Use Case and High-Level Description

BN-Inception with Squeeze-and-Excitation blocks

Example

Specification

Metric	Value
Type	Classification
GFLOPs	4.091
MParams	11.922
Source framework	Caffe*

Accuracy

Metric	Value
Top 1	75.996%
Top 5	92.964%

Performance

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 [0, 1] range

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 [0, 1] range

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.