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deeplabv3

Use Case and High-Level Description

DeepLab is a state-of-art deep learning model for semantic image segmentation. For details see paper.

Specification

Metric

Value

Type

Semantic segmentation

GFLOPs

11.469

MParams

23.819

Source framework

TensorFlow*

Accuracy

Metric

Value

mean_iou

68.41%

Input

Original model

Image, name: ImageTensor, shape: 1, 513, 513, 3, format: B, H, W, C, where:

  • B - batch size

  • H - image height

  • W - image width

  • C - number of channels

Expected color order: RGB.

Converted Model

Image, name: mul_1/placeholder_port_1, shape: 1, 513, 513, 3, format: B, H, W, C, where:

  • B - batch size

  • H - image height

  • W - image width

  • C - number of channels

Expected color order: BGR.

Output

Original Model

Integer values in a range [0, 20], which represent an index of a predicted class for each image pixel. Name: ArgMax, shape: 1, 513, 513 in B, H, W format, where:

  • B - batch size

  • H - image height

  • W - image width

Converted Model

Integer values in a range [0, 20], which represent an index of a predicted class for each image pixel. Name: ArgMax/Squeeze, shape: 1, 513, 513 in B, H, W format, where:

  • B - batch size

  • H - image height

  • W - image 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:

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