This is a multi-person 2D pose estimation network based on the EfficientHRNet approach (that follows the Associative Embedding framework). For every person in an image, the network detects a human pose: a body skeleton consisting of keypoints and connections between them. The pose may contain up to 17 keypoints: ears, eyes, nose, shoulders, elbows, wrists, hips, knees, and ankles.
|Average Precision (AP)||51.1%|
Average Precision metric described in COCO Keypoint Evaluation site.
1, 3, 352, 352. An input image in the
B, C, H, W format , where:
The net outputs three blobs:
B, 17, 176, 176containing location heatmaps for keypoints of all types. Locations that are filtered out by non-maximum suppression algorithm have negated values assigned to them.
B, 17, 176, 176, 1containing associative embedding values, which are used for grouping individual keypoints into poses.
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