Single human pose estimation model based on https://arxiv.org/abs/1906.04104.
| Metric | Value |
|---|---|
| AP(coco orig) | 69.04% |
| GFlops | 60.125 |
| MParams | 33.165 |
| Source framework | PyTorch* |
Name: "data" , shape: [1x3x384x288] - An input image in the format [BxCxHxW], 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]
Name: "data" , shape: [1x3x384x288] - An input image in the format [BxCxHxW], where:
- B - batch size
- C - number of channels
- H - image height
- W - image width
Expected color order: BGR.
The net outputs list of tensor. Count of list elements is 6. Every tensor with shapes: [1x17x48x36] ( 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).
The net outputs tensor with shapes: [1x17x48x36]. ( For every keypoint own heatmap)
You can download models and if necessary convert them into Inference Engine format using the Model Downloader and other automation tools as shown in the examples below.
An example of using the Model Downloader:
python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>
An example of using the Model Converter:
python3 <omz_dir>/tools/downloader/converter.py --name <model_name>
The original model is distributed under the Apache License, Version 2.0. A copy of the license is provided in APACHE-2.0.txt.
[*] Other names and brands may be claimed as the property of others.