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 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:

python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>

An example of using the Model Converter:

python3 <omz_dir>/tools/downloader/converter.py --name <model_name>

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-SENet.txt.