mmpose/configs/body_2d_keypoint/simcc

Top-down SimCC-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, SimCC based methods reformulate human pose estimation as two classification tasks for horizontal and vertical coordinates, and uniformly divide each pixel into several bins, thus obtain the keypoint coordinates given the features extracted from the bounding box area, following the paradigm introduced in SimCC: a Simple Coordinate Classification Perspective for Human Pose Estimation.

Results and Models

COCO Dataset

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

Model	Input Size	AP	AR	Details and Download
ResNet-50+SimCC	384x288	0.735	0.790	resnet_coco.md
ResNet-50+SimCC	256x192	0.721	0.781	resnet_coco.md
S-ViPNAS-MobileNet-V3+SimCC	256x192	0.695	0.755	vipnas_coco.md
MobileNet-V2+SimCC(wo/deconv)	256x192	0.620	0.678	mobilenetv2_coco.md