# human-pose-estimation-0006¶

## Use Case and High-Level Description¶

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.

## Specification¶

Metric

Value

Average Precision (AP)

51.1%

GFlops

8.844

MParams

8.1506

Source framework

PyTorch*

Average Precision metric described in COCO Keypoint Evaluation site.

## Inputs¶

Image, name: input, shape: 1, 3, 352, 352 in the B, C, H, W format, where:

• B - batch size

• C - number of channels

• H - image height

• W - image width

Expected color order is BGR.

## Outputs¶

The net outputs are two blobs:

1. heatmaps of shape 1, 17, 176, 176 containing location heatmaps for keypoints of all types. Locations that are filtered out by non-maximum suppression algorithm have negated values assigned to them.

2. embeddings of shape 1, 17, 176, 176, 1 containing associative embedding values, which are used for grouping individual keypoints into poses.