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

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Demo usage

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