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]

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.