octave-se-resnet-50-0.125

## Use Case and High-Level Description

The octave-se-resnet-50-0.125 model is a modification of se-resnet-50 from this paper with octave convolutions from Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution with alpha=0.125. As origin, it's designed to perform image classification. For details about family of octave convolution models, check out the repository.

The model input is a blob that consists of a single image of 1, 3, 224, 224 in RGB order. The RGB mean values need to be subtracted as follows: [124, 117, 104] before passing the image blob into the network. In addition, values must be divided by 0.0167.

The model output for octave-se-resnet-50-0.125 is the typical object classifier output for the 1000 different classifications matching those in the ImageNet database.

## Specification

Metric Value
Type Classification
GFLOPs 7.246
MParams 28.082
Source framework MXNet*

Metric Value
Top 1 78.706%
Top 5 94.09%

## 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 RGB. Mean values - [124, 117, 104], scale value - 59.880239521

### 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

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 Converter:

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