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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)

Download a Model and Convert it into OpenVINO™ IR Format

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.

An example of using the Model Downloader:

omz_downloader --name <model_name>

An example of using the Model Converter:

omz_converter --name <model_name>

Demo usage

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:

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