single-human-pose-estimation-0001

## Use Case and High-Level Description

Single human pose estimation model based on paper.

## Specification

Metric Value
AP(coco orig) 69.04%
GFlops 60.125
MParams 33.165
Source framework PyTorch*

## Inputs

### Original model

Image, name: data, shape: 1, 3, 384, 288 in the format B, C, H, W, where:

• B - batch size
• C - number of channels
• H - image height
• W - image width

Expected color order - RGB. Mean values - [123.675, 116.28, 103.53]. Scale values - [58.395, 57.12, 57.375]

### Converted model

Image, name: data, shape: 1, 3, 384, 288 in the format B, C, H, W, where:

• B - batch size
• C - number of channels
• H - image height
• W - image width

Expected color order: BGR.

## Outputs

### Original model

The net outputs list of tensor. Count of list elements is 6. Every tensor with shapes: 1, 17, 48, 36 (For every keypoint own heatmap). The six outputs are necessary in order to calculate the loss in during training. But in the future, for obtaining the results of prediction and postprocessing them, the last output is used. Each following tensor gives more accurate predictions (in context metric AP).

### Converted model

The net output is a tensor with name heatmaps and shape 1, 17, 48, 36. (For every keypoint own heatmap)

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