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.
| Metric | Value |
|---|---|
| Average Precision (AP) | 44.4% |
| GFlops | 5.9393 |
| MParams | 8.1504 |
| Source framework | PyTorch* |
Average Precision metric described in COCO Keypoint Evaluation site.
Name: input, shape: [1x3x288x288]. An input image in the [BxCxHxW] format ,
where:
- B - batch size
- C - number of channels
- H - image height
- W - image width Expected color order is BGR.
The net outputs three blobs:
- "heatmaps" of shape [N, 17, 144, 144] containing location heatmaps for keypoints of all types.
- "nms_heatmaps" of shape [N, 17, 144, 144] containing heatmaps after non-maximum suppression.
- "embeddings" of shape [N, 17, 144, 144, 1] containing associative embedding values, which are used for grouping individual keypoints into poses.
[*] Other names and brands may be claimed as the property of others.