deeplabv3

Use Case and High-Level Description

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

Example

Specification

Metric Value
Type Semantic segmentation
GFLOPs 11.469
MParams 23.819
Source framework TensorFlow*

Accuracy

Metric Value
mean_iou 66.85%

Performance

Input

Original model

Image, name: ImageTensor, shape: [1x513x513x3], format: [BxHxWxC], 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: [1x3x513x513], format: [BxCxHxW], 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, 20], which represent an index of a predicted class for each image pixel. Name: ArgMax, shape: [1x513x513] in [BxHxW] 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: [1x513x513] in [BxHxW] format, where

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

Legal Information

The original model is distributed under the Apache License, Version 2.0. A copy of the license is provided in APACHE-2.0-TensorFlow.txt.