se-resnet-152

Use Case and High-Level Description

ResNet-152 with Squeeze-and-Excitation blocks

Example

Specification

Metric Value
Type Classification
GFLOPs 22.709
MParams 66.746
Source framework Caffe*

Accuracy

Metric Value
Top 1 78.506%
Top 5 94.45%

Performance

Input

Original model

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

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:

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:

Converted model

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

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.