The modnet-webcam-portrait-matting model is a lightweight matting objective decomposition network (MODNet) for online video portrait matting in real-time with a single input image with MobileNetV2 backbone. The model is pre-trained in PyTorch* framework and converted to ONNX* format.
More details provided in the paper and repository.
| Metric | Value |
|---|---|
| Type | Background Matting |
| GFlops | 31.1564 |
| MParams | 6.4597 |
| Source framework | PyTorch* |
Accuracy measured on the HumanMatting dataset
| Metric | Mean value | Std value |
|---|---|---|
| MAD | 5.66 | 6.21 |
| MSE | 762.52 | 1494.45 |
- MAD - mean of absolute difference
- MSE - mean squared error.
Image, name: input, shape: 1, 3, 512, 512, format: B, C, H, W, where:
B- batch sizeC- number of channelsH- image heightW- image width
Expected color order: RGB.
Mean values - [127.5, 127.5, 127.5], scale value - 127.5.
Image, name: input, shape: 1, 3, 512, 512, format: B, C, H, W, where:
B- batch sizeC- number of channelsH- image heightW- image width
Expected color order: BGR.
Alpha matte with values in [0, 1] range. Name: output Shape: 1, 1, 512, 512, format: B, C, H, W, where:
B- batch sizeC- number of channelsH- image heightW- image width
Alpha matte with values in [0, 1] range. Name: output Shape: 1, 1, 512, 512, format: B, C, H, W, where:
B- batch sizeC- number of channelsH- image heightW- image width
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>
The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:
The original model is distributed under the
Apache License, Version 2.0.
A copy of the license is provided in <omz_dir>/models/public/licenses/APACHE-2.0.txt.