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GitHub - HRNet/HRNet-Object-Detection: Object detection with multi-level representations generated from deep high-resolution representation learning (HRNetV2h). This is an official implementation for our TPAMI paper "Deep High-Resolution Representation Learning for Visual Recognition". https://arxiv.org/abs/1908.07919
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class=\"heading-element\" dir=\"auto\"\u003eHigh-resolution networks (HRNets) for object detection\u003c/h1\u003e\u003ca id=\"user-content-high-resolution-networks-hrnets-for-object-detection\" class=\"anchor\" aria-label=\"Permalink: High-resolution networks (HRNets) for object detection\" href=\"#high-resolution-networks-hrnets-for-object-detection\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNews\u003c/h2\u003e\u003ca id=\"user-content-news\" class=\"anchor\" aria-label=\"Permalink: News\" href=\"#news\"\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\n\u003cp dir=\"auto\"\u003e[2020/03/13] Our paper is accepted by TPAMI: \u003ca href=\"https://arxiv.org/pdf/1908.07919.pdf\" rel=\"nofollow\"\u003eDeep High-Resolution Representation Learning for Visual Recognition\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eResults on more detection frameworks available. For example, the score reaches 47 under the Hybrid Task Cascade framework.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eHRNet-Object-Detection is combined into the \u003ca href=\"https://github.com/open-mmlab/mmdetection\"\u003emmdetection\u003c/a\u003e codebase. More results are available at \u003ca href=\"https://github.com/open-mmlab/mmdetection/blob/master/docs/MODEL_ZOO.md\"\u003emodel zoo\u003c/a\u003e and \u003ca href=\"https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet\"\u003eHRNet in mmdetection\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eHRNet with an anchor-free detection algorithm \u003ca href=\"https://github.com/tianzhi0549/FCOS\"\u003eFCOS\u003c/a\u003e is available at \u003ca href=\"https://github.com/HRNet/HRNet-FCOS\"\u003ehttps://github.com/HRNet/HRNet-FCOS\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eMulti-scale training available. We've involved \u003cstrong\u003eSyncBatchNorm\u003c/strong\u003e and \u003cstrong\u003eMulti-scale training(We provided two kinds of implementation)\u003c/strong\u003e in HRNetV2 now! After trained with multiple scales and SyncBN, the detection models\nobtain better performance. Code and models have been updated already!\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eHRNet-Object-Detection on the \u003ca href=\"https://github.com/facebookresearch/maskrcnn-benchmark\"\u003eMaskRCNN-Benchmark\u003c/a\u003e codebase is available at \u003ca href=\"https://github.com/HRNet/HRNet-MaskRCNN-Benchmark\"\u003ehttps://github.com/HRNet/HRNet-MaskRCNN-Benchmark\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis is the official code of \u003ca href=\"https://arxiv.org/pdf/1904.04514.pdf\" rel=\"nofollow\"\u003eHigh-Resolution Representations for Object Detection\u003c/a\u003e. We extend the high-resolution representation (HRNet) [1] by augmenting the high-resolution representation by aggregating the (upsampled) representations from all the parallel\nconvolutions, leading to stronger representations. We build a multi-level representation from the high resolution and apply it to the Faster R-CNN, Mask R-CNN and Cascade R-CNN framework. This proposed approach achieves superior results to existing single-model networks\non COCO object detection. The code is based on \u003ca href=\"https://github.com/open-mmlab/mmdetection\"\u003emmdetection\u003c/a\u003e\u003c/p\u003e\n\u003cdiv align=\"center\" dir=\"auto\"\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/HRNet/HRNet-Object-Detection/blob/master/images/hrnetv2p.png\"\u003e\u003cimg src=\"/HRNet/HRNet-Object-Detection/raw/master/images/hrnetv2p.png\" alt=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePerformance\u003c/h2\u003e\u003ca id=\"user-content-performance\" class=\"anchor\" aria-label=\"Permalink: Performance\" href=\"#performance\"\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\"\u003eImageNet pretrained models\u003c/h3\u003e\u003ca id=\"user-content-imagenet-pretrained-models\" class=\"anchor\" aria-label=\"Permalink: ImageNet pretrained models\" href=\"#imagenet-pretrained-models\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eHRNetV2 ImageNet pretrained models are now available! Codes and \u003cstrong\u003epretrained models\u003c/strong\u003e are in \u003ca href=\"https://github.com/HRNet/HRNet-Image-Classification\"\u003eHRNets for Image Classification\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAll models are trained on COCO \u003cem\u003etrain2017\u003c/em\u003e set and evaluated on COCO \u003cem\u003eval2017\u003c/em\u003e set. Detailed settings or configurations are in \u003ca href=\"/HRNet/HRNet-Object-Detection/blob/master/configs/hrnet\"\u003e\u003ccode\u003econfigs/hrnet\u003c/code\u003e\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Models are trained with the newly released code and the results have minor differences with that in the paper.\nCurrent results will be updated soon and more models and results are comming.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNote: Pretrained HRNets can be downloaded at \u003ca href=\"https://github.com/HRNet/HRNet-Image-Classification\"\u003eHRNets for Image Classification\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Models with \u003ccode\u003e*\u003c/code\u003e are implemented in Official \u003ca href=\"https://github.com/open-mmlab/mmdetection\"\u003emmdetection\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFaster R-CNN\u003c/h3\u003e\u003ca id=\"user-content-faster-r-cnn\" class=\"anchor\" aria-label=\"Permalink: Faster R-CNN\" href=\"#faster-r-cnn\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eBackbone\u003c/th\u003e\n\u003cth align=\"center\"\u003e#Params\u003c/th\u003e\n\u003cth align=\"center\"\u003eGFLOPs\u003c/th\u003e\n\u003cth align=\"center\"\u003elr sched\u003c/th\u003e\n\u003cth align=\"center\"\u003eSyncBN\u003c/th\u003e\n\u003cth align=\"center\"\u003eMS train\u003c/th\u003e\n\u003cth align=\"center\"\u003emAP\u003c/th\u003e\n\u003cth align=\"center\"\u003emodel\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W18\u003c/td\u003e\n\u003ctd align=\"center\"\u003e26.2M\u003c/td\u003e\n\u003ctd align=\"center\"\u003e159.1\u003c/td\u003e\n\u003ctd align=\"center\"\u003e1x\u003c/td\u003e\n\u003ctd align=\"center\"\u003eN\u003c/td\u003e\n\u003ctd align=\"center\"\u003eN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e36.1\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://1drv.ms/u/s!AiWjZ1LamlxzaTqcKb9QJrIZS7Y\" rel=\"nofollow\"\u003eOneDrive\u003c/a\u003e,\u003ca href=\"https://pan.baidu.com/s/1CLycxiy0RVjFGmkOWxmc9Q\" rel=\"nofollow\"\u003eBaiduDrive\u003c/a\u003e(y4hs)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W18\u003c/td\u003e\n\u003ctd align=\"center\"\u003e26.2M\u003c/td\u003e\n\u003ctd align=\"center\"\u003e159.1\u003c/td\u003e\n\u003ctd align=\"center\"\u003e1x\u003c/td\u003e\n\u003ctd align=\"center\"\u003eY\u003c/td\u003e\n\u003ctd align=\"center\"\u003eN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e37.2\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://1drv.ms/u/s!Avk3cZ0cr1JedwR-inENTWU8X2E?e=llP4sR\" rel=\"nofollow\"\u003eOneDrive\u003c/a\u003e,\u003ca href=\"https://pan.baidu.com/s/1i_Ze9yIGvIo-Sa_vBMwipw\" 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align=\"center\"\u003e159.1\u003c/td\u003e\n\u003ctd align=\"center\"\u003e1x\u003c/td\u003e\n\u003ctd align=\"center\"\u003eY\u003c/td\u003e\n\u003ctd align=\"center\"\u003eY(ResizeCrop)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003cstrong\u003e37.6\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://1drv.ms/u/s!Ao8vsd6Ousckab_Y65bdvmP9Qjk?e=LvWihi\" rel=\"nofollow\"\u003eOneDrive\u003c/a\u003e,\u003ca href=\"https://pan.baidu.com/s/1LkSyfQTFGYzTqjtu-p5qJw\" rel=\"nofollow\"\u003eBaiduDrive\u003c/a\u003e(phgo)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W18\u003c/td\u003e\n\u003ctd align=\"center\"\u003e26.2M\u003c/td\u003e\n\u003ctd align=\"center\"\u003e159.1\u003c/td\u003e\n\u003ctd align=\"center\"\u003e2x\u003c/td\u003e\n\u003ctd align=\"center\"\u003eN\u003c/td\u003e\n\u003ctd align=\"center\"\u003eN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e38.1\u003c/td\u003e\n\u003ctd 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rel=\"nofollow\"\u003eBaiduDrive\u003c/a\u003e(ocuf)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W18\u003c/td\u003e\n\u003ctd align=\"center\"\u003e26.2M\u003c/td\u003e\n\u003ctd align=\"center\"\u003e159.1\u003c/td\u003e\n\u003ctd align=\"center\"\u003e2x\u003c/td\u003e\n\u003ctd align=\"center\"\u003eY\u003c/td\u003e\n\u003ctd align=\"center\"\u003eY(ResizeCrop)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003cstrong\u003e39.7\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W32\u003c/td\u003e\n\u003ctd align=\"center\"\u003e45.0M\u003c/td\u003e\n\u003ctd align=\"center\"\u003e245.3\u003c/td\u003e\n\u003ctd align=\"center\"\u003e1x\u003c/td\u003e\n\u003ctd align=\"center\"\u003eN\u003c/td\u003e\n\u003ctd align=\"center\"\u003eN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e39.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://1drv.ms/u/s!AiWjZ1LamlxzaxRamJewuDqSozQ\" rel=\"nofollow\"\u003eOneDrive\u003c/a\u003e,\u003ca href=\"https://pan.baidu.com/s/1UIYopD_PvKrswlhXenBxiw\" rel=\"nofollow\"\u003eBaiduDrive\u003c/a\u003e(ztwa)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W32\u003c/td\u003e\n\u003ctd align=\"center\"\u003e45.0M\u003c/td\u003e\n\u003ctd align=\"center\"\u003e245.3\u003c/td\u003e\n\u003ctd align=\"center\"\u003e1x\u003c/td\u003e\n\u003ctd align=\"center\"\u003eY\u003c/td\u003e\n\u003ctd align=\"center\"\u003eY(Default)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003cstrong\u003e41.0\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W32\u003c/td\u003e\n\u003ctd align=\"center\"\u003e45.0M\u003c/td\u003e\n\u003ctd align=\"center\"\u003e245.3\u003c/td\u003e\n\u003ctd align=\"center\"\u003e2x\u003c/td\u003e\n\u003ctd align=\"center\"\u003eN\u003c/td\u003e\n\u003ctd align=\"center\"\u003eN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e40.8\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://1drv.ms/u/s!AiWjZ1LamlxzbE6rbdU9whYJkqs\" rel=\"nofollow\"\u003eOneDrive\u003c/a\u003e,\u003ca href=\"https://pan.baidu.com/s/1zdne32p_p0c4Tk3IAhLfpA\" rel=\"nofollow\"\u003eBaiduDrive\u003c/a\u003e(hmdo)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W32\u003c/td\u003e\n\u003ctd align=\"center\"\u003e45.0M\u003c/td\u003e\n\u003ctd align=\"center\"\u003e245.3\u003c/td\u003e\n\u003ctd align=\"center\"\u003e2x\u003c/td\u003e\n\u003ctd align=\"center\"\u003eY\u003c/td\u003e\n\u003ctd align=\"center\"\u003eY(Default)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003cstrong\u003e42.6\u003c/strong\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://1drv.ms/u/s!Ao8vsd6OusckanYRdh_HXQRGFjQ?e=hBtvfo\" rel=\"nofollow\"\u003eOneDrive\u003c/a\u003e,\u003ca href=\"https://pan.baidu.com/s/1MFCzseuy8RXloVyThotLSw\" rel=\"nofollow\"\u003eBaiduDrive\u003c/a\u003e(k03x)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W40\u003c/td\u003e\n\u003ctd align=\"center\"\u003e60.5M\u003c/td\u003e\n\u003ctd align=\"center\"\u003e314.9\u003c/td\u003e\n\u003ctd align=\"center\"\u003e1x\u003c/td\u003e\n\u003ctd align=\"center\"\u003eN\u003c/td\u003e\n\u003ctd align=\"center\"\u003eN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e40.4\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://1drv.ms/u/s!AiWjZ1LamlxzbE6rbdU9whYJkqs\" rel=\"nofollow\"\u003eOneDrive\u003c/a\u003e,\u003ca href=\"https://pan.baidu.com/s/1_9D_3Z75doqo4ksy5iIDqA\" rel=\"nofollow\"\u003eBaiduDrive\u003c/a\u003e(0qda)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W40\u003c/td\u003e\n\u003ctd align=\"center\"\u003e60.5M\u003c/td\u003e\n\u003ctd align=\"center\"\u003e314.9\u003c/td\u003e\n\u003ctd align=\"center\"\u003e2x\u003c/td\u003e\n\u003ctd align=\"center\"\u003eN\u003c/td\u003e\n\u003ctd align=\"center\"\u003eN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e41.4\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://1drv.ms/u/s!AiWjZ1Lamlxzb1Uy6QLZnsyfuFc\" rel=\"nofollow\"\u003eOneDrive\u003c/a\u003e,\u003ca href=\"https://pan.baidu.com/s/1sJJdhR2aC9eSsEzqKl1jCA\" rel=\"nofollow\"\u003eBaiduDrive\u003c/a\u003e(xny6)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W48*\u003c/td\u003e\n\u003ctd align=\"center\"\u003e79.4M\u003c/td\u003e\n\u003ctd align=\"center\"\u003e399.1\u003c/td\u003e\n\u003ctd align=\"center\"\u003e1x\u003c/td\u003e\n\u003ctd align=\"center\"\u003eN\u003c/td\u003e\n\u003ctd align=\"center\"\u003eN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e40.9\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w48_1x_20190820-5c6d0903.pth\" rel=\"nofollow\"\u003emodel\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W48*\u003c/td\u003e\n\u003ctd align=\"center\"\u003e79.4M\u003c/td\u003e\n\u003ctd align=\"center\"\u003e399.1\u003c/td\u003e\n\u003ctd align=\"center\"\u003e1x\u003c/td\u003e\n\u003ctd align=\"center\"\u003eN\u003c/td\u003e\n\u003ctd align=\"center\"\u003eN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e41.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w48_2x_20190820-79fb8bfc.pth\" rel=\"nofollow\"\u003emodel\u003c/a\u003e\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\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eMask R-CNN\u003c/h3\u003e\u003ca id=\"user-content-mask-r-cnn\" class=\"anchor\" aria-label=\"Permalink: Mask R-CNN\" href=\"#mask-r-cnn\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eBackbone\u003c/th\u003e\n\u003cth align=\"center\"\u003elr sched\u003c/th\u003e\n\u003cth align=\"center\"\u003eMask mAP\u003c/th\u003e\n\u003cth align=\"center\"\u003eBox mAP\u003c/th\u003e\n\u003cth align=\"center\"\u003emodel\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W18\u003c/td\u003e\n\u003ctd align=\"center\"\u003e1x\u003c/td\u003e\n\u003ctd align=\"center\"\u003e34.2\u003c/td\u003e\n\u003ctd align=\"center\"\u003e37.3\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://1drv.ms/u/s!AiWjZ1Lamlxzcfh06SXd2GR1zKw\" rel=\"nofollow\"\u003eOneDrive\u003c/a\u003e,\u003ca href=\"https://pan.baidu.com/s/1gnAQeFNaJAmMXEZW9iChqQ\" rel=\"nofollow\"\u003eBaiduDrive\u003c/a\u003e(vvc1)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W18\u003c/td\u003e\n\u003ctd align=\"center\"\u003e2x\u003c/td\u003e\n\u003ctd align=\"center\"\u003e35.7\u003c/td\u003e\n\u003ctd align=\"center\"\u003e39.2\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://1drv.ms/u/s!AjfnYvdHLH5TafSZNlgq6UWnJWk\" rel=\"nofollow\"\u003eOneDrive\u003c/a\u003e,\u003ca href=\"https://pan.baidu.com/s/1uj9GmGEaYq2DT6EBX1BQXQ\" rel=\"nofollow\"\u003eBaiduDrive\u003c/a\u003e(x2m7)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W32\u003c/td\u003e\n\u003ctd align=\"center\"\u003e1x\u003c/td\u003e\n\u003ctd align=\"center\"\u003e36.8\u003c/td\u003e\n\u003ctd align=\"center\"\u003e40.7\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://1drv.ms/u/s!AiWjZ1LamlxzcugO3KlXfy_YhiE\" rel=\"nofollow\"\u003eOneDrive\u003c/a\u003e,\u003ca href=\"https://pan.baidu.com/s/1txiS1pEpryu_Y7KOmvpI9A\" rel=\"nofollow\"\u003eBaiduDrive\u003c/a\u003e(j2ir)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W32\u003c/td\u003e\n\u003ctd align=\"center\"\u003e2x\u003c/td\u003e\n\u003ctd align=\"center\"\u003e37.6\u003c/td\u003e\n\u003ctd align=\"center\"\u003e42.1\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://1drv.ms/u/s!AjfnYvdHLH5Taqt21comOmTbdBg\" rel=\"nofollow\"\u003eOneDrive\u003c/a\u003e,\u003ca href=\"https://pan.baidu.com/s/1P8zG7AKKo2JDZMZui65LFg\" rel=\"nofollow\"\u003eBaiduDrive\u003c/a\u003e(tzkz)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W48*\u003c/td\u003e\n\u003ctd align=\"center\"\u003e1x\u003c/td\u003e\n\u003ctd align=\"center\"\u003e42.4\u003c/td\u003e\n\u003ctd align=\"center\"\u003e38.1\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w48_1x_20190820-0923d1ad.pth\" rel=\"nofollow\"\u003emodel\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W48*\u003c/td\u003e\n\u003ctd align=\"center\"\u003e2x\u003c/td\u003e\n\u003ctd align=\"center\"\u003e42.9\u003c/td\u003e\n\u003ctd align=\"center\"\u003e38.3\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w48_2x_20190820-70df51b2.pth\" rel=\"nofollow\"\u003emodel\u003c/a\u003e\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\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCascade R-CNN\u003c/h3\u003e\u003ca id=\"user-content-cascade-r-cnn\" class=\"anchor\" aria-label=\"Permalink: Cascade R-CNN\" href=\"#cascade-r-cnn\"\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\u003eNote:\u003c/strong\u003e we follow the original paper[2] and adopt 280k training iterations which is equal to 20 epochs in mmdetection.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eBackbone\u003c/th\u003e\n\u003cth align=\"center\"\u003elr sched\u003c/th\u003e\n\u003cth align=\"center\"\u003emAP\u003c/th\u003e\n\u003cth align=\"center\"\u003emodel\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W18*\u003c/td\u003e\n\u003ctd align=\"center\"\u003e20e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e41.2\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_rcnn_hrnetv2p_w18_20e_20190810-132012d0.pth\" rel=\"nofollow\"\u003emodel\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W32\u003c/td\u003e\n\u003ctd align=\"center\"\u003e20e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e43.7\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://1drv.ms/u/s!AiWjZ1LamlxzasFUt8GWHW1Og3I\" rel=\"nofollow\"\u003eOneDrive\u003c/a\u003e,\u003ca href=\"https://pan.baidu.com/s/1st1qi2MyeO7qLguj2fKLfQ\" rel=\"nofollow\"\u003eBaiduDrive\u003c/a\u003e(ydd7)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W48*\u003c/td\u003e\n\u003ctd align=\"center\"\u003e20e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e44.6\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_rcnn_hrnetv2p_w48_20e_20190810-f40ed8e1.pth\" rel=\"nofollow\"\u003emodel\u003c/a\u003e\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\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCascade Mask R-CNN\u003c/h3\u003e\u003ca id=\"user-content-cascade-mask-r-cnn\" class=\"anchor\" aria-label=\"Permalink: Cascade Mask R-CNN\" href=\"#cascade-mask-r-cnn\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eBackbone\u003c/th\u003e\n\u003cth align=\"center\"\u003elr sched\u003c/th\u003e\n\u003cth align=\"center\"\u003eMask mAP\u003c/th\u003e\n\u003cth align=\"center\"\u003eBox mAP\u003c/th\u003e\n\u003cth align=\"center\"\u003emodel\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W18*\u003c/td\u003e\n\u003ctd align=\"center\"\u003e20e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e36.4\u003c/td\u003e\n\u003ctd align=\"center\"\u003e41.9\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_20190810-054fb7bf.pth\" rel=\"nofollow\"\u003emodel\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W32*\u003c/td\u003e\n\u003ctd align=\"center\"\u003e20e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e38.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e44.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_20190810-76f61cd0.pth\" rel=\"nofollow\"\u003emodel\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W48*\u003c/td\u003e\n\u003ctd align=\"center\"\u003e20e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e39.5\u003c/td\u003e\n\u003ctd align=\"center\"\u003e46.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_mask_rcnn_hrnetv2p_w48_20e_20190810-d04a1415.pth\" rel=\"nofollow\"\u003emodel\u003c/a\u003e\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\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHybrid Task Cascade\u003c/h3\u003e\u003ca id=\"user-content-hybrid-task-cascade\" class=\"anchor\" aria-label=\"Permalink: Hybrid Task Cascade\" href=\"#hybrid-task-cascade\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eBackbone\u003c/th\u003e\n\u003cth align=\"center\"\u003elr sched\u003c/th\u003e\n\u003cth align=\"center\"\u003eMask mAP\u003c/th\u003e\n\u003cth align=\"center\"\u003eBox mAP\u003c/th\u003e\n\u003cth align=\"center\"\u003emodel\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W18*\u003c/td\u003e\n\u003ctd align=\"center\"\u003e20e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e37.9\u003c/td\u003e\n\u003ctd align=\"center\"\u003e43.1\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/htc_hrnetv2p_w18_20e_20190810-d70072af.pth\" rel=\"nofollow\"\u003emodel\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W32*\u003c/td\u003e\n\u003ctd align=\"center\"\u003e20e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e39.6\u003c/td\u003e\n\u003ctd align=\"center\"\u003e45.3\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/htc_hrnetv2p_w32_20e_20190810-82f9ef5a.pth\" rel=\"nofollow\"\u003emodel\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W48*\u003c/td\u003e\n\u003ctd align=\"center\"\u003e20e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e40.7\u003c/td\u003e\n\u003ctd align=\"center\"\u003e46.8\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/htc_hrnetv2p_w48_20e_20190810-f6d2c3fd.pth\" rel=\"nofollow\"\u003emodel\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2-W48*\u003c/td\u003e\n\u003ctd align=\"center\"\u003e28e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e41.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e47.0\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/htc_hrnetv2p_w48_28e_20190810-a4274b38.pth\" rel=\"nofollow\"\u003emodel\u003c/a\u003e\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\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFCOS\u003c/h3\u003e\u003ca id=\"user-content-fcos\" class=\"anchor\" aria-label=\"Permalink: FCOS\" href=\"#fcos\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eBackbone\u003c/th\u003e\n\u003cth align=\"center\"\u003eGN\u003c/th\u003e\n\u003cth align=\"center\"\u003eMS train\u003c/th\u003e\n\u003cth align=\"center\"\u003eLr schd\u003c/th\u003e\n\u003cth align=\"center\"\u003eBox mAP\u003c/th\u003e\n\u003cth align=\"center\"\u003emodel\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2p-W18*\u003c/td\u003e\n\u003ctd align=\"center\"\u003eY\u003c/td\u003e\n\u003ctd align=\"center\"\u003eN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e1x\u003c/td\u003e\n\u003ctd align=\"center\"\u003e35.2\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/fcos_hrnetv2p_w18_1x_20190810-87a17998.pth\" rel=\"nofollow\"\u003emodel\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2p-W18*\u003c/td\u003e\n\u003ctd align=\"center\"\u003eY\u003c/td\u003e\n\u003ctd align=\"center\"\u003eN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e2x\u003c/td\u003e\n\u003ctd align=\"center\"\u003e38.2\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/fcos_hrnetv2p_w18_2x_20190810-dfd60a7b.pth\" rel=\"nofollow\"\u003emodel\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2p-W32*\u003c/td\u003e\n\u003ctd align=\"center\"\u003eY\u003c/td\u003e\n\u003ctd align=\"center\"\u003eN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e1x\u003c/td\u003e\n\u003ctd align=\"center\"\u003e37.7\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/fcos_hrnetv2p_w32_1x_20190810-62014622.pth\" rel=\"nofollow\"\u003emodel\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2p-W32*\u003c/td\u003e\n\u003ctd align=\"center\"\u003eY\u003c/td\u003e\n\u003ctd align=\"center\"\u003eN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e2x\u003c/td\u003e\n\u003ctd align=\"center\"\u003e40.3\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/fcos_hrnetv2p_w32_2x_20190810-8e987ec1.pth\" rel=\"nofollow\"\u003emodel\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2p-W18*\u003c/td\u003e\n\u003ctd align=\"center\"\u003eY\u003c/td\u003e\n\u003ctd align=\"center\"\u003eY\u003c/td\u003e\n\u003ctd align=\"center\"\u003e2x\u003c/td\u003e\n\u003ctd align=\"center\"\u003e38.1\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/fcos_hrnetv2p_w18_mstrain_2x_20190810-eb846b2c.pth\" rel=\"nofollow\"\u003emodel\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2p-W32*\u003c/td\u003e\n\u003ctd align=\"center\"\u003eY\u003c/td\u003e\n\u003ctd align=\"center\"\u003eY\u003c/td\u003e\n\u003ctd align=\"center\"\u003e2x\u003c/td\u003e\n\u003ctd align=\"center\"\u003e41.4\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/fcos_hrnetv2p_w32_mstrain_2x_20190810-96127bf8.pth\" rel=\"nofollow\"\u003emodel\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHRNetV2p-W48*\u003c/td\u003e\n\u003ctd align=\"center\"\u003eY\u003c/td\u003e\n\u003ctd align=\"center\"\u003eY\u003c/td\u003e\n\u003ctd align=\"center\"\u003e2x\u003c/td\u003e\n\u003ctd align=\"center\"\u003e42.9\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/fcos_hrnetv2p_w48_mstrain_2x_20190810-f7dc8801.pth\" rel=\"nofollow\"\u003emodel\u003c/a\u003e\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\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTechniques about multi-scale training\u003c/h2\u003e\u003ca id=\"user-content-techniques-about-multi-scale-training\" class=\"anchor\" aria-label=\"Permalink: Techniques about multi-scale training\" href=\"#techniques-about-multi-scale-training\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDefault\u003c/h4\u003e\u003ca id=\"user-content-default\" class=\"anchor\" aria-label=\"Permalink: Default\" href=\"#default\"\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\n\u003cp dir=\"auto\"\u003eProcedure\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003eSelect one scale from provided scales randomly and apply it.\u003c/li\u003e\n\u003cli\u003ePad all images in a GPU Batch(e.g. 2 images per GPU) to the same size (see \u003ccode\u003epad_size\u003c/code\u003e, \u003ccode\u003e1600*1000\u003c/code\u003e or \u003ccode\u003e1000*1600\u003c/code\u003e)\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eCode\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eYou need to change lines below in config files\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"data = dict(\n imgs_per_gpu=4,\n workers_per_gpu=8,\n pad_size=(1600, 1024),\n train=dict(\n type=dataset_type,\n ann_file=data_root + 'annotations/instances_train2017.json',\n img_prefix=data_root + 'images/train2017.zip',\n img_scale=[(1600, 1000), (1000, 600), (1333, 800)],\n img_norm_cfg=img_norm_cfg,\n size_divisor=32,\n flip_ratio=0.5,\n with_mask=False,\n with_crowd=True,\n with_label=True),\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edict\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003eimgs_per_gpu\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eworkers_per_gpu\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e8\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003epad_size\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e1600\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e1024\u003c/span\u003e),\n \u003cspan class=\"pl-s1\"\u003etrain\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-en\"\u003edict\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003etype\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003edataset_type\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eann_file\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003edata_root\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e+\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e'annotations/instances_train2017.json'\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eimg_prefix\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003edata_root\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e+\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e'images/train2017.zip'\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eimg_scale\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[(\u003cspan class=\"pl-c1\"\u003e1600\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e1000\u003c/span\u003e), (\u003cspan class=\"pl-c1\"\u003e1000\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e600\u003c/span\u003e), (\u003cspan class=\"pl-c1\"\u003e1333\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e800\u003c/span\u003e)],\n \u003cspan class=\"pl-s1\"\u003eimg_norm_cfg\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eimg_norm_cfg\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003esize_divisor\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e32\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eflip_ratio\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0.5\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ewith_mask\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ewith_crowd\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ewith_label\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\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\"\u003eResizeCrop\u003c/h4\u003e\u003ca id=\"user-content-resizecrop\" class=\"anchor\" aria-label=\"Permalink: ResizeCrop\" href=\"#resizecrop\"\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\"\u003eLess memory and less time, this implementation is more efficient compared to the former one\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eProcedure\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003eSelect one scale from provided scales randomly and apply it.\u003c/li\u003e\n\u003cli\u003eCrop images to a fixed size randomly if they are larger than the given size.\u003c/li\u003e\n\u003cli\u003ePad all images to the same size (see \u003ccode\u003epad_size\u003c/code\u003e).\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eCode\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eYou need to change lines below in config files\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\" imgs_per_gpu=2,\n workers_per_gpu=4,\n pad_size=(1216, 800),\n train=dict(\n type=dataset_type,\n ann_file=data_root + 'annotations/instances_train2017.json',\n img_prefix=data_root + 'train2017.zip',\n img_scale=(1200, 800),\n img_norm_cfg=img_norm_cfg,\n size_divisor=1,\n extra_aug=dict(\n rand_resize_crop=dict(\n scales=[[1400, 600], [1400, 800], [1400, 1000]],\n size=[1200, 800]\n )),\n flip_ratio=0.5,\n with_mask=False,\n with_crowd=True,\n with_label=True),\"\u003e\u003cpre\u003e \u003cspan class=\"pl-s1\"\u003eimgs_per_gpu\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eworkers_per_gpu\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003epad_size\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e1216\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e800\u003c/span\u003e),\n \u003cspan class=\"pl-s1\"\u003etrain\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-en\"\u003edict\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003etype\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003edataset_type\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eann_file\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003edata_root\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e+\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e'annotations/instances_train2017.json'\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eimg_prefix\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003edata_root\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e+\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e'train2017.zip'\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eimg_scale\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e1200\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e800\u003c/span\u003e),\n \u003cspan class=\"pl-s1\"\u003eimg_norm_cfg\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eimg_norm_cfg\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003esize_divisor\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eextra_aug\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-en\"\u003edict\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003erand_resize_crop\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-en\"\u003edict\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003escales\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[[\u003cspan class=\"pl-c1\"\u003e1400\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e600\u003c/span\u003e], [\u003cspan class=\"pl-c1\"\u003e1400\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e800\u003c/span\u003e], [\u003cspan class=\"pl-c1\"\u003e1400\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e1000\u003c/span\u003e]],\n \u003cspan class=\"pl-s1\"\u003esize\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1200\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e800\u003c/span\u003e]\n )),\n \u003cspan class=\"pl-s1\"\u003eflip_ratio\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0.5\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ewith_mask\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ewith_crowd\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ewith_label\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e),\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eQuick start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick start\" href=\"#quick-start\"\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\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eEnvironment\u003c/h4\u003e\u003ca id=\"user-content-environment\" class=\"anchor\" aria-label=\"Permalink: Environment\" href=\"#environment\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis code is developed using on Python 3.6 and PyTorch 1.0.0 on Ubuntu 16.04 with NVIDIA GPUs. Training and testing are\nperformed using 4 NVIDIA P100 GPUs with CUDA 9.0 and cuDNN 7.0. Other platforms or GPUs are not fully tested.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstall\u003c/h4\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-label=\"Permalink: Install\" href=\"#install\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003eInstall PyTorch 1.0 following the \u003ca href=\"https://pytorch.org/\" rel=\"nofollow\"\u003eofficial instructions\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eInstall \u003ccode\u003emmcv\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip install mmcv\"\u003e\u003cpre\u003epip install mmcv\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"3\" dir=\"auto\"\u003e\n\u003cli\u003eInstall \u003ccode\u003epycocotools\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\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/cocodataset/cocoapi.git \\\n \u0026amp;\u0026amp; cd cocoapi/PythonAPI \\\n \u0026amp;\u0026amp; python setup.py build_ext install \\\n \u0026amp;\u0026amp; cd ../../\"\u003e\u003cpre\u003egit clone https://github.com/cocodataset/cocoapi.git \\\n \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e cocoapi/PythonAPI \\\n \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e python setup.py build_ext install \\\n \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ../../\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"4\" dir=\"auto\"\u003e\n\u003cli\u003eInstall \u003ccode\u003eNVIDIA/apex\u003c/code\u003e to enable \u003cstrong\u003eSyncBN\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\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/apex\ncd apex\npython setup install --cuda_ext\"\u003e\u003cpre\u003egit clone https://github.com/NVIDIA/apex\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e apex\npython setup install --cuda_ext\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"5\" dir=\"auto\"\u003e\n\u003cli\u003eInstall \u003ccode\u003eHRNet-Object-Detection\u003c/code\u003e\u003c/li\u003e\n\u003c/ol\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/HRNet/HRNet-Object-Detection.git\n\ncd HRNet-Object-Detection\n# compile CUDA extensions.\nchmod +x compile.sh\n./compile.sh\n\n# run setup\npython setup.py install \n\n# or install locally\npython setup.py install --user\"\u003e\u003cpre\u003egit clone https://github.com/HRNet/HRNet-Object-Detection.git\n\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e HRNet-Object-Detection\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e compile CUDA extensions.\u003c/span\u003e\nchmod +x compile.sh\n./compile.sh\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e run setup\u003c/span\u003e\npython setup.py install \n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or install locally\u003c/span\u003e\npython setup.py install --user\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFor more details, see \u003ca href=\"/HRNet/HRNet-Object-Detection/blob/master/INSTALL.md\"\u003eINSTALL.md\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHRNetV2 pretrained models\u003c/h4\u003e\u003ca id=\"user-content-hrnetv2-pretrained-models\" class=\"anchor\" aria-label=\"Permalink: HRNetV2 pretrained models\" href=\"#hrnetv2-pretrained-models\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"cd HRNet-Object-Detection\n# Download pretrained models into this folder\nmkdir hrnetv2_pretrained\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e HRNet-Object-Detection\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Download pretrained models into this folder\u003c/span\u003e\nmkdir hrnetv2_pretrained\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDatasets\u003c/h4\u003e\u003ca id=\"user-content-datasets\" class=\"anchor\" aria-label=\"Permalink: Datasets\" href=\"#datasets\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePlease download the COCO dataset from \u003ca href=\"http://cocodataset.org/#download\" rel=\"nofollow\"\u003ecocodataset\u003c/a\u003e. If you use \u003ccode\u003ezip\u003c/code\u003e format, please specify \u003ccode\u003eCocoZipDataset\u003c/code\u003e in \u003cstrong\u003econfig\u003c/strong\u003e files or \u003ccode\u003eCocoDataset\u003c/code\u003e if you unzip the downloaded dataset.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTrain (multi-gpu training)\u003c/h4\u003e\u003ca id=\"user-content-train-multi-gpu-training\" class=\"anchor\" aria-label=\"Permalink: Train (multi-gpu training)\" href=\"#train-multi-gpu-training\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePlease specify the configuration file in \u003ccode\u003econfigs\u003c/code\u003e (learning rate should be adjusted when the number of GPUs is changed).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python -m torch.distributed.launch --nproc_per_node \u0026lt;GPUS NUM\u0026gt; tools/train.py \u0026lt;CONFIG-FILE\u0026gt; --launcher pytorch\n# example:\npython -m torch.distributed.launch --nproc_per_node 4 tools/train.py configs/hrnet/faster_rcnn_hrnetv2p_w18_1x.py --launcher pytorch\"\u003e\u003cpre\u003epython -m torch.distributed.launch --nproc_per_node \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eGPUS NUM\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e tools/train.py \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eCONFIG-FILE\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e --launcher pytorch\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e example:\u003c/span\u003e\npython -m torch.distributed.launch --nproc_per_node 4 tools/train.py configs/hrnet/faster_rcnn_hrnetv2p_w18_1x.py --launcher pytorch\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTest\u003c/h4\u003e\u003ca id=\"user-content-test\" class=\"anchor\" aria-label=\"Permalink: Test\" href=\"#test\"\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 tools/test.py \u0026lt;CONFIG-FILE\u0026gt; \u0026lt;MODEL WEIGHT\u0026gt; --gpus \u0026lt;GPUS NUM\u0026gt; --eval bbox --out result.pkl\n# example:\npython tools/test.py configs/hrnet/faster_rcnn_hrnetv2p_w18_1x.py work_dirs/faster_rcnn_hrnetv2p_w18_1x/model_final.pth --gpus 4 --eval bbox --out result.pkl\"\u003e\u003cpre\u003epython tools/test.py \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eCONFIG-FILE\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eMODEL WEIGHT\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e --gpus \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eGPUS NUM\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e --eval bbox --out result.pkl\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e example:\u003c/span\u003e\npython tools/test.py configs/hrnet/faster_rcnn_hrnetv2p_w18_1x.py work_dirs/faster_rcnn_hrnetv2p_w18_1x/model_final.pth --gpus 4 --eval bbox --out result.pkl\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e If you meet some problems, you may find a solution in \u003ca href=\"https://github.com/open-mmlab/mmdetection/issues\"\u003eissues of official mmdetection repo\u003c/a\u003e\nor submit a new issue in our repo.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOther applications of HRNets (codes and models):\u003c/h2\u003e\u003ca id=\"user-content-other-applications-of-hrnets-codes-and-models\" class=\"anchor\" aria-label=\"Permalink: Other applications of HRNets (codes and models):\" href=\"#other-applications-of-hrnets-codes-and-models\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/leoxiaobin/deep-high-resolution-net.pytorch\"\u003eHuman pose estimation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/HRNet/HRNet-Semantic-Segmentation\"\u003eSemantic segmentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/HRNet/HRNet-Facial-Landmark-Detection\"\u003eFacial landmark detection\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/HRNet/HRNet-Image-Classification\"\u003eImage classification\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\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\"\u003eIf you find this work or code is helpful in your research, please cite:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@inproceedings{SunXLW19,\n title={Deep High-Resolution Representation Learning for Human Pose Estimation},\n author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang},\n booktitle={CVPR},\n year={2019}\n}\n\n@article{WangSCJDZLMTWLX19,\n title={Deep High-Resolution Representation Learning for Visual Recognition},\n author={Jingdong Wang and Ke Sun and Tianheng Cheng and \n Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and \n Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao},\n journal = {TPAMI}\n year={2019}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@inproceedings{SunXLW19,\n title={Deep High-Resolution Representation Learning for Human Pose Estimation},\n author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang},\n booktitle={CVPR},\n year={2019}\n}\n\n@article{WangSCJDZLMTWLX19,\n title={Deep High-Resolution Representation Learning for Visual Recognition},\n author={Jingdong Wang and Ke Sun and Tianheng Cheng and \n Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and \n Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao},\n journal = {TPAMI}\n year={2019}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eReference\u003c/h2\u003e\u003ca id=\"user-content-reference\" class=\"anchor\" aria-label=\"Permalink: Reference\" href=\"#reference\"\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[1] Deep High-Resolution Representation Learning for Visual Recognition. Jingdong Wang, Ke Sun, Tianheng Cheng,\nBorui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, Wenyu Liu, Bin Xiao. Accepted by TPAMI. \u003ca href=\"https://arxiv.org/pdf/1908.07919.pdf\" rel=\"nofollow\"\u003edownload\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAcknowledgement\u003c/h2\u003e\u003ca id=\"user-content-acknowledgement\" class=\"anchor\" aria-label=\"Permalink: Acknowledgement\" href=\"#acknowledgement\"\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\"\u003eThanks \u003ca href=\"https://github.com/open-mmlab\"\u003e@open-mmlab\u003c/a\u003e for providing the easily-used code and kind help!\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"High-resolution networks (HRNets) for object detection","anchor":"high-resolution-networks-hrnets-for-object-detection","htmlText":"High-resolution networks (HRNets) for object detection"},{"level":2,"text":"News","anchor":"news","htmlText":"News"},{"level":2,"text":"Introduction","anchor":"introduction","htmlText":"Introduction"},{"level":2,"text":"Performance","anchor":"performance","htmlText":"Performance"},{"level":3,"text":"ImageNet pretrained models","anchor":"imagenet-pretrained-models","htmlText":"ImageNet pretrained models"},{"level":3,"text":"Faster R-CNN","anchor":"faster-r-cnn","htmlText":"Faster R-CNN"},{"level":3,"text":"Mask R-CNN","anchor":"mask-r-cnn","htmlText":"Mask R-CNN"},{"level":3,"text":"Cascade R-CNN","anchor":"cascade-r-cnn","htmlText":"Cascade R-CNN"},{"level":3,"text":"Cascade Mask R-CNN","anchor":"cascade-mask-r-cnn","htmlText":"Cascade Mask R-CNN"},{"level":3,"text":"Hybrid Task Cascade","anchor":"hybrid-task-cascade","htmlText":"Hybrid Task Cascade"},{"level":3,"text":"FCOS","anchor":"fcos","htmlText":"FCOS"},{"level":2,"text":"Techniques about multi-scale training","anchor":"techniques-about-multi-scale-training","htmlText":"Techniques about multi-scale training"},{"level":4,"text":"Default","anchor":"default","htmlText":"Default"},{"level":4,"text":"ResizeCrop","anchor":"resizecrop","htmlText":"ResizeCrop"},{"level":2,"text":"Quick start","anchor":"quick-start","htmlText":"Quick start"},{"level":4,"text":"Environment","anchor":"environment","htmlText":"Environment"},{"level":4,"text":"Install","anchor":"install","htmlText":"Install"},{"level":4,"text":"HRNetV2 pretrained models","anchor":"hrnetv2-pretrained-models","htmlText":"HRNetV2 pretrained models"},{"level":4,"text":"Datasets","anchor":"datasets","htmlText":"Datasets"},{"level":4,"text":"Train (multi-gpu training)","anchor":"train-multi-gpu-training","htmlText":"Train (multi-gpu training)"},{"level":4,"text":"Test","anchor":"test","htmlText":"Test"},{"level":2,"text":"Other applications of HRNets (codes and models):","anchor":"other-applications-of-hrnets-codes-and-models","htmlText":"Other applications of HRNets (codes and models):"},{"level":2,"text":"Citation","anchor":"citation","htmlText":"Citation"},{"level":2,"text":"Reference","anchor":"reference","htmlText":"Reference"},{"level":2,"text":"Acknowledgement","anchor":"acknowledgement","htmlText":"Acknowledgement"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2FHRNet%2FHRNet-Object-Detection"}},{"displayName":"LICENSE","repoName":"HRNet-Object-Detection","refName":"master","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%2FHRNet%2FHRNet-Object-Detection"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-1583894afd38.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-67668e8c2caa.js","githubDevUrl":null,"enabled_features":{"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"copilot_workspace":false,"blob_edit_unsaved_changes_storage":true,"accessible_code_button":true}}}}</script> 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js-snippet-clipboard-copy-unpositioned" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">High-resolution networks (HRNets) for object detection</h1><a id="user-content-high-resolution-networks-hrnets-for-object-detection" class="anchor" aria-label="Permalink: High-resolution networks (HRNets) for object detection" href="#high-resolution-networks-hrnets-for-object-detection"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">News</h2><a id="user-content-news" class="anchor" aria-label="Permalink: News" href="#news"><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> <p dir="auto">[2020/03/13] Our paper is accepted by TPAMI: <a href="https://arxiv.org/pdf/1908.07919.pdf" rel="nofollow">Deep High-Resolution Representation Learning for Visual Recognition</a>.</p> </li> <li> <p dir="auto">Results on more detection frameworks available. For example, the score reaches 47 under the Hybrid Task Cascade framework.</p> </li> <li> <p dir="auto">HRNet-Object-Detection is combined into the <a href="https://github.com/open-mmlab/mmdetection">mmdetection</a> codebase. More results are available at <a href="https://github.com/open-mmlab/mmdetection/blob/master/docs/MODEL_ZOO.md">model zoo</a> and <a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet">HRNet in mmdetection</a>.</p> </li> <li> <p dir="auto">HRNet with an anchor-free detection algorithm <a href="https://github.com/tianzhi0549/FCOS">FCOS</a> is available at <a href="https://github.com/HRNet/HRNet-FCOS">https://github.com/HRNet/HRNet-FCOS</a></p> </li> <li> <p dir="auto">Multi-scale training available. We've involved <strong>SyncBatchNorm</strong> and <strong>Multi-scale training(We provided two kinds of implementation)</strong> in HRNetV2 now! After trained with multiple scales and SyncBN, the detection models obtain better performance. Code and models have been updated already!</p> </li> <li> <p dir="auto">HRNet-Object-Detection on the <a href="https://github.com/facebookresearch/maskrcnn-benchmark">MaskRCNN-Benchmark</a> codebase is available at <a href="https://github.com/HRNet/HRNet-MaskRCNN-Benchmark">https://github.com/HRNet/HRNet-MaskRCNN-Benchmark</a>.</p> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Introduction</h2><a id="user-content-introduction" class="anchor" aria-label="Permalink: Introduction" href="#introduction"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This is the official code of <a href="https://arxiv.org/pdf/1904.04514.pdf" rel="nofollow">High-Resolution Representations for Object Detection</a>. We extend the high-resolution representation (HRNet) [1] by augmenting the high-resolution representation by aggregating the (upsampled) representations from all the parallel convolutions, leading to stronger representations. We build a multi-level representation from the high resolution and apply it to the Faster R-CNN, Mask R-CNN and Cascade R-CNN framework. This proposed approach achieves superior results to existing single-model networks on COCO object detection. The code is based on <a href="https://github.com/open-mmlab/mmdetection">mmdetection</a></p> <div align="center" dir="auto"> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/HRNet/HRNet-Object-Detection/blob/master/images/hrnetv2p.png"><img src="/HRNet/HRNet-Object-Detection/raw/master/images/hrnetv2p.png" alt="" style="max-width: 100%;"></a></p> </div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Performance</h2><a id="user-content-performance" class="anchor" aria-label="Permalink: Performance" href="#performance"><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">ImageNet pretrained models</h3><a id="user-content-imagenet-pretrained-models" class="anchor" aria-label="Permalink: ImageNet pretrained models" href="#imagenet-pretrained-models"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">HRNetV2 ImageNet pretrained models are now available! Codes and <strong>pretrained models</strong> are in <a href="https://github.com/HRNet/HRNet-Image-Classification">HRNets for Image Classification</a></p> <p dir="auto">All models are trained on COCO <em>train2017</em> set and evaluated on COCO <em>val2017</em> set. Detailed settings or configurations are in <a href="/HRNet/HRNet-Object-Detection/blob/master/configs/hrnet"><code>configs/hrnet</code></a>.</p> <p dir="auto"><strong>Note:</strong> Models are trained with the newly released code and the results have minor differences with that in the paper. Current results will be updated soon and more models and results are comming.</p> <p dir="auto"><strong>Note: Pretrained HRNets can be downloaded at <a href="https://github.com/HRNet/HRNet-Image-Classification">HRNets for Image Classification</a></strong></p> <p dir="auto"><strong>Note</strong>: Models with <code>*</code> are implemented in Official <a href="https://github.com/open-mmlab/mmdetection">mmdetection</a>.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Faster R-CNN</h3><a id="user-content-faster-r-cnn" class="anchor" aria-label="Permalink: Faster R-CNN" href="#faster-r-cnn"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th align="center">Backbone</th> <th align="center">#Params</th> <th align="center">GFLOPs</th> <th align="center">lr sched</th> <th align="center">SyncBN</th> <th align="center">MS train</th> <th align="center">mAP</th> <th align="center">model</th> </tr> </thead> <tbody> <tr> <td align="center">HRNetV2-W18</td> <td align="center">26.2M</td> <td align="center">159.1</td> <td align="center">1x</td> <td align="center">N</td> <td align="center">N</td> <td align="center">36.1</td> <td align="center"><a href="https://1drv.ms/u/s!AiWjZ1LamlxzaTqcKb9QJrIZS7Y" rel="nofollow">OneDrive</a>,<a href="https://pan.baidu.com/s/1CLycxiy0RVjFGmkOWxmc9Q" rel="nofollow">BaiduDrive</a>(y4hs)</td> </tr> <tr> <td align="center">HRNetV2-W18</td> <td align="center">26.2M</td> <td align="center">159.1</td> <td align="center">1x</td> <td align="center">Y</td> <td align="center">N</td> <td align="center">37.2</td> <td align="center"><a href="https://1drv.ms/u/s!Avk3cZ0cr1JedwR-inENTWU8X2E?e=llP4sR" rel="nofollow">OneDrive</a>,<a href="https://pan.baidu.com/s/1i_Ze9yIGvIo-Sa_vBMwipw" rel="nofollow">BaiduDrive</a>(ypnu)</td> </tr> <tr> <td align="center">HRNetV2-W18</td> <td align="center">26.2M</td> <td align="center">159.1</td> <td align="center">1x</td> <td align="center">Y</td> <td align="center">Y(Default)</td> <td align="center"><strong>37.6</strong></td> <td align="center"><a href="https://1drv.ms/u/s!Ao8vsd6OusckbJjMoiThi4DojsY?e=9qS2Mh" rel="nofollow">OneDrive</a>,<a href="https://pan.baidu.com/s/1zrDBR2a0yYQZngBWkeU9WQ" rel="nofollow">BaiduDrive</a>(ekkm)</td> </tr> <tr> <td align="center">HRNetV2-W18</td> <td align="center">26.2M</td> <td align="center">159.1</td> <td align="center">1x</td> <td align="center">Y</td> <td align="center">Y(ResizeCrop)</td> <td align="center"><strong>37.6</strong></td> <td align="center"><a href="https://1drv.ms/u/s!Ao8vsd6Ousckab_Y65bdvmP9Qjk?e=LvWihi" rel="nofollow">OneDrive</a>,<a href="https://pan.baidu.com/s/1LkSyfQTFGYzTqjtu-p5qJw" rel="nofollow">BaiduDrive</a>(phgo)</td> </tr> <tr> <td align="center">HRNetV2-W18</td> <td align="center">26.2M</td> <td align="center">159.1</td> <td align="center">2x</td> <td align="center">N</td> <td align="center">N</td> <td align="center">38.1</td> <td align="center"><a href="https://1drv.ms/u/s!AiWjZ1LamlxzcHt7xyWTgVxmMLw" rel="nofollow">OneDrive</a>,<a href="https://pan.baidu.com/s/1xy-YovDSdCaMUVbOaA0ZMA" rel="nofollow">BaiduDrive</a>(mz9y)</td> </tr> <tr> <td align="center">HRNetV2-W18</td> <td align="center">26.2M</td> <td align="center">159.1</td> <td align="center">2x</td> <td align="center">Y</td> <td align="center">Y(Default)</td> <td align="center"><strong>39.4</strong></td> <td align="center"><a href="https://1drv.ms/u/s!Ao8vsd6OusckbYWE2UwMf5fas7A?e=oi0lmh" rel="nofollow">OneDrive</a>,<a href="https://pan.baidu.com/s/1OgsayHL21ur0aWx0R4Oa9w" rel="nofollow">BaiduDrive</a>(ocuf)</td> </tr> <tr> <td align="center">HRNetV2-W18</td> <td align="center">26.2M</td> <td align="center">159.1</td> <td align="center">2x</td> <td align="center">Y</td> <td align="center">Y(ResizeCrop)</td> <td align="center"><strong>39.7</strong></td> <td align="center"></td> </tr> <tr> <td align="center">HRNetV2-W32</td> <td align="center">45.0M</td> <td align="center">245.3</td> <td align="center">1x</td> <td align="center">N</td> <td align="center">N</td> <td align="center">39.5</td> <td align="center"><a href="https://1drv.ms/u/s!AiWjZ1LamlxzaxRamJewuDqSozQ" rel="nofollow">OneDrive</a>,<a href="https://pan.baidu.com/s/1UIYopD_PvKrswlhXenBxiw" rel="nofollow">BaiduDrive</a>(ztwa)</td> </tr> <tr> <td align="center">HRNetV2-W32</td> <td align="center">45.0M</td> <td align="center">245.3</td> <td align="center">1x</td> <td align="center">Y</td> <td align="center">Y(Default)</td> <td align="center"><strong>41.0</strong></td> <td align="center"></td> </tr> <tr> <td align="center">HRNetV2-W32</td> <td align="center">45.0M</td> <td align="center">245.3</td> <td align="center">2x</td> <td align="center">N</td> <td align="center">N</td> <td align="center">40.8</td> <td align="center"><a href="https://1drv.ms/u/s!AiWjZ1LamlxzbE6rbdU9whYJkqs" rel="nofollow">OneDrive</a>,<a href="https://pan.baidu.com/s/1zdne32p_p0c4Tk3IAhLfpA" rel="nofollow">BaiduDrive</a>(hmdo)</td> </tr> <tr> <td align="center">HRNetV2-W32</td> <td align="center">45.0M</td> <td align="center">245.3</td> <td align="center">2x</td> <td align="center">Y</td> <td align="center">Y(Default)</td> <td align="center"><strong>42.6</strong></td> <td align="center"><a href="https://1drv.ms/u/s!Ao8vsd6OusckanYRdh_HXQRGFjQ?e=hBtvfo" rel="nofollow">OneDrive</a>,<a href="https://pan.baidu.com/s/1MFCzseuy8RXloVyThotLSw" rel="nofollow">BaiduDrive</a>(k03x)</td> </tr> <tr> <td align="center">HRNetV2-W40</td> <td align="center">60.5M</td> <td align="center">314.9</td> <td align="center">1x</td> <td align="center">N</td> <td align="center">N</td> <td align="center">40.4</td> <td align="center"><a href="https://1drv.ms/u/s!AiWjZ1LamlxzbE6rbdU9whYJkqs" rel="nofollow">OneDrive</a>,<a href="https://pan.baidu.com/s/1_9D_3Z75doqo4ksy5iIDqA" rel="nofollow">BaiduDrive</a>(0qda)</td> </tr> <tr> <td align="center">HRNetV2-W40</td> <td align="center">60.5M</td> <td align="center">314.9</td> <td align="center">2x</td> <td align="center">N</td> <td align="center">N</td> <td align="center">41.4</td> <td align="center"><a href="https://1drv.ms/u/s!AiWjZ1Lamlxzb1Uy6QLZnsyfuFc" rel="nofollow">OneDrive</a>,<a href="https://pan.baidu.com/s/1sJJdhR2aC9eSsEzqKl1jCA" rel="nofollow">BaiduDrive</a>(xny6)</td> </tr> <tr> <td align="center">HRNetV2-W48*</td> <td align="center">79.4M</td> <td align="center">399.1</td> <td align="center">1x</td> <td align="center">N</td> <td align="center">N</td> <td align="center">40.9</td> <td align="center"><a href="https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w48_1x_20190820-5c6d0903.pth" rel="nofollow">model</a></td> </tr> <tr> <td align="center">HRNetV2-W48*</td> <td align="center">79.4M</td> <td align="center">399.1</td> <td align="center">1x</td> <td align="center">N</td> <td align="center">N</td> <td align="center">41.5</td> <td align="center"><a href="https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w48_2x_20190820-79fb8bfc.pth" rel="nofollow">model</a></td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Mask R-CNN</h3><a id="user-content-mask-r-cnn" class="anchor" aria-label="Permalink: Mask R-CNN" href="#mask-r-cnn"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th align="center">Backbone</th> <th align="center">lr sched</th> <th align="center">Mask mAP</th> <th align="center">Box mAP</th> <th align="center">model</th> </tr> </thead> <tbody> <tr> <td align="center">HRNetV2-W18</td> <td align="center">1x</td> <td align="center">34.2</td> <td align="center">37.3</td> <td align="center"><a href="https://1drv.ms/u/s!AiWjZ1Lamlxzcfh06SXd2GR1zKw" rel="nofollow">OneDrive</a>,<a href="https://pan.baidu.com/s/1gnAQeFNaJAmMXEZW9iChqQ" rel="nofollow">BaiduDrive</a>(vvc1)</td> </tr> <tr> <td align="center">HRNetV2-W18</td> <td align="center">2x</td> <td align="center">35.7</td> <td align="center">39.2</td> <td align="center"><a href="https://1drv.ms/u/s!AjfnYvdHLH5TafSZNlgq6UWnJWk" rel="nofollow">OneDrive</a>,<a href="https://pan.baidu.com/s/1uj9GmGEaYq2DT6EBX1BQXQ" rel="nofollow">BaiduDrive</a>(x2m7)</td> </tr> <tr> <td align="center">HRNetV2-W32</td> <td align="center">1x</td> <td align="center">36.8</td> <td align="center">40.7</td> <td align="center"><a href="https://1drv.ms/u/s!AiWjZ1LamlxzcugO3KlXfy_YhiE" rel="nofollow">OneDrive</a>,<a href="https://pan.baidu.com/s/1txiS1pEpryu_Y7KOmvpI9A" rel="nofollow">BaiduDrive</a>(j2ir)</td> </tr> <tr> <td align="center">HRNetV2-W32</td> <td align="center">2x</td> <td align="center">37.6</td> <td align="center">42.1</td> <td align="center"><a href="https://1drv.ms/u/s!AjfnYvdHLH5Taqt21comOmTbdBg" rel="nofollow">OneDrive</a>,<a href="https://pan.baidu.com/s/1P8zG7AKKo2JDZMZui65LFg" rel="nofollow">BaiduDrive</a>(tzkz)</td> </tr> <tr> <td align="center">HRNetV2-W48*</td> <td align="center">1x</td> <td align="center">42.4</td> <td align="center">38.1</td> <td align="center"><a href="https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w48_1x_20190820-0923d1ad.pth" rel="nofollow">model</a></td> </tr> <tr> <td align="center">HRNetV2-W48*</td> <td align="center">2x</td> <td align="center">42.9</td> <td align="center">38.3</td> <td align="center"><a href="https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w48_2x_20190820-70df51b2.pth" rel="nofollow">model</a></td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Cascade R-CNN</h3><a id="user-content-cascade-r-cnn" class="anchor" aria-label="Permalink: Cascade R-CNN" href="#cascade-r-cnn"><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>Note:</strong> we follow the original paper[2] and adopt 280k training iterations which is equal to 20 epochs in mmdetection.</p> <markdown-accessiblity-table><table> <thead> <tr> <th align="center">Backbone</th> <th align="center">lr sched</th> <th align="center">mAP</th> <th align="center">model</th> </tr> </thead> <tbody> <tr> <td align="center">HRNetV2-W18*</td> <td align="center">20e</td> <td align="center">41.2</td> <td align="center"><a href="https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_rcnn_hrnetv2p_w18_20e_20190810-132012d0.pth" rel="nofollow">model</a></td> </tr> <tr> <td align="center">HRNetV2-W32</td> <td align="center">20e</td> <td align="center">43.7</td> <td align="center"><a href="https://1drv.ms/u/s!AiWjZ1LamlxzasFUt8GWHW1Og3I" rel="nofollow">OneDrive</a>,<a href="https://pan.baidu.com/s/1st1qi2MyeO7qLguj2fKLfQ" rel="nofollow">BaiduDrive</a>(ydd7)</td> </tr> <tr> <td align="center">HRNetV2-W48*</td> <td align="center">20e</td> <td align="center">44.6</td> <td align="center"><a href="https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_rcnn_hrnetv2p_w48_20e_20190810-f40ed8e1.pth" rel="nofollow">model</a></td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Cascade Mask R-CNN</h3><a id="user-content-cascade-mask-r-cnn" class="anchor" aria-label="Permalink: Cascade Mask R-CNN" href="#cascade-mask-r-cnn"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th align="center">Backbone</th> <th align="center">lr sched</th> <th align="center">Mask mAP</th> <th align="center">Box mAP</th> <th align="center">model</th> </tr> </thead> <tbody> <tr> <td align="center">HRNetV2-W18*</td> <td align="center">20e</td> <td align="center">36.4</td> <td align="center">41.9</td> <td align="center"><a href="https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_20190810-054fb7bf.pth" rel="nofollow">model</a></td> </tr> <tr> <td align="center">HRNetV2-W32*</td> <td align="center">20e</td> <td align="center">38.5</td> <td align="center">44.5</td> <td align="center"><a href="https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_20190810-76f61cd0.pth" rel="nofollow">model</a></td> </tr> <tr> <td align="center">HRNetV2-W48*</td> <td align="center">20e</td> <td align="center">39.5</td> <td align="center">46.0</td> <td align="center"><a href="https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_mask_rcnn_hrnetv2p_w48_20e_20190810-d04a1415.pth" rel="nofollow">model</a></td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Hybrid Task Cascade</h3><a id="user-content-hybrid-task-cascade" class="anchor" aria-label="Permalink: Hybrid Task Cascade" href="#hybrid-task-cascade"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th align="center">Backbone</th> <th align="center">lr sched</th> <th align="center">Mask mAP</th> <th align="center">Box mAP</th> <th align="center">model</th> </tr> </thead> <tbody> <tr> <td align="center">HRNetV2-W18*</td> <td align="center">20e</td> <td align="center">37.9</td> <td align="center">43.1</td> <td align="center"><a href="https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/htc_hrnetv2p_w18_20e_20190810-d70072af.pth" rel="nofollow">model</a></td> </tr> <tr> <td align="center">HRNetV2-W32*</td> <td align="center">20e</td> <td align="center">39.6</td> <td align="center">45.3</td> <td align="center"><a href="https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/htc_hrnetv2p_w32_20e_20190810-82f9ef5a.pth" rel="nofollow">model</a></td> </tr> <tr> <td align="center">HRNetV2-W48*</td> <td align="center">20e</td> <td align="center">40.7</td> <td align="center">46.8</td> <td align="center"><a href="https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/htc_hrnetv2p_w48_20e_20190810-f6d2c3fd.pth" rel="nofollow">model</a></td> </tr> <tr> <td align="center">HRNetV2-W48*</td> <td align="center">28e</td> <td align="center">41.0</td> <td align="center">47.0</td> <td align="center"><a href="https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/htc_hrnetv2p_w48_28e_20190810-a4274b38.pth" rel="nofollow">model</a></td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">FCOS</h3><a id="user-content-fcos" class="anchor" aria-label="Permalink: FCOS" href="#fcos"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th align="center">Backbone</th> <th align="center">GN</th> <th align="center">MS train</th> <th align="center">Lr schd</th> <th align="center">Box mAP</th> <th align="center">model</th> </tr> </thead> <tbody> <tr> <td align="center">HRNetV2p-W18*</td> <td align="center">Y</td> <td align="center">N</td> <td align="center">1x</td> <td align="center">35.2</td> <td align="center"><a href="https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/fcos_hrnetv2p_w18_1x_20190810-87a17998.pth" rel="nofollow">model</a></td> </tr> <tr> <td align="center">HRNetV2p-W18*</td> <td align="center">Y</td> <td align="center">N</td> <td align="center">2x</td> <td align="center">38.2</td> <td align="center"><a href="https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/fcos_hrnetv2p_w18_2x_20190810-dfd60a7b.pth" rel="nofollow">model</a></td> </tr> <tr> <td align="center">HRNetV2p-W32*</td> <td align="center">Y</td> <td align="center">N</td> <td align="center">1x</td> <td align="center">37.7</td> <td align="center"><a href="https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/fcos_hrnetv2p_w32_1x_20190810-62014622.pth" rel="nofollow">model</a></td> </tr> <tr> <td align="center">HRNetV2p-W32*</td> <td align="center">Y</td> <td align="center">N</td> <td align="center">2x</td> <td align="center">40.3</td> <td align="center"><a href="https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/fcos_hrnetv2p_w32_2x_20190810-8e987ec1.pth" rel="nofollow">model</a></td> </tr> <tr> <td align="center">HRNetV2p-W18*</td> <td align="center">Y</td> <td align="center">Y</td> <td align="center">2x</td> <td align="center">38.1</td> <td align="center"><a href="https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/fcos_hrnetv2p_w18_mstrain_2x_20190810-eb846b2c.pth" rel="nofollow">model</a></td> </tr> <tr> <td align="center">HRNetV2p-W32*</td> <td align="center">Y</td> <td align="center">Y</td> <td align="center">2x</td> <td align="center">41.4</td> <td align="center"><a href="https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/fcos_hrnetv2p_w32_mstrain_2x_20190810-96127bf8.pth" rel="nofollow">model</a></td> </tr> <tr> <td align="center">HRNetV2p-W48*</td> <td align="center">Y</td> <td align="center">Y</td> <td align="center">2x</td> <td align="center">42.9</td> <td align="center"><a href="https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/fcos_hrnetv2p_w48_mstrain_2x_20190810-f7dc8801.pth" rel="nofollow">model</a></td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Techniques about multi-scale training</h2><a id="user-content-techniques-about-multi-scale-training" class="anchor" aria-label="Permalink: Techniques about multi-scale training" href="#techniques-about-multi-scale-training"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Default</h4><a id="user-content-default" class="anchor" aria-label="Permalink: Default" href="#default"><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> <p dir="auto">Procedure</p> <ol dir="auto"> <li>Select one scale from provided scales randomly and apply it.</li> <li>Pad all images in a GPU Batch(e.g. 2 images per GPU) to the same size (see <code>pad_size</code>, <code>1600*1000</code> or <code>1000*1600</code>)</li> </ol> </li> <li> <p dir="auto">Code</p> </li> </ul> <p dir="auto">You need to change lines below in config files</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="data = dict( imgs_per_gpu=4, workers_per_gpu=8, pad_size=(1600, 1024), train=dict( type=dataset_type, ann_file=data_root + 'annotations/instances_train2017.json', img_prefix=data_root + 'images/train2017.zip', img_scale=[(1600, 1000), (1000, 600), (1333, 800)], img_norm_cfg=img_norm_cfg, size_divisor=32, flip_ratio=0.5, with_mask=False, with_crowd=True, with_label=True),"><pre><span class="pl-s1">data</span> <span class="pl-c1">=</span> <span class="pl-s1">dict</span>( <span class="pl-s1">imgs_per_gpu</span><span class="pl-c1">=</span><span class="pl-c1">4</span>, <span class="pl-s1">workers_per_gpu</span><span class="pl-c1">=</span><span class="pl-c1">8</span>, <span class="pl-s1">pad_size</span><span class="pl-c1">=</span>(<span class="pl-c1">1600</span>, <span class="pl-c1">1024</span>), <span class="pl-s1">train</span><span class="pl-c1">=</span><span class="pl-en">dict</span>( <span class="pl-s1">type</span><span class="pl-c1">=</span><span class="pl-s1">dataset_type</span>, <span class="pl-s1">ann_file</span><span class="pl-c1">=</span><span class="pl-s1">data_root</span> <span class="pl-c1">+</span> <span class="pl-s">'annotations/instances_train2017.json'</span>, <span class="pl-s1">img_prefix</span><span class="pl-c1">=</span><span class="pl-s1">data_root</span> <span class="pl-c1">+</span> <span class="pl-s">'images/train2017.zip'</span>, <span class="pl-s1">img_scale</span><span class="pl-c1">=</span>[(<span class="pl-c1">1600</span>, <span class="pl-c1">1000</span>), (<span class="pl-c1">1000</span>, <span class="pl-c1">600</span>), (<span class="pl-c1">1333</span>, <span class="pl-c1">800</span>)], <span class="pl-s1">img_norm_cfg</span><span class="pl-c1">=</span><span class="pl-s1">img_norm_cfg</span>, <span class="pl-s1">size_divisor</span><span class="pl-c1">=</span><span class="pl-c1">32</span>, <span class="pl-s1">flip_ratio</span><span class="pl-c1">=</span><span class="pl-c1">0.5</span>, <span class="pl-s1">with_mask</span><span class="pl-c1">=</span><span class="pl-c1">False</span>, <span class="pl-s1">with_crowd</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">with_label</span><span class="pl-c1">=</span><span class="pl-c1">True</span>),</pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">ResizeCrop</h4><a id="user-content-resizecrop" class="anchor" aria-label="Permalink: ResizeCrop" href="#resizecrop"><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">Less memory and less time, this implementation is more efficient compared to the former one</p> <ul dir="auto"> <li> <p dir="auto">Procedure</p> <ol dir="auto"> <li>Select one scale from provided scales randomly and apply it.</li> <li>Crop images to a fixed size randomly if they are larger than the given size.</li> <li>Pad all images to the same size (see <code>pad_size</code>).</li> </ol> </li> <li> <p dir="auto">Code</p> </li> </ul> <p dir="auto">You need to change lines below in config files</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content=" imgs_per_gpu=2, workers_per_gpu=4, pad_size=(1216, 800), train=dict( type=dataset_type, ann_file=data_root + 'annotations/instances_train2017.json', img_prefix=data_root + 'train2017.zip', img_scale=(1200, 800), img_norm_cfg=img_norm_cfg, size_divisor=1, extra_aug=dict( rand_resize_crop=dict( scales=[[1400, 600], [1400, 800], [1400, 1000]], size=[1200, 800] )), flip_ratio=0.5, with_mask=False, with_crowd=True, with_label=True),"><pre> <span class="pl-s1">imgs_per_gpu</span><span class="pl-c1">=</span><span class="pl-c1">2</span>, <span class="pl-s1">workers_per_gpu</span><span class="pl-c1">=</span><span class="pl-c1">4</span>, <span class="pl-s1">pad_size</span><span class="pl-c1">=</span>(<span class="pl-c1">1216</span>, <span class="pl-c1">800</span>), <span class="pl-s1">train</span><span class="pl-c1">=</span><span class="pl-en">dict</span>( <span class="pl-s1">type</span><span class="pl-c1">=</span><span class="pl-s1">dataset_type</span>, <span class="pl-s1">ann_file</span><span class="pl-c1">=</span><span class="pl-s1">data_root</span> <span class="pl-c1">+</span> <span class="pl-s">'annotations/instances_train2017.json'</span>, <span class="pl-s1">img_prefix</span><span class="pl-c1">=</span><span class="pl-s1">data_root</span> <span class="pl-c1">+</span> <span class="pl-s">'train2017.zip'</span>, <span class="pl-s1">img_scale</span><span class="pl-c1">=</span>(<span class="pl-c1">1200</span>, <span class="pl-c1">800</span>), <span class="pl-s1">img_norm_cfg</span><span class="pl-c1">=</span><span class="pl-s1">img_norm_cfg</span>, <span class="pl-s1">size_divisor</span><span class="pl-c1">=</span><span class="pl-c1">1</span>, <span class="pl-s1">extra_aug</span><span class="pl-c1">=</span><span class="pl-en">dict</span>( <span class="pl-s1">rand_resize_crop</span><span class="pl-c1">=</span><span class="pl-en">dict</span>( <span class="pl-s1">scales</span><span class="pl-c1">=</span>[[<span class="pl-c1">1400</span>, <span class="pl-c1">600</span>], [<span class="pl-c1">1400</span>, <span class="pl-c1">800</span>], [<span class="pl-c1">1400</span>, <span class="pl-c1">1000</span>]], <span class="pl-s1">size</span><span class="pl-c1">=</span>[<span class="pl-c1">1200</span>, <span class="pl-c1">800</span>] )), <span class="pl-s1">flip_ratio</span><span class="pl-c1">=</span><span class="pl-c1">0.5</span>, <span class="pl-s1">with_mask</span><span class="pl-c1">=</span><span class="pl-c1">False</span>, <span class="pl-s1">with_crowd</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">with_label</span><span class="pl-c1">=</span><span class="pl-c1">True</span>),</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Quick start</h2><a id="user-content-quick-start" class="anchor" aria-label="Permalink: Quick start" href="#quick-start"><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"><h4 tabindex="-1" class="heading-element" dir="auto">Environment</h4><a id="user-content-environment" class="anchor" aria-label="Permalink: Environment" href="#environment"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This code is developed using on Python 3.6 and PyTorch 1.0.0 on Ubuntu 16.04 with NVIDIA GPUs. Training and testing are performed using 4 NVIDIA P100 GPUs with CUDA 9.0 and cuDNN 7.0. Other platforms or GPUs are not fully tested.</p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Install</h4><a id="user-content-install" class="anchor" aria-label="Permalink: Install" href="#install"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li>Install PyTorch 1.0 following the <a href="https://pytorch.org/" rel="nofollow">official instructions</a></li> <li>Install <code>mmcv</code></li> </ol> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip install mmcv"><pre>pip install mmcv</pre></div> <ol start="3" dir="auto"> <li>Install <code>pycocotools</code></li> </ol> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="git clone https://github.com/cocodataset/cocoapi.git \ && cd cocoapi/PythonAPI \ && python setup.py build_ext install \ && cd ../../"><pre>git clone https://github.com/cocodataset/cocoapi.git \ <span class="pl-k">&&</span> <span class="pl-c1">cd</span> cocoapi/PythonAPI \ <span class="pl-k">&&</span> python setup.py build_ext install \ <span class="pl-k">&&</span> <span class="pl-c1">cd</span> ../../</pre></div> <ol start="4" dir="auto"> <li>Install <code>NVIDIA/apex</code> to enable <strong>SyncBN</strong></li> </ol> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="git clone https://github.com/NVIDIA/apex cd apex python setup install --cuda_ext"><pre>git clone https://github.com/NVIDIA/apex <span class="pl-c1">cd</span> apex python setup install --cuda_ext</pre></div> <ol start="5" dir="auto"> <li>Install <code>HRNet-Object-Detection</code></li> </ol> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="git clone https://github.com/HRNet/HRNet-Object-Detection.git cd HRNet-Object-Detection # compile CUDA extensions. chmod +x compile.sh ./compile.sh # run setup python setup.py install # or install locally python setup.py install --user"><pre>git clone https://github.com/HRNet/HRNet-Object-Detection.git <span class="pl-c1">cd</span> HRNet-Object-Detection <span class="pl-c"><span class="pl-c">#</span> compile CUDA extensions.</span> chmod +x compile.sh ./compile.sh <span class="pl-c"><span class="pl-c">#</span> run setup</span> python setup.py install <span class="pl-c"><span class="pl-c">#</span> or install locally</span> python setup.py install --user</pre></div> <p dir="auto">For more details, see <a href="/HRNet/HRNet-Object-Detection/blob/master/INSTALL.md">INSTALL.md</a></p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">HRNetV2 pretrained models</h4><a id="user-content-hrnetv2-pretrained-models" class="anchor" aria-label="Permalink: HRNetV2 pretrained models" href="#hrnetv2-pretrained-models"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="cd HRNet-Object-Detection # Download pretrained models into this folder mkdir hrnetv2_pretrained"><pre><span class="pl-c1">cd</span> HRNet-Object-Detection <span class="pl-c"><span class="pl-c">#</span> Download pretrained models into this folder</span> mkdir hrnetv2_pretrained</pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Datasets</h4><a id="user-content-datasets" class="anchor" aria-label="Permalink: Datasets" href="#datasets"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Please download the COCO dataset from <a href="http://cocodataset.org/#download" rel="nofollow">cocodataset</a>. If you use <code>zip</code> format, please specify <code>CocoZipDataset</code> in <strong>config</strong> files or <code>CocoDataset</code> if you unzip the downloaded dataset.</p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Train (multi-gpu training)</h4><a id="user-content-train-multi-gpu-training" class="anchor" aria-label="Permalink: Train (multi-gpu training)" href="#train-multi-gpu-training"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Please specify the configuration file in <code>configs</code> (learning rate should be adjusted when the number of GPUs is changed).</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python -m torch.distributed.launch --nproc_per_node <GPUS NUM> tools/train.py <CONFIG-FILE> --launcher pytorch # example: python -m torch.distributed.launch --nproc_per_node 4 tools/train.py configs/hrnet/faster_rcnn_hrnetv2p_w18_1x.py --launcher pytorch"><pre>python -m torch.distributed.launch --nproc_per_node <span class="pl-k"><</span>GPUS NUM<span class="pl-k">></span> tools/train.py <span class="pl-k"><</span>CONFIG-FILE<span class="pl-k">></span> --launcher pytorch <span class="pl-c"><span class="pl-c">#</span> example:</span> python -m torch.distributed.launch --nproc_per_node 4 tools/train.py configs/hrnet/faster_rcnn_hrnetv2p_w18_1x.py --launcher pytorch</pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Test</h4><a id="user-content-test" class="anchor" aria-label="Permalink: Test" href="#test"><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 tools/test.py <CONFIG-FILE> <MODEL WEIGHT> --gpus <GPUS NUM> --eval bbox --out result.pkl # example: python tools/test.py configs/hrnet/faster_rcnn_hrnetv2p_w18_1x.py work_dirs/faster_rcnn_hrnetv2p_w18_1x/model_final.pth --gpus 4 --eval bbox --out result.pkl"><pre>python tools/test.py <span class="pl-k"><</span>CONFIG-FILE<span class="pl-k">></span> <span class="pl-k"><</span>MODEL WEIGHT<span class="pl-k">></span> --gpus <span class="pl-k"><</span>GPUS NUM<span class="pl-k">></span> --eval bbox --out result.pkl <span class="pl-c"><span class="pl-c">#</span> example:</span> python tools/test.py configs/hrnet/faster_rcnn_hrnetv2p_w18_1x.py work_dirs/faster_rcnn_hrnetv2p_w18_1x/model_final.pth --gpus 4 --eval bbox --out result.pkl</pre></div> <p dir="auto"><strong>NOTE:</strong> If you meet some problems, you may find a solution in <a href="https://github.com/open-mmlab/mmdetection/issues">issues of official mmdetection repo</a> or submit a new issue in our repo.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Other applications of HRNets (codes and models):</h2><a id="user-content-other-applications-of-hrnets-codes-and-models" class="anchor" aria-label="Permalink: Other applications of HRNets (codes and models):" href="#other-applications-of-hrnets-codes-and-models"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://github.com/leoxiaobin/deep-high-resolution-net.pytorch">Human pose estimation</a></li> <li><a href="https://github.com/HRNet/HRNet-Semantic-Segmentation">Semantic segmentation</a></li> <li><a href="https://github.com/HRNet/HRNet-Facial-Landmark-Detection">Facial landmark detection</a></li> <li><a href="https://github.com/HRNet/HRNet-Image-Classification">Image classification</a></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Citation</h2><a id="user-content-citation" class="anchor" aria-label="Permalink: Citation" href="#citation"><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">If you find this work or code is helpful in your research, please cite:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@inproceedings{SunXLW19, title={Deep High-Resolution Representation Learning for Human Pose Estimation}, author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang}, booktitle={CVPR}, year={2019} } @article{WangSCJDZLMTWLX19, title={Deep High-Resolution Representation Learning for Visual Recognition}, author={Jingdong Wang and Ke Sun and Tianheng Cheng and Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao}, journal = {TPAMI} year={2019} }"><pre class="notranslate"><code>@inproceedings{SunXLW19, title={Deep High-Resolution Representation Learning for Human Pose Estimation}, author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang}, booktitle={CVPR}, year={2019} } @article{WangSCJDZLMTWLX19, title={Deep High-Resolution Representation Learning for Visual Recognition}, author={Jingdong Wang and Ke Sun and Tianheng Cheng and Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao}, journal = {TPAMI} year={2019} } </code></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Reference</h2><a id="user-content-reference" class="anchor" aria-label="Permalink: Reference" href="#reference"><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">[1] Deep High-Resolution Representation Learning for Visual Recognition. Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, Wenyu Liu, Bin Xiao. Accepted by TPAMI. <a href="https://arxiv.org/pdf/1908.07919.pdf" rel="nofollow">download</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Acknowledgement</h2><a id="user-content-acknowledgement" class="anchor" aria-label="Permalink: Acknowledgement" href="#acknowledgement"><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">Thanks <a href="https://github.com/open-mmlab">@open-mmlab</a> for providing the easily-used code and kind help!</p> </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="kmucVFdTGyAEQuXTG7CMOaDapb/xcAjQATOrw64+hnX0E57iM7ILyACXz5OdLAqfVF64kEcF2kLxKXiUk9E6Gg==" /> </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"> Object detection with multi-level representations generated from deep high-resolution representation learning (HRNetV2h). This is an official implementation for our TPAMI paper "Deep High-Resolution Representation Learning for Visual Recognition". <a href="https://arxiv.org/abs/1908.07919" rel="nofollow">https://arxiv.org/abs/1908.07919</a> </p> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:faster-rcnn" href="/topics/faster-rcnn" title="Topic: faster-rcnn" data-view-component="true" class="topic-tag topic-tag-link"> faster-rcnn </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:object-detection" href="/topics/object-detection" title="Topic: object-detection" data-view-component="true" class="topic-tag topic-tag-link"> object-detection </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:mscoco" href="/topics/mscoco" title="Topic: mscoco" data-view-component="true" class="topic-tag 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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>