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ocrnet-hrnet-w48-paddle

Use Case and High-Level Description

ocrnet-hrnet-w48-paddle is a semantic segmentation model, pre-trained on on Cityscapes dataset for 19 object classes, listed in <omz_dir>/data/dataset_classes/cityscapes_19cl_bkgr.txt file. See Cityscapes classes definition for more details. The model was built on HRNet backbone and address the semantic segmentation problem characterizing a pixel by exploiting the representation of the corresponding object class using Object-Contextual Representations. This model is used for pixel-level prediction tasks. For details see repository, paper.

Specification

Metric Value
Type Semantic segmentation
GFlops 324.66
MParams 70.47
Source framework Paddle*

Accuracy

Metric Value
mean_iou 82.15%

Accuracy metrics were obtained with fixed input resolution 2048x1024 on CityScapes dataset.

Input

Original model

Image, name: x, shape: 1, 3, 1024, 2048, 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: [127.5, 127.5, 127.5], scale values: [127.5, 127.5, 127.5]

Converted Model

Image, name: x, shape: 1, 3, 1024, 2048, format: B, C, H, W, where:

  • B - batch size
  • C - number of channels
  • H - image height
  • W - image width

Expected color order: BGR.

Output

Original Model

Integer values in a range [0, 18], which represent an index of a predicted class for each image pixel. Name: argmax_0.tmp_0, shape: 1, 1024, 2048 in B, H, W format, where:

  • B - batch size
  • H - image height
  • W - image width

Converted Model

Integer values in a range [0, 18], which represent an index of a predicted class for each image pixel. Name: argmax_0.tmp_0, shape: 1, 1024, 2048 in B, H, W format, where:

  • B - batch size
  • H - image height
  • W - image width

Download a Model and Convert it into Inference Engine Format

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:

omz_downloader --name <model_name>

An example of using the Model Converter:

omz_converter --name <model_name>

Legal Information

The original model is distributed under the Apache License, Version 2.0.