# single-human-pose-estimation-0001¶

## Use Case and High-Level Description¶

Single human pose estimation model based on paper.

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)

You can download models and if necessary convert them into OpenVINO™ IR format using the Model Downloader and other automation tools as shown in the examples below.

omz_downloader --name <model_name>
omz_converter --name <model_name>