Demo-Maker / modules / rtmpose / configs / body_2d_keypoint / topdown_regression /
@mikado-4410 mikado-4410 authored on 11 Oct 2024
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coco [update]RTMposeを用いた検出機能の実装 1 year ago
mpii [update]RTMposeを用いた検出機能の実装 1 year ago
README.md [update]RTMposeを用いた検出機能の実装 1 year ago
README.md

Top-down regression-based pose estimation

Top-down methods divide the task into two stages: object detection, followed by single-object pose estimation given object bounding boxes. At the 2nd stage, regression based methods directly regress the keypoint coordinates given the features extracted from the bounding box area, following the paradigm introduced in Deeppose: Human pose estimation via deep neural networks.

Results and Models

COCO Dataset

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

ModelInput SizeAPARDetails and Download
ResNet-152+RLE256x1920.7310.805resnet_rle_coco.md
ResNet-101+RLE256x1920.7220.768resnet_rle_coco.md
ResNet-50+RLE256x1920.7060.768resnet_rle_coco.md
MobileNet-v2+RLE256x1920.5930.644mobilenetv2_rle_coco.md
ResNet-152256x1920.5840.688resnet_coco.md
ResNet-101256x1920.5620.670resnet_coco.md
ResNet-50256x1920.5280.639resnet_coco.md

MPII Dataset

ModelInput SizePCKh@0.5PCKh@0.1Details and Download
ResNet-50+RLE256x2560.8610.277resnet_rle_mpii.md
ResNet-152256x2560.8500.208resnet_mpii.md
ResNet-101256x2560.8410.200resnet_mpii.md
ResNet-50256x2560.8260.180resnet_mpii.md