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e[2023/11/23]\u003c/strong\u003e \u003cbr\u003e\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/LSH9832/edgeyolo/tree/main/cpp/mnn\"\u003eMNN模型推理部署代码\u003c/a\u003e已发布。 \u003cbr\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e[2023/2/28]\u003c/strong\u003e \u003cbr\u003e\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e现在支持对TensorRT模型进行evaluation了。 \u003cbr\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e[2023/2/24]\u003c/strong\u003e \u003cbr\u003e\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e现在edgeyolo支持\u003ca href=\"https://github.com/LSH9832/edgeyolo/blob/main/params/dataset/yolo.yaml\"\u003eYOLO格式的数据集\u003c/a\u003e了 \u003cbr\u003e\u003c/li\u003e\n\u003cli\u003e修复了一些已知错误（在Linux下的cpp代码使用--loop选项时的错误以及分布式训练对标签进行缓存时发生的错误）\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e[2023/2/20]\u003c/strong\u003e \u003cbr\u003e\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/LSH9832/edgeyolo/tree/main/cpp/console\"\u003eTensorRT C++ 命令行演示代码\u003c/a\u003e (需要用到\u003cstrong\u003eopencv\u003c/strong\u003e 和 \u003cstrong\u003eqt5\u003c/strong\u003e的库) \u003cbr\u003e\u003c/li\u003e\n\u003cli\u003e修复了使用7.X版本的TensorRT导出模型时产生的bugs\u003cbr\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e[2023/2/19]\u003c/strong\u003e \u003cbr\u003e\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e发布带有校准训练过程的TensorRT int8模型导出代码 \u003cbr\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e即将到来\u003c/h2\u003e\u003ca id=\"user-content-即将到来\" class=\"anchor\" aria-label=\"Permalink: 即将到来\" href=\"#即将到来\"\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用于训练和一键式导出边缘设备支持的模型（RKNN/地平线J5/Jetson等）\u003c/li\u003e\n\u003cli\u003e重构TensorRT部署代码，使其更方便使用\u003c/li\u003e\n\u003cli\u003e更多不同的模型\u003c/li\u003e\n\u003cli\u003e用于TensorRT推理的、带有界面的C++代码\u003c/li\u003e\n\u003cli\u003e用于实例分割任务EdgeYOLO-mask模型\u003c/li\u003e\n\u003cli\u003e简单有效的预训练方法\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e模型\u003c/h2\u003e\u003ca id=\"user-content-模型\" class=\"anchor\" aria-label=\"Permalink: 模型\" href=\"#模型\"\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在COCO2017-train上训练的模型\u003c/li\u003e\n\u003c/ul\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e模型\u003c/th\u003e\n\u003cth align=\"center\"\u003e输入大小\u003c/th\u003e\n\u003cth align=\"center\"\u003emAP\u003csup\u003eval\u003cbr\u003e0.5:0.95\u003c/sup\u003e\u003c/th\u003e\n\u003cth align=\"center\"\u003emAP\u003csup\u003eval\u003cbr\u003e0.5\u003c/sup\u003e\u003c/th\u003e\n\u003cth align=\"center\"\u003eFPS\u003csup\u003eAGX Xavier\u003cbr\u003etrt fp16 批大小=16 \u003cbr\u003e包含NMS\u003c/sup\u003e\u003c/th\u003e\n\u003cth align=\"center\"\u003e参数量\u003cbr\u003etrain / infer\u003cbr\u003e\u003csup\u003e(M)\u003c/sup\u003e\u003c/th\u003e\n\u003cth align=\"center\"\u003e下载\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003cstrong\u003eEdgeYOLO-Tiny-LRELU\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e416\u003cbr\u003e640\u003c/td\u003e\n\u003ctd align=\"center\"\u003e33.1\u003cbr\u003e37.8\u003c/td\u003e\n\u003ctd align=\"center\"\u003e50.5\u003cbr\u003e56.7\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003cstrong\u003e206\u003c/strong\u003e\u003cbr\u003e109\u003c/td\u003e\n\u003ctd align=\"center\"\u003e7.6 / 7.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/LSH9832/edgeyolo/releases/download/v0.0.0/edgeyolo_tiny_lrelu_coco.pth\"\u003e\u003cstrong\u003egithub\u003c/strong\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003cstrong\u003eEdgeYOLO-Tiny\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e416\u003cbr\u003e640\u003c/td\u003e\n\u003ctd align=\"center\"\u003e37.2\u003cbr\u003e41.4\u003c/td\u003e\n\u003ctd align=\"center\"\u003e55.4\u003cbr\u003e60.4\u003c/td\u003e\n\u003ctd align=\"center\"\u003e136\u003cbr\u003e67\u003c/td\u003e\n\u003ctd align=\"center\"\u003e5.8 / 5.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/LSH9832/edgeyolo/releases/download/v0.0.0/edgeyolo_tiny_coco.pth\"\u003e\u003cstrong\u003egithub\u003c/strong\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003cstrong\u003eEdgeYOLO-S\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e640\u003c/td\u003e\n\u003ctd align=\"center\"\u003e44.1\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003cstrong\u003e63.3\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e53\u003c/td\u003e\n\u003ctd align=\"center\"\u003e9.9 / 9.3\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/LSH9832/edgeyolo/releases/download/v0.0.0/edgeyolo_s_coco.pth\"\u003e\u003cstrong\u003egithub\u003c/strong\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003cstrong\u003eEdgeYOLO-M\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e640\u003c/td\u003e\n\u003ctd align=\"center\"\u003e47.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e66.6\u003c/td\u003e\n\u003ctd align=\"center\"\u003e46\u003c/td\u003e\n\u003ctd align=\"center\"\u003e19.0 / 17.8\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/LSH9832/edgeyolo/releases/download/v0.0.0/edgeyolo_m_coco.pth\"\u003e\u003cstrong\u003egithub\u003c/strong\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003cstrong\u003eEdgeYOLO\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e640\u003c/td\u003e\n\u003ctd align=\"center\"\u003e50.6\u003c/td\u003e\n\u003ctd align=\"center\"\u003e69.8\u003c/td\u003e\n\u003ctd align=\"center\"\u003e34\u003c/td\u003e\n\u003ctd align=\"center\"\u003e41.2 / 40.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/LSH9832/edgeyolo/releases/download/v0.0.0/edgeyolo_coco.pth\"\u003e\u003cstrong\u003egithub\u003c/strong\u003e\u003c/a\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\u003e在VisDrone2019上训练的模型 (这些模型骨干网络初始参数来自于上面的模型)\u003c/li\u003e\n\u003c/ul\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e训练时使用的是 \u003ca href=\"https://github.com/LSH9832/edgeyolo/releases/download/v1.0.0/visdrone_coco.zip\"\u003e已转化为COCO格式的 VisDrone2019 数据集\u003c/a\u003e 。\u003c/li\u003e\n\u003cli\u003e以下是没有去掉在\u003cstrong\u003eignored region\u003c/strong\u003e中检测框的情况下测得的结果\u003c/li\u003e\n\u003c/ol\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e模型\u003c/th\u003e\n\u003cth align=\"center\"\u003e输入大小\u003c/th\u003e\n\u003cth align=\"center\"\u003emAP\u003csup\u003eval\u003cbr\u003e0.5:0.95\u003c/sup\u003e\u003c/th\u003e\n\u003cth align=\"center\"\u003emAP\u003csup\u003eval\u003cbr\u003e0.5\u003c/sup\u003e\u003c/th\u003e\n\u003cth align=\"center\"\u003e下载\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003cstrong\u003eEdgeYOLO-Tiny-LRELU\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e416\u003cbr\u003e640\u003c/td\u003e\n\u003ctd align=\"center\"\u003e12.1\u003cbr\u003e18.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e22.8\u003cbr\u003e33.6\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/LSH9832/edgeyolo/releases/download/v0.0.0/edgeyolo_tiny_lrelu_visdrone.pth\"\u003e\u003cstrong\u003egithub\u003c/strong\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003cstrong\u003eEdgeYOLO-Tiny\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e416\u003cbr\u003e640\u003c/td\u003e\n\u003ctd align=\"center\"\u003e14.9\u003cbr\u003e21.8\u003c/td\u003e\n\u003ctd align=\"center\"\u003e27.3\u003cbr\u003e38.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/LSH9832/edgeyolo/releases/download/v0.0.0/edgeyolo_tiny_visdrone.pth\"\u003e\u003cstrong\u003egithub\u003c/strong\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003cstrong\u003eEdgeYOLO-S\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e640\u003c/td\u003e\n\u003ctd align=\"center\"\u003e23.6\u003c/td\u003e\n\u003ctd align=\"center\"\u003e40.8\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/LSH9832/edgeyolo/releases/download/v0.0.0/edgeyolo_s_visdrone.pth\"\u003e\u003cstrong\u003egithub\u003c/strong\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003cstrong\u003eEdgeYOLO-M\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e640\u003c/td\u003e\n\u003ctd align=\"center\"\u003e25.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e42.9\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/LSH9832/edgeyolo/releases/download/v0.0.0/edgeyolo_m_visdrone.pth\"\u003e\u003cstrong\u003egithub\u003c/strong\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003cstrong\u003eEdgeYOLO\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e640\u003c/td\u003e\n\u003ctd align=\"center\"\u003e25.9\u003cbr\u003e26.9\u003c/td\u003e\n\u003ctd align=\"center\"\u003e43.9\u003cbr\u003e45.4\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/LSH9832/edgeyolo/releases/download/v0.0.0/edgeyolo_visdrone.pth\"\u003e\u003cstrong\u003egithub(原)\u003c/strong\u003e\u003c/a\u003e\u003cbr\u003e\u003ca href=\"https://github.com/LSH9832/edgeyolo/releases/download/v1.0.0/edgeyolo_visdrone.pth\"\u003e\u003cstrong\u003egithub(新)\u003c/strong\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdetails\u003e\n\u003csummary\u003e在 COCO2017 上的部分检测结果\u003c/summary\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/LSH9832/edgeyolo/blob/main/assets/coco.jpg\"\u003e\u003cimg src=\"/LSH9832/edgeyolo/raw/main/assets/coco.jpg\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/details\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e快速使用\u003c/h2\u003e\u003ca id=\"user-content-快速使用\" class=\"anchor\" aria-label=\"Permalink: 快速使用\" href=\"#快速使用\"\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\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e安装\u003c/h3\u003e\u003ca id=\"user-content-安装\" class=\"anchor\" aria-label=\"Permalink: 安装\" href=\"#安装\"\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=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"git clone https://github.com/LSH9832/edgeyolo.git\ncd edgeyolo\npip install -r requirements.txt\"\u003e\u003cpre\u003egit clone https://github.com/LSH9832/edgeyolo.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e edgeyolo\npip install -r requirements.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e如果使用TensorRT，请确保设备上已经安装好torch2trt和TensorRT Development Toolkit(version\u0026gt;7.1.3.0)。\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"git clone https://github.com/NVIDIA-AI-IOT/torch2trt.git\ncd torch2trt\npython setup.py install\"\u003e\u003cpre\u003egit clone https://github.com/NVIDIA-AI-IOT/torch2trt.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e torch2trt\npython setup.py install\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e或者为了保证和我们用的是同一个版本的torch2trt, \u003ca href=\"https://github.com/LSH9832/edgeyolo/releases/download/v1.0.0/torch2trt.zip\"\u003e请在这里下载\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e如果你想使用docker那么\u003c/h4\u003e\u003ca id=\"user-content-如果你想使用docker那么\" class=\"anchor\" aria-label=\"Permalink: 如果你想使用docker那么\" href=\"#如果你想使用docker那么\"\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从\u003ca href=\"https://pan.baidu.com/s/1D9ReGtI-iM79RoEGat1eAw?pwd=ujar\" rel=\"nofollow\"\u003e百度网盘, 14.3G\u003c/a\u003e下载docker镜像, 分享码: ujar\u003c/li\u003e\n\u003cli\u003e然后导入镜像\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"docker import edgeyolo_deploy.tar.gz edgeyolo:latest\"\u003e\u003cpre\u003edocker import edgeyolo_deploy.tar.gz edgeyolo:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e运行镜像\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"docker run -it \\\n --runtime=nvidia \\\n -e NVIDIA_DRIVER_CAPABILITIES=compute,utility \\\n -e NVIDIA_VISIBLE_DEVICES=all \\\n --shm-size 15g \\\n -w /code \\\n -v \u0026quot;/path/to/your/edgeyolo/parent_dir\u0026quot;:/code \\\n -v \u0026quot;/path/to/your/dataset/parent_dir\u0026quot;:/dataset \\\n edgeyolo:latest\"\u003e\u003cpre\u003edocker run -it \\\n --runtime=nvidia \\\n -e NVIDIA_DRIVER_CAPABILITIES=compute,utility \\\n -e NVIDIA_VISIBLE_DEVICES=all \\\n --shm-size 15g \\\n -w /code \\\n -v \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/your/edgeyolo/parent_dir\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e:/code \\\n -v \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/path/to/your/dataset/parent_dir\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e:/dataset \\\n edgeyolo:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e之后你就可以在docker镜像里使用 \"\u003ca href=\"https://github.com/LSH9832/edgeyolo/blob/main/docker_export.py\"\u003edocker_export.py\u003c/a\u003e\" 导出模型而不是使用 \"\u003ca href=\"https://github.com/LSH9832/edgeyolo/blob/main/export.py\"\u003eexport.py\u003c/a\u003e\" 了，这将会更加方便。\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e推理\u003c/h3\u003e\u003ca id=\"user-content-推理\" class=\"anchor\" aria-label=\"Permalink: 推理\" href=\"#推理\"\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\u003cstrong\u003e首先 \u003ca href=\"https://github.com/LSH9832/edgeyolo/releases/tag/v0.0.0\"\u003e在此下载模型的权重文件\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python detect.py --weights edgeyolo_coco.pth --source XXX.mp4 --fp16\n\n# 完整命令参数\npython detect.py --weights edgeyolo_coco.pth # 权重文件\n --source /XX/XXX.mp4 # 或网络视频流地址，或目录下全部为图片的文件夹, 或rosbag（xxx.bag）\n --topic /camera/xxx # 仅当使用rosbag或从ros中某个话题提取图片时使用该选项\n --conf-thres 0.25 # 置信度阈值\n --nms-thres 0.5 # 重合度阈值\n --input-size 640 640 # 输入大小\n --batch 1 # 批大小\n --save-dir ./output/detect/imgs # 如果按下键盘\u0026quot;s\u0026quot;键，当前图像将保存在该文件夹中\n --fp16 # 半精度推理\n --no-fuse # 不进行重参数化\n --no-label # 不显示带有类别和置信度的标签，仅画框\n --mp # 使用多进程，当 batch\u0026gt;1 时可以使图像显示更流畅\n --fps 30 # 最大FPS限制, 仅在使用 --mp 选项时有效\"\u003e\u003cpre\u003epython detect.py --weights edgeyolo_coco.pth --source XXX.mp4 --fp16\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 完整命令参数\u003c/span\u003e\npython detect.py --weights edgeyolo_coco.pth \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 权重文件\u003c/span\u003e\n --source /XX/XXX.mp4 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 或网络视频流地址，或目录下全部为图片的文件夹, 或rosbag（xxx.bag）\u003c/span\u003e\n --topic /camera/xxx \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 仅当使用rosbag或从ros中某个话题提取图片时使用该选项\u003c/span\u003e\n --conf-thres 0.25 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 置信度阈值\u003c/span\u003e\n --nms-thres 0.5 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 重合度阈值\u003c/span\u003e\n --input-size 640 640 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 输入大小\u003c/span\u003e\n --batch 1 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 批大小\u003c/span\u003e\n --save-dir ./output/detect/imgs \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 如果按下键盘\"s\"键，当前图像将保存在该文件夹中\u003c/span\u003e\n --fp16 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 半精度推理\u003c/span\u003e\n --no-fuse \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 不进行重参数化\u003c/span\u003e\n --no-label \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 不显示带有类别和置信度的标签，仅画框\u003c/span\u003e\n --mp \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 使用多进程，当 batch\u0026gt;1 时可以使图像显示更流畅\u003c/span\u003e\n --fps 30 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 最大FPS限制, 仅在使用 --mp 选项时有效\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e训练\u003c/h3\u003e\u003ca id=\"user-content-训练\" class=\"anchor\" aria-label=\"Permalink: 训练\" href=\"#训练\"\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首先准备好你的数据集并创建好相应的数据集配置文件(./params/dataset/XXX.yaml)，配置文件中应包含如下信息:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e（目前支持COCO、YOLO、VOC、VisDrone、DOTA五种格式的数据集）\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-yaml notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"type: \u0026quot;coco\u0026quot; # 数据集格式（小写），目前支持COCO格式、YOLO格式、VOC格式、VisDrone格式、DOTA格式\ndataset_path: \u0026quot;/dataset/coco2017\u0026quot; # 数据集根目录\n\nkwargs:\n suffix: \u0026quot;jpg\u0026quot; # 数据集图片后缀名\n use_cache: true # 使用缓存文件，在i5-12490f上测试完整加载时间：有分割标签979MB：52s -\u0026gt; 10s, 无分割标签228MB：39s -\u0026gt; 4s\n\ntrain:\n image_dir: \u0026quot;images/train2017\u0026quot; # 训练集图片文件夹\n label: \u0026quot;annotations/instances_train2017.json\u0026quot; # 训练集标签文件（单文件格式）或文件夹（多文件格式）\n\nval:\n image_dir: \u0026quot;images/val2017\u0026quot; # 验证集图片文件夹\n label: \u0026quot;annotations/instances_val2017.json\u0026quot; # 验证集标签文件（单文件格式）或文件夹（多文件格式）\n\ntest:\n test_dir: \u0026quot;test2017\u0026quot; # 测试集图片文件夹（代码中暂未使用，后续将会完善）\n\nsegmentaion_enabled: true # 是否有并且使用实例分割标签代替目标框标签进行训练\n\nnames: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',\n 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',\n 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',\n 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',\n 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',\n 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',\n 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',\n 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',\n 'hair drier', 'toothbrush'] # 类别名称\"\u003e\u003cpre\u003e\u003cspan class=\"pl-ent\"\u003etype\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ecoco\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 数据集格式（小写），目前支持COCO格式、YOLO格式、VOC格式、VisDrone格式、DOTA格式\u003c/span\u003e\n\u003cspan class=\"pl-ent\"\u003edataset_path\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e/dataset/coco2017\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 数据集根目录\u003c/span\u003e\n\n\u003cspan class=\"pl-ent\"\u003ekwargs\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003esuffix\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ejpg\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 数据集图片后缀名\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003euse_cache\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003etrue \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 使用缓存文件，在i5-12490f上测试完整加载时间：有分割标签979MB：52s -\u0026gt; 10s, 无分割标签228MB：39s -\u0026gt; 4s\u003c/span\u003e\n\n\u003cspan class=\"pl-ent\"\u003etrain\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eimage_dir\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eimages/train2017\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 训练集图片文件夹\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003elabel\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eannotations/instances_train2017.json\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 训练集标签文件（单文件格式）或文件夹（多文件格式）\u003c/span\u003e\n\n\u003cspan class=\"pl-ent\"\u003eval\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003eimage_dir\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eimages/val2017\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 验证集图片文件夹\u003c/span\u003e\n \u003cspan class=\"pl-ent\"\u003elabel\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eannotations/instances_val2017.json\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 验证集标签文件（单文件格式）或文件夹（多文件格式）\u003c/span\u003e\n\n\u003cspan class=\"pl-ent\"\u003etest\u003c/span\u003e:\n \u003cspan class=\"pl-ent\"\u003etest_dir\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003etest2017\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 测试集图片文件夹（代码中暂未使用，后续将会完善）\u003c/span\u003e\n\n\u003cspan class=\"pl-ent\"\u003esegmentaion_enabled\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003etrue \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 是否有并且使用实例分割标签代替目标框标签进行训练\u003c/span\u003e\n\n\u003cspan class=\"pl-ent\"\u003enames\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003efire hydrant\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e, 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003eelephant\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e, 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003eskis\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e, 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003etennis racket\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e, 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003esandwich\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e, 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003epotted plant\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e, 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003emicrowave\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e, 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003ehair drier\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e, 'toothbrush'] \u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 类别名称\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e修改训练配置文件 ./params/train/train_XXX.yaml中的相应参数\u003c/li\u003e\n\u003cli\u003e最后使用如下命令启动训练\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python train.py --cfg ./params/train/train_XXX.yaml\"\u003e\u003cpre\u003epython train.py --cfg ./params/train/train_XXX.yaml\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e你可以通过\"plot.py\"绘制损失曲线、学习率曲线和准确度曲线(AP50 和 AP50:95)\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python plot.py --all \\ # 绘制所有曲线或 (--lr：学习率, --ap：准确度, --loss：损失)\n -f ./output/train/edgeyolo_tiny_lrelu \\ # 训练的输出文件夹路径或 (文件路径：--ap则使用output_path/eval.yaml， --lr和--loss则使用output_path/log.txt)\n --no-show \\ # 不使用plt.show()显示曲线, 适用于无图形界面的设备（如云服务器），或你仅仅想保存图片\n --save \\ # 保存图片\n --format pdf png svg jpg eps # 保存图片的格式，支持多种格式同时保存\"\u003e\u003cpre\u003epython plot.py --all \u003cspan class=\"pl-cce\"\u003e\\ \u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 绘制所有曲线或 (--lr：学习率, --ap：准确度, --loss：损失)\u003c/span\u003e\n -f ./output/train/edgeyolo_tiny_lrelu \u003cspan class=\"pl-cce\"\u003e\\ \u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 训练的输出文件夹路径或 (文件路径：--ap则使用output_path/eval.yaml， --lr和--loss则使用output_path/log.txt)\u003c/span\u003e\n --no-show \u003cspan class=\"pl-cce\"\u003e\\ \u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 不使用plt.show()显示曲线, 适用于无图形界面的设备（如云服务器），或你仅仅想保存图片\u003c/span\u003e\n --save \u003cspan class=\"pl-cce\"\u003e\\ \u003c/span\u003e \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 保存图片\u003c/span\u003e\n --format pdf png svg jpg eps \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 保存图片的格式，支持多种格式同时保存\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdetails\u003e\n\u003csummary\u003e绘图将如下所示\u003c/summary\u003e\n\u003cp dir=\"auto\"\u003e准确度曲线\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://private-user-images.githubusercontent.com/69880398/300196480-091cddab-c79e-44ae-bead-43391c7d8916.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.Olkfk3JrWZtJ-KLlrm4Ee6T_SKI4SR_y-G-RdOofr3E\"\u003e\u003cimg src=\"https://private-user-images.githubusercontent.com/69880398/300196480-091cddab-c79e-44ae-bead-43391c7d8916.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.Olkfk3JrWZtJ-KLlrm4Ee6T_SKI4SR_y-G-RdOofr3E\" alt=\"ap\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e损失曲线\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://private-user-images.githubusercontent.com/69880398/300196518-51de4116-2c4a-4a35-ab95-62a41f791749.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.CWniUzO_Gw1ytH3cCDi5Z_vhumhQ_gU3_483PFSgRbY\"\u003e\u003cimg src=\"https://private-user-images.githubusercontent.com/69880398/300196518-51de4116-2c4a-4a35-ab95-62a41f791749.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3NDQyNjM4OTMsIm5iZiI6MTc0NDI2MzU5MywicGF0aCI6Ii82OTg4MDM5OC8zMDAxOTY1MTgtNTFkZTQxMTYtMmM0YS00YTM1LWFiOTUtNjJhNDFmNzkxNzQ5LnBuZz9YLUFtei1BbGdvcml0aG09QVdTNC1ITUFDLVNIQTI1NiZYLUFtei1DcmVkZW50aWFsPUFLSUFWQ09EWUxTQTUzUFFLNFpBJTJGMjAyNTA0MTAlMkZ1cy1lYXN0LTElMkZzMyUyRmF3czRfcmVxdWVzdCZYLUFtei1EYXRlPTIwMjUwNDEwVDA1Mzk1M1omWC1BbXotRXhwaXJlcz0zMDAmWC1BbXotU2lnbmF0dXJlPWViNjk0OTJmZjcwN2Y4YzI1N2M4ZmU0MWVkZDhjMjZmNTk3YjgwZGY1ZjQzNGNmOWVjZmJhNWFhNGI4YzA5ZDUmWC1BbXotU2lnbmVkSGVhZGVycz1ob3N0In0.CWniUzO_Gw1ytH3cCDi5Z_vhumhQ_gU3_483PFSgRbY\" alt=\"loss\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e学习率曲线\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://private-user-images.githubusercontent.com/69880398/300196521-1be1d98d-5440-4e2d-8f8c-89aa9f07bd42.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.24l0eeDmcF_qGF9d8Qy_ZoXTmiUnBGlSSQdX3fh9smg\"\u003e\u003cimg src=\"https://private-user-images.githubusercontent.com/69880398/300196521-1be1d98d-5440-4e2d-8f8c-89aa9f07bd42.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.24l0eeDmcF_qGF9d8Qy_ZoXTmiUnBGlSSQdX3fh9smg\" alt=\"lr\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/details\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e验证\u003c/h3\u003e\u003ca id=\"user-content-验证\" class=\"anchor\" aria-label=\"Permalink: 验证\" href=\"#验证\"\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=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python evaluate.py --weights edgeyolo_coco.pth --dataset params/dataset/XXX.yaml --batch 16 --device 0\n\n# 完整命令参数\npython evaluate.py --weights edgeyolo_coco.pth # 权重文件， 也可是tensorrt模型，output/export/edgeyolo_coco/model.pt\n --dataset params/dataset/XXX.yaml # 数据集配置文件\n --batch 16 # 每一个GPU上的批大小\n --device 0 # 只用一个就写0就行\n --input-size 640 640 # 高、宽（注意别反了）\n --trt # 如果使用的是TensorRT模型需要加上此选项\n --save # 保存没有优化器参数的权重文件，并将训练次数重置为-1\"\u003e\u003cpre\u003epython evaluate.py --weights edgeyolo_coco.pth --dataset params/dataset/XXX.yaml --batch 16 --device 0\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 完整命令参数\u003c/span\u003e\npython evaluate.py --weights edgeyolo_coco.pth \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 权重文件， 也可是tensorrt模型，output/export/edgeyolo_coco/model.pt\u003c/span\u003e\n --dataset params/dataset/XXX.yaml \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 数据集配置文件\u003c/span\u003e\n --batch 16 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 每一个GPU上的批大小\u003c/span\u003e\n --device 0 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 只用一个就写0就行\u003c/span\u003e\n --input-size 640 640 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 高、宽（注意别反了）\u003c/span\u003e\n --trt \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 如果使用的是TensorRT模型需要加上此选项\u003c/span\u003e\n --save \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 保存没有优化器参数的权重文件，并将训练次数重置为-1\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e导出 onnx \u0026amp; tensorrt\u003c/h3\u003e\u003ca id=\"user-content-导出-onnx--tensorrt\" class=\"anchor\" aria-label=\"Permalink: 导出 onnx \u0026amp; tensorrt\" href=\"#导出-onnx--tensorrt\"\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\u003eONNX\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python export.py --onnx --weights edgeyolo_coco.pth --batch 1\n\n# all options\npython export.py --onnx # 如果没有安装tensorrt和torch2trt，用--onnx-only代替\n --weights edgeyolo_coco.pth \n --input-size 640 640 # 宽、高（注意别反了）\n --batch 1\n --opset 11\n --no-simplify # 不简化模型结构\"\u003e\u003cpre\u003epython export.py --onnx --weights edgeyolo_coco.pth --batch 1\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e all options\u003c/span\u003e\npython export.py --onnx \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 如果没有安装tensorrt和torch2trt，用--onnx-only代替\u003c/span\u003e\n --weights edgeyolo_coco.pth \n --input-size 640 640 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 宽、高（注意别反了）\u003c/span\u003e\n --batch 1\n --opset 11\n --no-simplify \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 不简化模型结构\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e将生成\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"output/export/edgeyolo_coco/640x640_batch1.onnx\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003eoutput/export/edgeyolo_coco/640x640_batch1.onnx\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTensorRT\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# fp16\npython export.py --trt --weights edgeyolo_coco.pth --batch 1 --workspace 8\n\n# int8\npython export.py --trt --weights edgeyolo_coco.pth --batch 1 --workspace 8 --int8 --dataset params/dataset/coco.yaml --num-imgs 1024\n\n# 完整选项\npython export.py --trt # 你可以添加上述--onnx和其相关选项，同时导出两种模型\n --weights edgeyolo_coco.pth\n --input-size 640 640 # 宽、高（注意别反了）\n --batch 1\n --workspace 10 # (GB)\n --no-fp16 # 默认输出fp16精度模型，使用此选项关闭，转为fp32精度\n --int8 # 输出int8精度，需要用到以下选项用于校准训练\n --datset params/dataset/coco.yaml # 校准训练图片来源于该数据集的验证集图片（上限5120张）\n --train # 使用训练集图片而不是验证集图片（上限5120张）\n --all # 使用训练集和验证集所有图片（上限5120张）\n --num-imgs 512 # （上限5120张）\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e fp16\u003c/span\u003e\npython export.py --trt --weights edgeyolo_coco.pth --batch 1 --workspace 8\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e int8\u003c/span\u003e\npython export.py --trt --weights edgeyolo_coco.pth --batch 1 --workspace 8 --int8 --dataset params/dataset/coco.yaml --num-imgs 1024\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 完整选项\u003c/span\u003e\npython export.py --trt \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 你可以添加上述--onnx和其相关选项，同时导出两种模型\u003c/span\u003e\n --weights edgeyolo_coco.pth\n --input-size 640 640 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 宽、高（注意别反了）\u003c/span\u003e\n --batch 1\n --workspace 10 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e (GB)\u003c/span\u003e\n --no-fp16 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 默认输出fp16精度模型，使用此选项关闭，转为fp32精度\u003c/span\u003e\n --int8 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 输出int8精度，需要用到以下选项用于校准训练\u003c/span\u003e\n --datset params/dataset/coco.yaml \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 校准训练图片来源于该数据集的验证集图片（上限5120张）\u003c/span\u003e\n --train \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 使用训练集图片而不是验证集图片（上限5120张）\u003c/span\u003e\n --all \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 使用训练集和验证集所有图片（上限5120张）\u003c/span\u003e\n --num-imgs 512 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e （上限5120张）\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e将产生\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"(可选) output/export/edgeyolo_coco/640x640_batch1.onnx\noutput/export/edgeyolo_coco/640x640_batch1_fp16(int8).pt # 用于 python 推理\noutput/export/edgeyolo_coco/640x640_batch1_fp16(int8).engine # 用于 c++ 推理\noutput/export/edgeyolo_coco/640x640_batch1_fp16(int8).json # 用于 c++ 推理\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e(可选) output/export/edgeyolo_coco/640x640_batch1.onnx\noutput/export/edgeyolo_coco/640x640_batch1_fp16(int8).pt # 用于 python 推理\noutput/export/edgeyolo_coco/640x640_batch1_fp16(int8).engine # 用于 c++ 推理\noutput/export/edgeyolo_coco/640x640_batch1_fp16(int8).json # 用于 c++ 推理\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTensorRT Int8 量化模型基准测试\u003c/h4\u003e\u003ca id=\"user-content-tensorrt-int8-量化模型基准测试\" class=\"anchor\" aria-label=\"Permalink: TensorRT Int8 量化模型基准测试\" href=\"#tensorrt-int8-量化模型基准测试\"\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测试环境: TensorRT版本8.2.5.1, Windows, i5-12490F, RTX 3060 12GB\u003c/li\u003e\n\u003cli\u003e对于TensorRT来说，使用不同的校准数据集，产生的int8模型之间的精度和速度都可能有很明显的差异，这是由于tensorrt不是强制转换为int8的，有些层它觉得转了后效果不好就不转了，所以我认为这是为什么大部分官方项目都不给出int8量化效果的代码。我自认为下表结果并不具有多少参考意义。\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eCOCO2017-TensorRT-int8\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003eInt8 模型\u003c/th\u003e\n\u003cth align=\"center\"\u003e尺寸\u003c/th\u003e\n\u003cth align=\"center\"\u003e校准图片数量\u003c/th\u003e\n\u003cth align=\"center\"\u003e工作内存空间\u003cbr\u003e\u003csup\u003e(GB)\u003c/sup\u003e\u003c/th\u003e\n\u003cth align=\"center\"\u003emAP\u003csup\u003eval\u003cbr\u003e0.5:0.95\u003c/sup\u003e\u003c/th\u003e\n\u003cth align=\"center\"\u003emAP\u003csup\u003eval\u003cbr\u003e0.5\u003c/sup\u003e\u003c/th\u003e\n\u003cth align=\"center\"\u003eFPS\u003csup\u003eRTX 3060\u003cbr\u003etrt int8 batch=16 \u003cbr\u003einclude NMS\u003c/sup\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003cstrong\u003eTiny-LRELU\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e416\u003cbr\u003e640\u003c/td\u003e\n\u003ctd align=\"center\"\u003e512\u003c/td\u003e\n\u003ctd align=\"center\"\u003e8\u003c/td\u003e\n\u003ctd align=\"center\"\u003e31.5\u003cbr\u003e36.4\u003c/td\u003e\n\u003ctd align=\"center\"\u003e48.7\u003cbr\u003e55.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e730\u003cbr\u003e360\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003cstrong\u003eTiny\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e416\u003cbr\u003e640\u003c/td\u003e\n\u003ctd align=\"center\"\u003e512\u003c/td\u003e\n\u003ctd align=\"center\"\u003e8\u003c/td\u003e\n\u003ctd align=\"center\"\u003e34.9\u003cbr\u003e39.8\u003c/td\u003e\n\u003ctd align=\"center\"\u003e53.1\u003cbr\u003e59.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e549\u003cbr\u003e288\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003cstrong\u003eS\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e640\u003c/td\u003e\n\u003ctd align=\"center\"\u003e512\u003c/td\u003e\n\u003ctd align=\"center\"\u003e8\u003c/td\u003e\n\u003ctd align=\"center\"\u003e42.4\u003c/td\u003e\n\u003ctd align=\"center\"\u003e61.8\u003c/td\u003e\n\u003ctd align=\"center\"\u003e233\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e640\u003c/td\u003e\n\u003ctd align=\"center\"\u003e512\u003c/td\u003e\n\u003ctd align=\"center\"\u003e8\u003c/td\u003e\n\u003ctd align=\"center\"\u003e45.2\u003c/td\u003e\n\u003ctd align=\"center\"\u003e64.2\u003c/td\u003e\n\u003ctd align=\"center\"\u003e211\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003cstrong\u003eL\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e640\u003c/td\u003e\n\u003ctd align=\"center\"\u003e512\u003c/td\u003e\n\u003ctd align=\"center\"\u003e8\u003c/td\u003e\n\u003ctd align=\"center\"\u003e49.1\u003c/td\u003e\n\u003ctd align=\"center\"\u003e68.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e176\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003epython推理\u003c/h4\u003e\u003ca id=\"user-content-python推理\" class=\"anchor\" aria-label=\"Permalink: python推理\" href=\"#python推理\"\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=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python detect.py --trt --weights output/export/edgeyolo_coco/640x640_batch1_int8.pt --source XXX.mp4\n\n# full commands\npython detect.py --trt \n --weights output/export/edgeyolo_coco/640x640_batch1_int8.pt \n --source XXX.mp4\n --legacy # 如果训练时\u0026quot;img = img / 255\u0026quot;（图像输入归一化）\n --use-decoder # 如果使用早期的YOLOX（v0.2.0及以前）的tensorrt模型\n --mp # 使用多进程，当 batch\u0026gt;1 时可以使图像显示更流畅\n --fps 30 # 最大FPS限制, 仅在使用 --mp 选项时有效\"\u003e\u003cpre\u003epython detect.py --trt --weights output/export/edgeyolo_coco/640x640_batch1_int8.pt --source XXX.mp4\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e full commands\u003c/span\u003e\npython detect.py --trt \n --weights output/export/edgeyolo_coco/640x640_batch1_int8.pt \n --source XXX.mp4\n --legacy \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 如果训练时\"img = img / 255\"（图像输入归一化）\u003c/span\u003e\n --use-decoder \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 如果使用早期的YOLOX（v0.2.0及以前）的tensorrt模型\u003c/span\u003e\n --mp \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 使用多进程，当 batch\u0026gt;1 时可以使图像显示更流畅\u003c/span\u003e\n --fps 30 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 最大FPS限制, 仅在使用 --mp 选项时有效\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ec++ 推理\u003c/h4\u003e\u003ca id=\"user-content-c-推理\" class=\"anchor\" aria-label=\"Permalink: c++ 推理\" href=\"#c-推理\"\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=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# 编译\ncd cpp/console/linux\nmkdir build \u0026amp;\u0026amp; cd build\ncmake ..\nmake -j4\n\n# 帮助\n./yolo -?\n./yolo --help\n \n# 运行\n# ./yolo [json文件（需要与engine文件同名）] [图像源] [--conf 置信度阈值] [--nms IOU阈值] [--loop 循环播放] [--no-label 不画标签]\n./yolo ../../../../output/export/edgeyolo_coco/640x640_batch1_int8.json ~/Videos/test.avi --conf 0.25 --nms 0.5 --loop --no-label\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 编译\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e cpp/console/linux\nmkdir build \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e build\ncmake ..\nmake -j4\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 帮助\u003c/span\u003e\n./yolo -\u003cspan class=\"pl-k\"\u003e?\u003c/span\u003e\n./yolo --help\n \n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 运行\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e ./yolo [json文件（需要与engine文件同名）] [图像源] [--conf 置信度阈值] [--nms IOU阈值] [--loop 循环播放] [--no-label 不画标签]\u003c/span\u003e\n./yolo ../../../../output/export/edgeyolo_coco/640x640_batch1_int8.json \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Videos/test.avi --conf 0.25 --nms 0.5 --loop --no-label\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e引用EdgeYOLO\u003c/h2\u003e\u003ca id=\"user-content-引用edgeyolo\" class=\"anchor\" aria-label=\"Permalink: 引用EdgeYOLO\" href=\"#引用edgeyolo\"\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=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@article{edgeyolo2023,\n title={EdgeYOLO: An Edge-Real-Time Object Detector},\n author={Shihan Liu, Junlin Zha, Jian Sun, Zhuo Li, and Gang Wang},\n journal={arXiv preprint arXiv:2302.07483},\n year={2023}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@article{edgeyolo2023,\n title={EdgeYOLO: An Edge-Real-Time Object Detector},\n author={Shihan Liu, Junlin Zha, Jian Sun, Zhuo Li, and Gang Wang},\n journal={arXiv preprint arXiv:2302.07483},\n year={2023}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://star-history.com/#LSH9832/edgeyolo\u0026amp;Timeline\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6d7f90eaff6f87c1e1a80d9ccb1169e3ed980bf8bf760001b15b856800c73b6a/68747470733a2f2f6170692e737461722d686973746f72792e636f6d2f7376673f7265706f733d4c5348393833322f65646765796f6c6f26747970653d54696d656c696e65\" alt=\"Star History Chart\" data-canonical-src=\"https://api.star-history.com/svg?repos=LSH9832/edgeyolo\u0026amp;type=Timeline\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e目前发现的bugs\u003c/h2\u003e\u003ca id=\"user-content-目前发现的bugs\" class=\"anchor\" aria-label=\"Permalink: 目前发现的bugs\" href=\"#目前发现的bugs\"\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在训练时有时可能会触发以下错误，降低pytorch版本至1.8.0应该可以解决这个问题。\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"File \u0026quot;XXX/edgeyolo/edgeyolo/train/loss.py\u0026quot;, line 667, in dynamic_k_matching\n_, pos_idx = torch.topk(cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False)\nRuntimeError: CUDA error: device-side assert triggered\nCUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1.\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003eFile \"XXX/edgeyolo/edgeyolo/train/loss.py\", line 667, in dynamic_k_matching\n_, pos_idx = torch.topk(cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False)\nRuntimeError: CUDA error: device-side assert triggered\nCUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e对于DOTA数据集，目前我们仅支持单GPU进行训练，请不要使用分布式训练方式对DOTA数据集进行训练，否则无法进行正确的训练，也无法得到正确的结果\u003c/li\u003e\n\u003cli\u003e如果使用TensorRT 8.4.X.X及以上的版本进行半精度模型的转换可能存在丢失大量精度甚至完全没有检出目标的情况，为保证使用请使用TensorRT 7.X.X.X 或 8.2.X.X 的版本\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":" EdgeYOLO: 边缘设备友好的无锚框检测器","anchor":"-edgeyolo-边缘设备友好的无锚框检测器","htmlText":" EdgeYOLO: 边缘设备友好的无锚框检测器"},{"level":4,"text":"工具推荐: 使用SAM(Segment Anything Model) 辅助的图像标注工具: SAMLabeler Pro，支持多人同时远程标注","anchor":"工具推荐-使用samsegment-anything-model-辅助的图像标注工具-samlabeler-pro支持多人同时远程标注","htmlText":"工具推荐: 使用SAM(Segment Anything Model) 辅助的图像标注工具: SAMLabeler Pro，支持多人同时远程标注"},{"level":2,"text":"简介","anchor":"简介","htmlText":"简介"},{"level":2,"text":"更新","anchor":"更新","htmlText":"更新"},{"level":2,"text":"即将到来","anchor":"即将到来","htmlText":"即将到来"},{"level":2,"text":"模型","anchor":"模型","htmlText":"模型"},{"level":2,"text":"快速使用","anchor":"快速使用","htmlText":"快速使用"},{"level":3,"text":"安装","anchor":"安装","htmlText":"安装"},{"level":4,"text":"如果你想使用docker那么","anchor":"如果你想使用docker那么","htmlText":"如果你想使用docker那么"},{"level":3,"text":"推理","anchor":"推理","htmlText":"推理"},{"level":3,"text":"训练","anchor":"训练","htmlText":"训练"},{"level":3,"text":"验证","anchor":"验证","htmlText":"验证"},{"level":3,"text":"导出 onnx \u0026 tensorrt","anchor":"导出-onnx--tensorrt","htmlText":"导出 onnx \u0026amp; tensorrt"},{"level":4,"text":"TensorRT Int8 量化模型基准测试","anchor":"tensorrt-int8-量化模型基准测试","htmlText":"TensorRT Int8 量化模型基准测试"},{"level":4,"text":"python推理","anchor":"python推理","htmlText":"python推理"},{"level":4,"text":"c++ 推理","anchor":"c-推理","htmlText":"c++ 推理"},{"level":2,"text":"引用EdgeYOLO","anchor":"引用edgeyolo","htmlText":"引用EdgeYOLO"},{"level":2,"text":"目前发现的bugs","anchor":"目前发现的bugs","htmlText":"目前发现的bugs"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2FLSH9832%2Fedgeyolo"}},{"displayName":"LICENSE","repoName":"edgeyolo","refName":"main","path":"LICENSE","preferredFileType":"license","tabName":"Apache-2.0","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2FLSH9832%2Fedgeyolo"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-7d7eb7c71814.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-708ec8ade250.js","githubDevUrl":null,"enabled_features":{"copilot_workspace":null,"code_nav_ui_events":false,"overview_shared_code_dropdown_button":true,"react_blob_overlay":false,"accessible_code_button":true,"github_models_repo_integration":false}}}}</script> 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dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto"><a href="https://www.bit.edu.cn" rel="nofollow"><img src="/LSH9832/edgeyolo/raw/main/assets/bit.png" align="left" height="60" width="60" style="max-width: 100%;"></a> EdgeYOLO: 边缘设备友好的无锚框检测器</h1><a id="user-content--edgeyolo-边缘设备友好的无锚框检测器" class="anchor" aria-label="Permalink: EdgeYOLO: 边缘设备友好的无锚框检测器" href="#-edgeyolo-边缘设备友好的无锚框检测器"><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 align="center" dir="auto"> <p dir="auto"><a href="/LSH9832/edgeyolo/blob/main/README_EN.md">English</a></p> <p dir="auto"><a href="https://arxiv.org/abs/2302.07483" rel="nofollow"><img src="https://camo.githubusercontent.com/c552f6035d2cd66bb8b6b8c3e0dad0ecac7aecf3676c741ff79c977d09c399d4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323330322e30373438332d253343434f4c4f522533452e737667" alt="arXiv" data-canonical-src="https://img.shields.io/badge/arXiv-2302.07483-%3CCOLOR%3E.svg" style="max-width: 100%;"></a></p> </div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">工具推荐: 使用SAM(<a href="https://github.com/facebookresearch/segment-anything">Segment Anything Model</a>) 辅助的图像标注工具: <a href="https://github.com/LSH9832/SAMLabelerPro">SAMLabeler Pro</a>，支持多人同时远程标注</h4><a id="user-content-工具推荐-使用samsegment-anything-model-辅助的图像标注工具-samlabeler-pro支持多人同时远程标注" class="anchor" aria-label="Permalink: 工具推荐: 使用SAM(Segment Anything Model) 辅助的图像标注工具: SAMLabeler Pro，支持多人同时远程标注" href="#工具推荐-使用samsegment-anything-model-辅助的图像标注工具-samlabeler-pro支持多人同时远程标注"><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">简介</h2><a id="user-content-简介" class="anchor" aria-label="Permalink: 简介" href="#简介"><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>EdgeYOLO 在嵌入式设备 Nvidia Jetson AGX Xavier 上达到了34FPS，在COCO2017数据集上有<strong>50.6</strong>% AP的准确度，在VisDrone2019-DET数据集上有<strong>25.9</strong>% AP的准确度 <strong>(图像输入大小为640x640, 批大小为16, 包含后处理时间)</strong>。更小的模型EdgeYOLO-S在COCO2017数据集上以<strong>44.1</strong>% AP、<strong>63.3</strong>% AP<sup>0.5</sup>（目前单阶段P5小模型中最好的）准度达到了53FPS的速度。</li> <li>我们提供了更加强大的数据增强方法，可以在数据集标签稀疏时起到更好的效果。</li> <li>在训练末尾阶段使用RH损失函数，中小模型的检测效果有所提升。</li> <li>我们的论文（预印版）已在<a href="https://arxiv.org/abs/2302.07483" rel="nofollow"><strong>arxiv</strong></a>公布</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">更新</h2><a id="user-content-更新" class="anchor" aria-label="Permalink: 更新" href="#更新"><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"><strong>[2024/12/7] 重大更新</strong> <br></p> <ol dir="auto"> <li>上传了多平台统一化部署项目，无需针对不同平台修改代码，详见目录<a href="/LSH9832/edgeyolo/blob/main/deployment">deployment</a></li> </ol> <p dir="auto"><strong>[2024/3/16]</strong> <br></p> <ol dir="auto"> <li>上传了<a href="https://github.com/LSH9832/edgeyolo/blob/main/demo/amct_onnx2om.py">demo/amct_onnx2om.py</a> 该脚本用于将onnx模型转换为华为昇腾系列设备上支持的om模型(比如昇腾310)，并且发布<a href="https://github.com/LSH9832/edgeyolo/blob/main/cpp/ascend">c++部署代码示例</a>. （注意，你必须拥有华为官网提供的相应依赖库和工具，部分依赖仅购买相应硬件的用户才有下载权限）</li> </ol> <p dir="auto"><strong>[2024/3/6]</strong> <br></p> <ol dir="auto"> <li>我们上传了用于方便训练和模型导出的<a href="#%E5%A6%82%E6%9E%9C%E4%BD%A0%E6%83%B3%E4%BD%BF%E7%94%A8docker%E9%82%A3%E4%B9%88">docker镜像</a>，可以方便地将模型一键导出为RKNN，MNN，地平线J5，Jetson等平台支持的部署模型</li> </ol> <p dir="auto"><strong>[2023/12/6]</strong> <br></p> <ol dir="auto"> <li><a href="https://github.com/LSH9832/edgeyolo/tree/main/cpp/rknn">RKNN模型推理部署代码(仅用于RK3588芯片)</a>已发布。 <br></li> </ol> <p dir="auto"><strong>[2023/11/23]</strong> <br></p> <ol dir="auto"> <li><a href="https://github.com/LSH9832/edgeyolo/tree/main/cpp/mnn">MNN模型推理部署代码</a>已发布。 <br></li> </ol> <p dir="auto"><strong>[2023/2/28]</strong> <br></p> <ol dir="auto"> <li>现在支持对TensorRT模型进行evaluation了。 <br></li> </ol> <p dir="auto"><strong>[2023/2/24]</strong> <br></p> <ol dir="auto"> <li>现在edgeyolo支持<a href="https://github.com/LSH9832/edgeyolo/blob/main/params/dataset/yolo.yaml">YOLO格式的数据集</a>了 <br></li> <li>修复了一些已知错误（在Linux下的cpp代码使用--loop选项时的错误以及分布式训练对标签进行缓存时发生的错误）</li> </ol> <p dir="auto"><strong>[2023/2/20]</strong> <br></p> <ol dir="auto"> <li><a href="https://github.com/LSH9832/edgeyolo/tree/main/cpp/console">TensorRT C++ 命令行演示代码</a> (需要用到<strong>opencv</strong> 和 <strong>qt5</strong>的库) <br></li> <li>修复了使用7.X版本的TensorRT导出模型时产生的bugs<br></li> </ol> <p dir="auto"><strong>[2023/2/19]</strong> <br></p> <ol dir="auto"> <li>发布带有校准训练过程的TensorRT int8模型导出代码 <br></li> </ol> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">即将到来</h2><a id="user-content-即将到来" class="anchor" aria-label="Permalink: 即将到来" href="#即将到来"><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>用于训练和一键式导出边缘设备支持的模型（RKNN/地平线J5/Jetson等）</li> <li>重构TensorRT部署代码，使其更方便使用</li> <li>更多不同的模型</li> <li>用于TensorRT推理的、带有界面的C++代码</li> <li>用于实例分割任务EdgeYOLO-mask模型</li> <li>简单有效的预训练方法</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">模型</h2><a id="user-content-模型" class="anchor" aria-label="Permalink: 模型" href="#模型"><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>在COCO2017-train上训练的模型</li> </ul> <markdown-accessiblity-table><table> <thead> <tr> <th align="left">模型</th> <th align="center">输入大小</th> <th align="center">mAP<sup>val<br>0.5:0.95</sup></th> <th align="center">mAP<sup>val<br>0.5</sup></th> <th align="center">FPS<sup>AGX Xavier<br>trt fp16 批大小=16 <br>包含NMS</sup></th> <th align="center">参数量<br>train / infer<br><sup>(M)</sup></th> <th align="center">下载</th> </tr> </thead> <tbody> <tr> <td align="left"><strong>EdgeYOLO-Tiny-LRELU</strong></td> <td align="center">416<br>640</td> <td align="center">33.1<br>37.8</td> <td align="center">50.5<br>56.7</td> <td align="center"><strong>206</strong><br>109</td> <td align="center">7.6 / 7.0</td> <td align="center"><a href="https://github.com/LSH9832/edgeyolo/releases/download/v0.0.0/edgeyolo_tiny_lrelu_coco.pth"><strong>github</strong></a></td> </tr> <tr> <td align="left"><strong>EdgeYOLO-Tiny</strong></td> <td align="center">416<br>640</td> <td align="center">37.2<br>41.4</td> <td align="center">55.4<br>60.4</td> <td align="center">136<br>67</td> <td align="center">5.8 / 5.5</td> <td align="center"><a href="https://github.com/LSH9832/edgeyolo/releases/download/v0.0.0/edgeyolo_tiny_coco.pth"><strong>github</strong></a></td> </tr> <tr> <td align="left"><strong>EdgeYOLO-S</strong></td> <td align="center">640</td> <td align="center">44.1</td> <td align="center"><strong>63.3</strong></td> <td align="center">53</td> <td align="center">9.9 / 9.3</td> <td align="center"><a href="https://github.com/LSH9832/edgeyolo/releases/download/v0.0.0/edgeyolo_s_coco.pth"><strong>github</strong></a></td> </tr> <tr> <td align="left"><strong>EdgeYOLO-M</strong></td> <td align="center">640</td> <td align="center">47.5</td> <td align="center">66.6</td> <td align="center">46</td> <td align="center">19.0 / 17.8</td> <td align="center"><a href="https://github.com/LSH9832/edgeyolo/releases/download/v0.0.0/edgeyolo_m_coco.pth"><strong>github</strong></a></td> </tr> <tr> <td align="left"><strong>EdgeYOLO</strong></td> <td align="center">640</td> <td align="center">50.6</td> <td align="center">69.8</td> <td align="center">34</td> <td align="center">41.2 / 40.5</td> <td align="center"><a href="https://github.com/LSH9832/edgeyolo/releases/download/v0.0.0/edgeyolo_coco.pth"><strong>github</strong></a></td> </tr> </tbody> </table></markdown-accessiblity-table> <ul dir="auto"> <li>在VisDrone2019上训练的模型 (这些模型骨干网络初始参数来自于上面的模型)</li> </ul> <ol dir="auto"> <li>训练时使用的是 <a href="https://github.com/LSH9832/edgeyolo/releases/download/v1.0.0/visdrone_coco.zip">已转化为COCO格式的 VisDrone2019 数据集</a> 。</li> <li>以下是没有去掉在<strong>ignored region</strong>中检测框的情况下测得的结果</li> </ol> <markdown-accessiblity-table><table> <thead> <tr> <th align="left">模型</th> <th align="center">输入大小</th> <th align="center">mAP<sup>val<br>0.5:0.95</sup></th> <th align="center">mAP<sup>val<br>0.5</sup></th> <th align="center">下载</th> </tr> </thead> <tbody> <tr> <td align="left"><strong>EdgeYOLO-Tiny-LRELU</strong></td> <td align="center">416<br>640</td> <td align="center">12.1<br>18.5</td> <td align="center">22.8<br>33.6</td> <td align="center"><a href="https://github.com/LSH9832/edgeyolo/releases/download/v0.0.0/edgeyolo_tiny_lrelu_visdrone.pth"><strong>github</strong></a></td> </tr> <tr> <td align="left"><strong>EdgeYOLO-Tiny</strong></td> <td align="center">416<br>640</td> <td align="center">14.9<br>21.8</td> <td align="center">27.3<br>38.5</td> <td align="center"><a href="https://github.com/LSH9832/edgeyolo/releases/download/v0.0.0/edgeyolo_tiny_visdrone.pth"><strong>github</strong></a></td> </tr> <tr> <td align="left"><strong>EdgeYOLO-S</strong></td> <td align="center">640</td> <td align="center">23.6</td> <td align="center">40.8</td> <td align="center"><a href="https://github.com/LSH9832/edgeyolo/releases/download/v0.0.0/edgeyolo_s_visdrone.pth"><strong>github</strong></a></td> </tr> <tr> <td align="left"><strong>EdgeYOLO-M</strong></td> <td align="center">640</td> <td align="center">25.0</td> <td align="center">42.9</td> <td align="center"><a href="https://github.com/LSH9832/edgeyolo/releases/download/v0.0.0/edgeyolo_m_visdrone.pth"><strong>github</strong></a></td> </tr> <tr> <td align="left"><strong>EdgeYOLO</strong></td> <td align="center">640</td> <td align="center">25.9<br>26.9</td> <td align="center">43.9<br>45.4</td> <td align="center"><a href="https://github.com/LSH9832/edgeyolo/releases/download/v0.0.0/edgeyolo_visdrone.pth"><strong>github(原)</strong></a><br><a href="https://github.com/LSH9832/edgeyolo/releases/download/v1.0.0/edgeyolo_visdrone.pth"><strong>github(新)</strong></a></td> </tr> </tbody> </table></markdown-accessiblity-table> <details> <summary>在 COCO2017 上的部分检测结果</summary> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/LSH9832/edgeyolo/blob/main/assets/coco.jpg"><img src="/LSH9832/edgeyolo/raw/main/assets/coco.jpg" alt="" style="max-width: 100%;"></a></p> </details> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">快速使用</h2><a id="user-content-快速使用" class="anchor" aria-label="Permalink: 快速使用" href="#快速使用"><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"><h3 tabindex="-1" class="heading-element" dir="auto">安装</h3><a id="user-content-安装" class="anchor" aria-label="Permalink: 安装" href="#安装"><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="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="git clone https://github.com/LSH9832/edgeyolo.git cd edgeyolo pip install -r requirements.txt"><pre>git clone https://github.com/LSH9832/edgeyolo.git <span class="pl-c1">cd</span> edgeyolo pip install -r requirements.txt</pre></div> <p dir="auto">如果使用TensorRT，请确保设备上已经安装好torch2trt和TensorRT Development Toolkit(version>7.1.3.0)。</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="git clone https://github.com/NVIDIA-AI-IOT/torch2trt.git cd torch2trt python setup.py install"><pre>git clone https://github.com/NVIDIA-AI-IOT/torch2trt.git <span class="pl-c1">cd</span> torch2trt python setup.py install</pre></div> <p dir="auto">或者为了保证和我们用的是同一个版本的torch2trt, <a href="https://github.com/LSH9832/edgeyolo/releases/download/v1.0.0/torch2trt.zip">请在这里下载</a></p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">如果你想使用docker那么</h4><a id="user-content-如果你想使用docker那么" class="anchor" aria-label="Permalink: 如果你想使用docker那么" href="#如果你想使用docker那么"><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>从<a href="https://pan.baidu.com/s/1D9ReGtI-iM79RoEGat1eAw?pwd=ujar" rel="nofollow">百度网盘, 14.3G</a>下载docker镜像, 分享码: ujar</li> <li>然后导入镜像</li> </ul> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="docker import edgeyolo_deploy.tar.gz edgeyolo:latest"><pre>docker import edgeyolo_deploy.tar.gz edgeyolo:latest</pre></div> <ul dir="auto"> <li>运行镜像</li> </ul> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="docker run -it \ --runtime=nvidia \ -e NVIDIA_DRIVER_CAPABILITIES=compute,utility \ -e NVIDIA_VISIBLE_DEVICES=all \ --shm-size 15g \ -w /code \ -v "/path/to/your/edgeyolo/parent_dir":/code \ -v "/path/to/your/dataset/parent_dir":/dataset \ edgeyolo:latest"><pre>docker run -it \ --runtime=nvidia \ -e NVIDIA_DRIVER_CAPABILITIES=compute,utility \ -e NVIDIA_VISIBLE_DEVICES=all \ --shm-size 15g \ -w /code \ -v <span class="pl-s"><span class="pl-pds">"</span>/path/to/your/edgeyolo/parent_dir<span class="pl-pds">"</span></span>:/code \ -v <span class="pl-s"><span class="pl-pds">"</span>/path/to/your/dataset/parent_dir<span class="pl-pds">"</span></span>:/dataset \ edgeyolo:latest</pre></div> <p dir="auto">之后你就可以在docker镜像里使用 "<a href="https://github.com/LSH9832/edgeyolo/blob/main/docker_export.py">docker_export.py</a>" 导出模型而不是使用 "<a href="https://github.com/LSH9832/edgeyolo/blob/main/export.py">export.py</a>" 了，这将会更加方便。</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">推理</h3><a id="user-content-推理" class="anchor" aria-label="Permalink: 推理" href="#推理"><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"><strong>首先 <a href="https://github.com/LSH9832/edgeyolo/releases/tag/v0.0.0">在此下载模型的权重文件</a></strong></p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python detect.py --weights edgeyolo_coco.pth --source XXX.mp4 --fp16 # 完整命令参数 python detect.py --weights edgeyolo_coco.pth # 权重文件 --source /XX/XXX.mp4 # 或网络视频流地址，或目录下全部为图片的文件夹, 或rosbag（xxx.bag） --topic /camera/xxx # 仅当使用rosbag或从ros中某个话题提取图片时使用该选项 --conf-thres 0.25 # 置信度阈值 --nms-thres 0.5 # 重合度阈值 --input-size 640 640 # 输入大小 --batch 1 # 批大小 --save-dir ./output/detect/imgs # 如果按下键盘"s"键，当前图像将保存在该文件夹中 --fp16 # 半精度推理 --no-fuse # 不进行重参数化 --no-label # 不显示带有类别和置信度的标签，仅画框 --mp # 使用多进程，当 batch>1 时可以使图像显示更流畅 --fps 30 # 最大FPS限制, 仅在使用 --mp 选项时有效"><pre>python detect.py --weights edgeyolo_coco.pth --source XXX.mp4 --fp16 <span class="pl-c"><span class="pl-c">#</span> 完整命令参数</span> python detect.py --weights edgeyolo_coco.pth <span class="pl-c"><span class="pl-c">#</span> 权重文件</span> --source /XX/XXX.mp4 <span class="pl-c"><span class="pl-c">#</span> 或网络视频流地址，或目录下全部为图片的文件夹, 或rosbag（xxx.bag）</span> --topic /camera/xxx <span class="pl-c"><span class="pl-c">#</span> 仅当使用rosbag或从ros中某个话题提取图片时使用该选项</span> --conf-thres 0.25 <span class="pl-c"><span class="pl-c">#</span> 置信度阈值</span> --nms-thres 0.5 <span class="pl-c"><span class="pl-c">#</span> 重合度阈值</span> --input-size 640 640 <span class="pl-c"><span class="pl-c">#</span> 输入大小</span> --batch 1 <span class="pl-c"><span class="pl-c">#</span> 批大小</span> --save-dir ./output/detect/imgs <span class="pl-c"><span class="pl-c">#</span> 如果按下键盘"s"键，当前图像将保存在该文件夹中</span> --fp16 <span class="pl-c"><span class="pl-c">#</span> 半精度推理</span> --no-fuse <span class="pl-c"><span class="pl-c">#</span> 不进行重参数化</span> --no-label <span class="pl-c"><span class="pl-c">#</span> 不显示带有类别和置信度的标签，仅画框</span> --mp <span class="pl-c"><span class="pl-c">#</span> 使用多进程，当 batch>1 时可以使图像显示更流畅</span> --fps 30 <span class="pl-c"><span class="pl-c">#</span> 最大FPS限制, 仅在使用 --mp 选项时有效</span></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">训练</h3><a id="user-content-训练" class="anchor" aria-label="Permalink: 训练" href="#训练"><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>首先准备好你的数据集并创建好相应的数据集配置文件(./params/dataset/XXX.yaml)，配置文件中应包含如下信息:</li> </ul> <p dir="auto">（目前支持COCO、YOLO、VOC、VisDrone、DOTA五种格式的数据集）</p> <div class="highlight highlight-source-yaml notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="type: "coco" # 数据集格式（小写），目前支持COCO格式、YOLO格式、VOC格式、VisDrone格式、DOTA格式 dataset_path: "/dataset/coco2017" # 数据集根目录 kwargs: suffix: "jpg" # 数据集图片后缀名 use_cache: true # 使用缓存文件，在i5-12490f上测试完整加载时间：有分割标签979MB：52s -> 10s, 无分割标签228MB：39s -> 4s train: image_dir: "images/train2017" # 训练集图片文件夹 label: "annotations/instances_train2017.json" # 训练集标签文件（单文件格式）或文件夹（多文件格式） val: image_dir: "images/val2017" # 验证集图片文件夹 label: "annotations/instances_val2017.json" # 验证集标签文件（单文件格式）或文件夹（多文件格式） test: test_dir: "test2017" # 测试集图片文件夹（代码中暂未使用，后续将会完善） segmentaion_enabled: true # 是否有并且使用实例分割标签代替目标框标签进行训练 names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'] # 类别名称"><pre><span class="pl-ent">type</span>: <span class="pl-s"><span class="pl-pds">"</span>coco<span class="pl-pds">"</span></span> <span class="pl-c"><span class="pl-c">#</span> 数据集格式（小写），目前支持COCO格式、YOLO格式、VOC格式、VisDrone格式、DOTA格式</span> <span class="pl-ent">dataset_path</span>: <span class="pl-s"><span class="pl-pds">"</span>/dataset/coco2017<span class="pl-pds">"</span></span> <span class="pl-c"><span class="pl-c">#</span> 数据集根目录</span> <span class="pl-ent">kwargs</span>: <span class="pl-ent">suffix</span>: <span class="pl-s"><span class="pl-pds">"</span>jpg<span class="pl-pds">"</span></span> <span class="pl-c"><span class="pl-c">#</span> 数据集图片后缀名</span> <span class="pl-ent">use_cache</span>: <span class="pl-s">true </span><span class="pl-c"><span class="pl-c">#</span> 使用缓存文件，在i5-12490f上测试完整加载时间：有分割标签979MB：52s -> 10s, 无分割标签228MB：39s -> 4s</span> <span class="pl-ent">train</span>: <span class="pl-ent">image_dir</span>: <span class="pl-s"><span class="pl-pds">"</span>images/train2017<span class="pl-pds">"</span></span> <span class="pl-c"><span class="pl-c">#</span> 训练集图片文件夹</span> <span class="pl-ent">label</span>: <span class="pl-s"><span class="pl-pds">"</span>annotations/instances_train2017.json<span class="pl-pds">"</span></span> <span class="pl-c"><span class="pl-c">#</span> 训练集标签文件（单文件格式）或文件夹（多文件格式）</span> <span class="pl-ent">val</span>: <span class="pl-ent">image_dir</span>: <span class="pl-s"><span class="pl-pds">"</span>images/val2017<span class="pl-pds">"</span></span> <span class="pl-c"><span class="pl-c">#</span> 验证集图片文件夹</span> <span class="pl-ent">label</span>: <span class="pl-s"><span class="pl-pds">"</span>annotations/instances_val2017.json<span class="pl-pds">"</span></span> <span class="pl-c"><span class="pl-c">#</span> 验证集标签文件（单文件格式）或文件夹（多文件格式）</span> <span class="pl-ent">test</span>: <span class="pl-ent">test_dir</span>: <span class="pl-s"><span class="pl-pds">"</span>test2017<span class="pl-pds">"</span></span> <span class="pl-c"><span class="pl-c">#</span> 测试集图片文件夹（代码中暂未使用，后续将会完善）</span> <span class="pl-ent">segmentaion_enabled</span>: <span class="pl-s">true </span><span class="pl-c"><span class="pl-c">#</span> 是否有并且使用实例分割标签代替目标框标签进行训练</span> <span class="pl-ent">names</span>: <span class="pl-s">['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',</span> <span class="pl-s"><span class="pl-pds">'</span>fire hydrant<span class="pl-pds">'</span></span><span class="pl-s">, 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',</span> <span class="pl-s"><span class="pl-pds">'</span>elephant<span class="pl-pds">'</span></span><span class="pl-s">, 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',</span> <span class="pl-s"><span class="pl-pds">'</span>skis<span class="pl-pds">'</span></span><span class="pl-s">, 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',</span> <span class="pl-s"><span class="pl-pds">'</span>tennis racket<span class="pl-pds">'</span></span><span class="pl-s">, 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',</span> <span class="pl-s"><span class="pl-pds">'</span>sandwich<span class="pl-pds">'</span></span><span class="pl-s">, 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',</span> <span class="pl-s"><span class="pl-pds">'</span>potted plant<span class="pl-pds">'</span></span><span class="pl-s">, 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',</span> <span class="pl-s"><span class="pl-pds">'</span>microwave<span class="pl-pds">'</span></span><span class="pl-s">, 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',</span> <span class="pl-s"><span class="pl-pds">'</span>hair drier<span class="pl-pds">'</span></span><span class="pl-s">, 'toothbrush'] </span><span class="pl-c"><span class="pl-c">#</span> 类别名称</span></pre></div> <ul dir="auto"> <li>修改训练配置文件 ./params/train/train_XXX.yaml中的相应参数</li> <li>最后使用如下命令启动训练</li> </ul> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python train.py --cfg ./params/train/train_XXX.yaml"><pre>python train.py --cfg ./params/train/train_XXX.yaml</pre></div> <p dir="auto">你可以通过"plot.py"绘制损失曲线、学习率曲线和准确度曲线(AP50 和 AP50:95)</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python plot.py --all \ # 绘制所有曲线或 (--lr：学习率, --ap：准确度, --loss：损失) -f ./output/train/edgeyolo_tiny_lrelu \ # 训练的输出文件夹路径或 (文件路径：--ap则使用output_path/eval.yaml， --lr和--loss则使用output_path/log.txt) --no-show \ # 不使用plt.show()显示曲线, 适用于无图形界面的设备（如云服务器），或你仅仅想保存图片 --save \ # 保存图片 --format pdf png svg jpg eps # 保存图片的格式，支持多种格式同时保存"><pre>python plot.py --all <span class="pl-cce">\ </span> <span class="pl-c"><span class="pl-c">#</span> 绘制所有曲线或 (--lr：学习率, --ap：准确度, --loss：损失)</span> -f ./output/train/edgeyolo_tiny_lrelu <span class="pl-cce">\ </span> <span class="pl-c"><span class="pl-c">#</span> 训练的输出文件夹路径或 (文件路径：--ap则使用output_path/eval.yaml， --lr和--loss则使用output_path/log.txt)</span> --no-show <span class="pl-cce">\ </span> <span class="pl-c"><span class="pl-c">#</span> 不使用plt.show()显示曲线, 适用于无图形界面的设备（如云服务器），或你仅仅想保存图片</span> --save <span class="pl-cce">\ </span> <span class="pl-c"><span class="pl-c">#</span> 保存图片</span> --format pdf png svg jpg eps <span class="pl-c"><span class="pl-c">#</span> 保存图片的格式，支持多种格式同时保存</span></pre></div> <details> <summary>绘图将如下所示</summary> <p dir="auto">准确度曲线 <a target="_blank" rel="noopener noreferrer" href="https://private-user-images.githubusercontent.com/69880398/300196480-091cddab-c79e-44ae-bead-43391c7d8916.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.Olkfk3JrWZtJ-KLlrm4Ee6T_SKI4SR_y-G-RdOofr3E"><img src="https://private-user-images.githubusercontent.com/69880398/300196480-091cddab-c79e-44ae-bead-43391c7d8916.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.Olkfk3JrWZtJ-KLlrm4Ee6T_SKI4SR_y-G-RdOofr3E" alt="ap" style="max-width: 100%;"></a></p> <p dir="auto">损失曲线 <a target="_blank" rel="noopener noreferrer" href="https://private-user-images.githubusercontent.com/69880398/300196518-51de4116-2c4a-4a35-ab95-62a41f791749.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.CWniUzO_Gw1ytH3cCDi5Z_vhumhQ_gU3_483PFSgRbY"><img src="https://private-user-images.githubusercontent.com/69880398/300196518-51de4116-2c4a-4a35-ab95-62a41f791749.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3NDQyNjM4OTMsIm5iZiI6MTc0NDI2MzU5MywicGF0aCI6Ii82OTg4MDM5OC8zMDAxOTY1MTgtNTFkZTQxMTYtMmM0YS00YTM1LWFiOTUtNjJhNDFmNzkxNzQ5LnBuZz9YLUFtei1BbGdvcml0aG09QVdTNC1ITUFDLVNIQTI1NiZYLUFtei1DcmVkZW50aWFsPUFLSUFWQ09EWUxTQTUzUFFLNFpBJTJGMjAyNTA0MTAlMkZ1cy1lYXN0LTElMkZzMyUyRmF3czRfcmVxdWVzdCZYLUFtei1EYXRlPTIwMjUwNDEwVDA1Mzk1M1omWC1BbXotRXhwaXJlcz0zMDAmWC1BbXotU2lnbmF0dXJlPWViNjk0OTJmZjcwN2Y4YzI1N2M4ZmU0MWVkZDhjMjZmNTk3YjgwZGY1ZjQzNGNmOWVjZmJhNWFhNGI4YzA5ZDUmWC1BbXotU2lnbmVkSGVhZGVycz1ob3N0In0.CWniUzO_Gw1ytH3cCDi5Z_vhumhQ_gU3_483PFSgRbY" alt="loss" style="max-width: 100%;"></a></p> <p dir="auto">学习率曲线 <a target="_blank" rel="noopener noreferrer" href="https://private-user-images.githubusercontent.com/69880398/300196521-1be1d98d-5440-4e2d-8f8c-89aa9f07bd42.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.24l0eeDmcF_qGF9d8Qy_ZoXTmiUnBGlSSQdX3fh9smg"><img src="https://private-user-images.githubusercontent.com/69880398/300196521-1be1d98d-5440-4e2d-8f8c-89aa9f07bd42.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.24l0eeDmcF_qGF9d8Qy_ZoXTmiUnBGlSSQdX3fh9smg" alt="lr" style="max-width: 100%;"></a></p> </details> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">验证</h3><a id="user-content-验证" class="anchor" aria-label="Permalink: 验证" href="#验证"><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="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python evaluate.py --weights edgeyolo_coco.pth --dataset params/dataset/XXX.yaml --batch 16 --device 0 # 完整命令参数 python evaluate.py --weights edgeyolo_coco.pth # 权重文件， 也可是tensorrt模型，output/export/edgeyolo_coco/model.pt --dataset params/dataset/XXX.yaml # 数据集配置文件 --batch 16 # 每一个GPU上的批大小 --device 0 # 只用一个就写0就行 --input-size 640 640 # 高、宽（注意别反了） --trt # 如果使用的是TensorRT模型需要加上此选项 --save # 保存没有优化器参数的权重文件，并将训练次数重置为-1"><pre>python evaluate.py --weights edgeyolo_coco.pth --dataset params/dataset/XXX.yaml --batch 16 --device 0 <span class="pl-c"><span class="pl-c">#</span> 完整命令参数</span> python evaluate.py --weights edgeyolo_coco.pth <span class="pl-c"><span class="pl-c">#</span> 权重文件， 也可是tensorrt模型，output/export/edgeyolo_coco/model.pt</span> --dataset params/dataset/XXX.yaml <span class="pl-c"><span class="pl-c">#</span> 数据集配置文件</span> --batch 16 <span class="pl-c"><span class="pl-c">#</span> 每一个GPU上的批大小</span> --device 0 <span class="pl-c"><span class="pl-c">#</span> 只用一个就写0就行</span> --input-size 640 640 <span class="pl-c"><span class="pl-c">#</span> 高、宽（注意别反了）</span> --trt <span class="pl-c"><span class="pl-c">#</span> 如果使用的是TensorRT模型需要加上此选项</span> --save <span class="pl-c"><span class="pl-c">#</span> 保存没有优化器参数的权重文件，并将训练次数重置为-1</span></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">导出 onnx & tensorrt</h3><a id="user-content-导出-onnx--tensorrt" class="anchor" aria-label="Permalink: 导出 onnx & tensorrt" href="#导出-onnx--tensorrt"><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>ONNX</li> </ul> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python export.py --onnx --weights edgeyolo_coco.pth --batch 1 # all options python export.py --onnx # 如果没有安装tensorrt和torch2trt，用--onnx-only代替 --weights edgeyolo_coco.pth --input-size 640 640 # 宽、高（注意别反了） --batch 1 --opset 11 --no-simplify # 不简化模型结构"><pre>python export.py --onnx --weights edgeyolo_coco.pth --batch 1 <span class="pl-c"><span class="pl-c">#</span> all options</span> python export.py --onnx <span class="pl-c"><span class="pl-c">#</span> 如果没有安装tensorrt和torch2trt，用--onnx-only代替</span> --weights edgeyolo_coco.pth --input-size 640 640 <span class="pl-c"><span class="pl-c">#</span> 宽、高（注意别反了）</span> --batch 1 --opset 11 --no-simplify <span class="pl-c"><span class="pl-c">#</span> 不简化模型结构</span></pre></div> <p dir="auto">将生成</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="output/export/edgeyolo_coco/640x640_batch1.onnx"><pre class="notranslate"><code>output/export/edgeyolo_coco/640x640_batch1.onnx </code></pre></div> <ul dir="auto"> <li>TensorRT</li> </ul> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# fp16 python export.py --trt --weights edgeyolo_coco.pth --batch 1 --workspace 8 # int8 python export.py --trt --weights edgeyolo_coco.pth --batch 1 --workspace 8 --int8 --dataset params/dataset/coco.yaml --num-imgs 1024 # 完整选项 python export.py --trt # 你可以添加上述--onnx和其相关选项，同时导出两种模型 --weights edgeyolo_coco.pth --input-size 640 640 # 宽、高（注意别反了） --batch 1 --workspace 10 # (GB) --no-fp16 # 默认输出fp16精度模型，使用此选项关闭，转为fp32精度 --int8 # 输出int8精度，需要用到以下选项用于校准训练 --datset params/dataset/coco.yaml # 校准训练图片来源于该数据集的验证集图片（上限5120张） --train # 使用训练集图片而不是验证集图片（上限5120张） --all # 使用训练集和验证集所有图片（上限5120张） --num-imgs 512 # （上限5120张）"><pre><span class="pl-c"><span class="pl-c">#</span> fp16</span> python export.py --trt --weights edgeyolo_coco.pth --batch 1 --workspace 8 <span class="pl-c"><span class="pl-c">#</span> int8</span> python export.py --trt --weights edgeyolo_coco.pth --batch 1 --workspace 8 --int8 --dataset params/dataset/coco.yaml --num-imgs 1024 <span class="pl-c"><span class="pl-c">#</span> 完整选项</span> python export.py --trt <span class="pl-c"><span class="pl-c">#</span> 你可以添加上述--onnx和其相关选项，同时导出两种模型</span> --weights edgeyolo_coco.pth --input-size 640 640 <span class="pl-c"><span class="pl-c">#</span> 宽、高（注意别反了）</span> --batch 1 --workspace 10 <span class="pl-c"><span class="pl-c">#</span> (GB)</span> --no-fp16 <span class="pl-c"><span class="pl-c">#</span> 默认输出fp16精度模型，使用此选项关闭，转为fp32精度</span> --int8 <span class="pl-c"><span class="pl-c">#</span> 输出int8精度，需要用到以下选项用于校准训练</span> --datset params/dataset/coco.yaml <span class="pl-c"><span class="pl-c">#</span> 校准训练图片来源于该数据集的验证集图片（上限5120张）</span> --train <span class="pl-c"><span class="pl-c">#</span> 使用训练集图片而不是验证集图片（上限5120张）</span> --all <span class="pl-c"><span class="pl-c">#</span> 使用训练集和验证集所有图片（上限5120张）</span> --num-imgs 512 <span class="pl-c"><span class="pl-c">#</span> （上限5120张）</span></pre></div> <p dir="auto">将产生</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="(可选) output/export/edgeyolo_coco/640x640_batch1.onnx output/export/edgeyolo_coco/640x640_batch1_fp16(int8).pt # 用于 python 推理 output/export/edgeyolo_coco/640x640_batch1_fp16(int8).engine # 用于 c++ 推理 output/export/edgeyolo_coco/640x640_batch1_fp16(int8).json # 用于 c++ 推理"><pre class="notranslate"><code>(可选) output/export/edgeyolo_coco/640x640_batch1.onnx output/export/edgeyolo_coco/640x640_batch1_fp16(int8).pt # 用于 python 推理 output/export/edgeyolo_coco/640x640_batch1_fp16(int8).engine # 用于 c++ 推理 output/export/edgeyolo_coco/640x640_batch1_fp16(int8).json # 用于 c++ 推理 </code></pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">TensorRT Int8 量化模型基准测试</h4><a id="user-content-tensorrt-int8-量化模型基准测试" class="anchor" aria-label="Permalink: TensorRT Int8 量化模型基准测试" href="#tensorrt-int8-量化模型基准测试"><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>测试环境: TensorRT版本8.2.5.1, Windows, i5-12490F, RTX 3060 12GB</li> <li>对于TensorRT来说，使用不同的校准数据集，产生的int8模型之间的精度和速度都可能有很明显的差异，这是由于tensorrt不是强制转换为int8的，有些层它觉得转了后效果不好就不转了，所以我认为这是为什么大部分官方项目都不给出int8量化效果的代码。我自认为下表结果并不具有多少参考意义。</li> </ul> <p dir="auto">COCO2017-TensorRT-int8</p> <markdown-accessiblity-table><table> <thead> <tr> <th align="left">Int8 模型</th> <th align="center">尺寸</th> <th align="center">校准图片数量</th> <th align="center">工作内存空间<br><sup>(GB)</sup></th> <th align="center">mAP<sup>val<br>0.5:0.95</sup></th> <th align="center">mAP<sup>val<br>0.5</sup></th> <th align="center">FPS<sup>RTX 3060<br>trt int8 batch=16 <br>include NMS</sup></th> </tr> </thead> <tbody> <tr> <td align="left"><strong>Tiny-LRELU</strong></td> <td align="center">416<br>640</td> <td align="center">512</td> <td align="center">8</td> <td align="center">31.5<br>36.4</td> <td align="center">48.7<br>55.5</td> <td align="center">730<br>360</td> </tr> <tr> <td align="left"><strong>Tiny</strong></td> <td align="center">416<br>640</td> <td align="center">512</td> <td align="center">8</td> <td align="center">34.9<br>39.8</td> <td align="center">53.1<br>59.5</td> <td align="center">549<br>288</td> </tr> <tr> <td align="left"><strong>S</strong></td> <td align="center">640</td> <td align="center">512</td> <td align="center">8</td> <td align="center">42.4</td> <td align="center">61.8</td> <td align="center">233</td> </tr> <tr> <td align="left"><strong>M</strong></td> <td align="center">640</td> <td align="center">512</td> <td align="center">8</td> <td align="center">45.2</td> <td align="center">64.2</td> <td align="center">211</td> </tr> <tr> <td align="left"><strong>L</strong></td> <td align="center">640</td> <td align="center">512</td> <td align="center">8</td> <td align="center">49.1</td> <td align="center">68.0</td> <td align="center">176</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">python推理</h4><a id="user-content-python推理" class="anchor" aria-label="Permalink: python推理" href="#python推理"><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="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python detect.py --trt --weights output/export/edgeyolo_coco/640x640_batch1_int8.pt --source XXX.mp4 # full commands python detect.py --trt --weights output/export/edgeyolo_coco/640x640_batch1_int8.pt --source XXX.mp4 --legacy # 如果训练时"img = img / 255"（图像输入归一化） --use-decoder # 如果使用早期的YOLOX（v0.2.0及以前）的tensorrt模型 --mp # 使用多进程，当 batch>1 时可以使图像显示更流畅 --fps 30 # 最大FPS限制, 仅在使用 --mp 选项时有效"><pre>python detect.py --trt --weights output/export/edgeyolo_coco/640x640_batch1_int8.pt --source XXX.mp4 <span class="pl-c"><span class="pl-c">#</span> full commands</span> python detect.py --trt --weights output/export/edgeyolo_coco/640x640_batch1_int8.pt --source XXX.mp4 --legacy <span class="pl-c"><span class="pl-c">#</span> 如果训练时"img = img / 255"（图像输入归一化）</span> --use-decoder <span class="pl-c"><span class="pl-c">#</span> 如果使用早期的YOLOX（v0.2.0及以前）的tensorrt模型</span> --mp <span class="pl-c"><span class="pl-c">#</span> 使用多进程，当 batch>1 时可以使图像显示更流畅</span> --fps 30 <span class="pl-c"><span class="pl-c">#</span> 最大FPS限制, 仅在使用 --mp 选项时有效</span></pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">c++ 推理</h4><a id="user-content-c-推理" class="anchor" aria-label="Permalink: c++ 推理" href="#c-推理"><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="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# 编译 cd cpp/console/linux mkdir build && cd build cmake .. make -j4 # 帮助 ./yolo -? ./yolo --help # 运行 # ./yolo [json文件（需要与engine文件同名）] [图像源] [--conf 置信度阈值] [--nms IOU阈值] [--loop 循环播放] [--no-label 不画标签] ./yolo ../../../../output/export/edgeyolo_coco/640x640_batch1_int8.json ~/Videos/test.avi --conf 0.25 --nms 0.5 --loop --no-label"><pre><span class="pl-c"><span class="pl-c">#</span> 编译</span> <span class="pl-c1">cd</span> cpp/console/linux mkdir build <span class="pl-k">&&</span> <span class="pl-c1">cd</span> build cmake .. make -j4 <span class="pl-c"><span class="pl-c">#</span> 帮助</span> ./yolo -<span class="pl-k">?</span> ./yolo --help <span class="pl-c"><span class="pl-c">#</span> 运行</span> <span class="pl-c"><span class="pl-c">#</span> ./yolo [json文件（需要与engine文件同名）] [图像源] [--conf 置信度阈值] [--nms IOU阈值] [--loop 循环播放] [--no-label 不画标签]</span> ./yolo ../../../../output/export/edgeyolo_coco/640x640_batch1_int8.json <span class="pl-k">~</span>/Videos/test.avi --conf 0.25 --nms 0.5 --loop --no-label</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">引用EdgeYOLO</h2><a id="user-content-引用edgeyolo" class="anchor" aria-label="Permalink: 引用EdgeYOLO" href="#引用edgeyolo"><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="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@article{edgeyolo2023, title={EdgeYOLO: An Edge-Real-Time Object Detector}, author={Shihan Liu, Junlin Zha, Jian Sun, Zhuo Li, and Gang Wang}, journal={arXiv preprint arXiv:2302.07483}, year={2023} }"><pre class="notranslate"><code>@article{edgeyolo2023, title={EdgeYOLO: An Edge-Real-Time Object Detector}, author={Shihan Liu, Junlin Zha, Jian Sun, Zhuo Li, and Gang Wang}, journal={arXiv preprint arXiv:2302.07483}, year={2023} } </code></pre></div> <p dir="auto"><a href="https://star-history.com/#LSH9832/edgeyolo&Timeline" rel="nofollow"><img src="https://camo.githubusercontent.com/6d7f90eaff6f87c1e1a80d9ccb1169e3ed980bf8bf760001b15b856800c73b6a/68747470733a2f2f6170692e737461722d686973746f72792e636f6d2f7376673f7265706f733d4c5348393833322f65646765796f6c6f26747970653d54696d656c696e65" alt="Star History Chart" data-canonical-src="https://api.star-history.com/svg?repos=LSH9832/edgeyolo&type=Timeline" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">目前发现的bugs</h2><a id="user-content-目前发现的bugs" class="anchor" aria-label="Permalink: 目前发现的bugs" href="#目前发现的bugs"><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>在训练时有时可能会触发以下错误，降低pytorch版本至1.8.0应该可以解决这个问题。</li> </ul> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="File "XXX/edgeyolo/edgeyolo/train/loss.py", line 667, in dynamic_k_matching _, pos_idx = torch.topk(cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False) RuntimeError: CUDA error: device-side assert triggered CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1."><pre class="notranslate"><code>File "XXX/edgeyolo/edgeyolo/train/loss.py", line 667, in dynamic_k_matching _, pos_idx = torch.topk(cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False) RuntimeError: CUDA error: device-side assert triggered CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1. </code></pre></div> <ul dir="auto"> <li>对于DOTA数据集，目前我们仅支持单GPU进行训练，请不要使用分布式训练方式对DOTA数据集进行训练，否则无法进行正确的训练，也无法得到正确的结果</li> <li>如果使用TensorRT 8.4.X.X及以上的版本进行半精度模型的转换可能存在丢失大量精度甚至完全没有检出目标的情况，为保证使用请使用TensorRT 7.X.X.X 或 8.2.X.X 的版本</li> </ul> </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="yJ3FPeya2omUt22Ut5oDqyAUkUwIjkE05HebMLO4ne5FVo8GVuGYmKJKoZzcWWvv07W2a8YBL6IPdfJdSBiq6w==" /> </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"> an edge-real-time anchor-free object detector with decent performance </p> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a href="/topics/computer-vision" title="Topic: computer-vision" data-view-component="true" class="topic-tag topic-tag-link"> computer-vision </a> <a href="/topics/deep-learning" title="Topic: deep-learning" data-view-component="true" class="topic-tag topic-tag-link"> deep-learning </a> <a href="/topics/pytorch" title="Topic: pytorch" data-view-component="true" class="topic-tag topic-tag-link"> pytorch </a> <a href="/topics/yolo" title="Topic: yolo" data-view-component="true" class="topic-tag topic-tag-link"> yolo </a> <a href="/topics/object-detection" title="Topic: object-detection" data-view-component="true" class="topic-tag topic-tag-link"> object-detection </a> <a href="/topics/tensorrt" title="Topic: tensorrt" data-view-component="true" class="topic-tag topic-tag-link"> tensorrt </a> <a href="/topics/mnn" title="Topic: mnn" data-view-component="true" class="topic-tag topic-tag-link"> mnn </a> <a href="/topics/edge-computing" title="Topic: edge-computing" data-view-component="true" class="topic-tag topic-tag-link"> edge-computing </a> <a href="/topics/onnx" title="Topic: onnx" data-view-component="true" class="topic-tag topic-tag-link"> onnx </a> <a href="/topics/ascend" title="Topic: ascend" data-view-component="true" class="topic-tag topic-tag-link"> ascend </a> <a href="/topics/anchor-free" title="Topic: anchor-free" data-view-component="true" class="topic-tag topic-tag-link"> anchor-free </a> <a href="/topics/object-detector" title="Topic: object-detector" data-view-component="true" class="topic-tag topic-tag-link"> object-detector </a> <a href="/topics/rknn" title="Topic: rknn" data-view-component="true" class="topic-tag topic-tag-link"> rknn </a> <a href="/topics/rk3588" title="Topic: rk3588" data-view-component="true" class="topic-tag topic-tag-link"> rk3588 </a> <a href="/topics/rk3588s" title="Topic: rk3588s" data-view-component="true" class="topic-tag topic-tag-link"> rk3588s </a> </div> </div> <h3 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1 4 8.5H.75a.75.75 0 0 1 0-1.5h2.742l1.812-4.529A.751.751 0 0 1 6 2Z"></path> </svg> <span class="color-fg-muted">Activity</span></a> </div> <h3 class="sr-only">Stars</h3> <div class="mt-2"> <a href="/LSH9832/edgeyolo/stargazers" 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-star mr-2"> <path d="M8 .25a.75.75 0 0 1 .673.418l1.882 3.815 4.21.612a.75.75 0 0 1 .416 1.279l-3.046 2.97.719 4.192a.751.751 0 0 1-1.088.791L8 12.347l-3.766 1.98a.75.75 0 0 1-1.088-.79l.72-4.194L.818 6.374a.75.75 0 0 1 .416-1.28l4.21-.611L7.327.668A.75.75 0 0 1 8 .25Zm0 2.445L6.615 5.5a.75.75 0 0 1-.564.41l-3.097.45 2.24 2.184a.75.75 0 0 1 .216.664l-.528 3.084 2.769-1.456a.75.75 0 0 1 .698 0l2.77 1.456-.53-3.084a.75.75 0 0 1 .216-.664l2.24-2.183-3.096-.45a.75.75 0 0 1-.564-.41L8 2.694Z"></path> </svg> <strong>475</strong> stars</a> </div> <h3 class="sr-only">Watchers</h3> <div class="mt-2"> <a href="/LSH9832/edgeyolo/watchers" 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-eye mr-2"> <path d="M8 2c1.981 0 3.671.992 4.933 2.078 1.27 1.091 2.187 2.345 2.637 3.023a1.62 1.62 0 0 1 0 1.798c-.45.678-1.367 1.932-2.637 3.023C11.67 13.008 9.981 14 8 14c-1.981 0-3.671-.992-4.933-2.078C1.797 10.83.88 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>11</strong> watching</a> </div> <h3 class="sr-only">Forks</h3> <div class="mt-2"> <a href="/LSH9832/edgeyolo/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>67</strong> forks</a> </div> <div class="mt-2"> <a class="Link--muted" href="/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2FLSH9832%2Fedgeyolo&report=LSH9832+%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" 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<div class="d-flex"> <span class="css-truncate css-truncate-target text-bold mr-2" style="max-width: none;">v1.1.0</span> <span title="Label: Latest" data-view-component="true" class="Label Label--success flex-shrink-0"> Latest </span> </div> <div class="text-small color-fg-muted"><relative-time datetime="2024-12-07T15:13:01Z" class="no-wrap">Dec 7, 2024</relative-time></div> </div> </a> <div data-view-component="true" class="mt-3"> <a text="small" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/LSH9832/edgeyolo/releases" data-view-component="true" class="Link">+ 4 releases</a></div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/users/LSH9832/packages?repo_name=edgeyolo" 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 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id="site-details-dialog"> <details class="details-reset details-overlay details-overlay-dark lh-default color-fg-default hx_rsm" open> <summary role="button" aria-label="Close dialog"></summary> <details-dialog class="Box Box--overlay d-flex flex-column anim-fade-in fast hx_rsm-dialog hx_rsm-modal"> <button class="Box-btn-octicon m-0 btn-octicon position-absolute right-0 top-0" type="button" aria-label="Close dialog" data-close-dialog> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> <div class="octocat-spinner my-6 js-details-dialog-spinner"></div> </details-dialog> </details> </template> <div class="Popover js-hovercard-content position-absolute" style="display: none; outline: none;"> <div class="Popover-message Popover-message--bottom-left Popover-message--large Box color-shadow-large" style="width:360px;"> </div> </div> <template id="snippet-clipboard-copy-button"> <div class="zeroclipboard-container position-absolute right-0 top-0"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn js-clipboard-copy m-2 p-0" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon m-2"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 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 m-2"> <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> <template id="snippet-clipboard-copy-button-unpositioned"> <div class="zeroclipboard-container"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 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>