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se-resnet-50

Use Case and High-Level Description

ResNet-50 with Squeeze-and-Excitation blocks

Specification

Metric Value
Type Classification
GFLOPs 7.775
MParams 28.061
Source framework Caffe*

Accuracy

Metric Value
Top 1 77.596%
Top 5 93.85%

Input

Original Model

Image, name: data, shape: 1, 3, 224, 224, format is B, C, H, W, where:

  • B - batch size
  • C - channel
  • H - height
  • W - width

Channel order is BGR. Mean values: [104.0, 117.0, 123.0].

Converted Model

Image, name: data, shape: 1, 3, 224, 224, format is B, C, H, W, where:

  • B - batch size
  • C - channel
  • H - height
  • W - width

Channel order is BGR.

Output

Original Model

Object classifier according to ImageNet classes, name: prob, shape: 1, 1000, output data format is B, C, where:

  • B - batch size
  • C - predicted probabilities for each class in the range [0, 1]

Converted Model

Object classifier according to ImageNet classes, name: prob, shape: 1, 1000, output data format is B, C, where:

  • B - batch size
  • C - predicted probabilities for each class in the range [0, 1]

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:

Legal Information

The original model is distributed under the Apache License, Version 2.0. A copy of the license is provided in <omz_dir>/models/public/licenses/APACHE-2.0-SENet.txt.