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se-resnet-50

Use Case and High-Level Description

ResNet-50 with Squeeze-and-Excitation blocks

Example

Specification

Metric Value
Type Classification
GFLOPs 7.775
MParams 28.061
Source framework Caffe*

Accuracy

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 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.