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: