hrnet-v2-c1-segmentation#

Use Case and High-Level Description#

This model is a pair of encoder and decoder. The encoder is HRNetV2-W48 and the decoder is C1 (one convolution module and interpolation). HRNetV2-W48 is semantic-segmentation model based on architecture described in paper High-Resolution Representations for Labeling Pixels and Regions. This is PyTorch* implementation based on retaining high resolution representations throughout the model and pre-trained on ADE20k dataset. For details about implementation of model, check out the Semantic Segmentation on MIT ADE20K dataset in PyTorch repository.

Specification#

Metric

Value

Type

Segmentation

GFLOPs

81.9930

MParams

66.4768

Source framework

PyTorch*

Accuracy#

Metric

Original model

Converted model

Pixel accuracy

77.69%

77.69%

mean IoU

33.02%

33.02%

Input#

Original Model#

Image, name - image, shape - 1, 3, 320, 320, format is B, C, H, W, where:

  • B - batch size

  • H - height

  • W - width

  • C - channel

Channel order is RGB. Mean values - [123.675, 116.28, 103.53], scale values - [58.395, 57.12, 57.375].

Converted Model#

Image, name - input.1, shape - 1, 3, 320, 320, format is B, C, H, W, where:

  • B - batch size

  • C - channel

  • H - height

  • W - width

Channel order is BGR.

Output#

Original Model#

Semantic-segmentation mask according to ADE20k classes, name - softmax, shape - 1, 150, 320, 320, output data format is B, C, H, W, where:

  • B - batch size

  • C - predicted probabilities for each class in [0, 1] range

  • H - height

  • W - width

Converted Model#

Semantic-segmentation mask according to ADE20k classes, name - softmax, shape - 1, 150, 320, 320, output data format is B, C, H, W, where:

  • B - batch size

  • C - predicted probabilities for each class in [0, 1] range

  • H - height

  • W - width

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: