octave-resnet-26-0.25

Use Case and High-Level Description

The octave-resnet-26-0.25 model is a modification of resnet-26 with Octave convolutions from Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution with alpha=0.25. Like the original model, this model is designed for 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. Before passing the image blob into the network, subtract RGB mean values as follows: [124, 117, 104]. In addition, values must be divided by 0.0167.

The model output for octave-resnet-26-0.25 is a typical object-classifier output for 1000 different classifications matching those in the ImageNet database.

Specification

Metric	Value
Type	Classification
GFLOPs	3.768
MParams	15.99
Source framework	MXNet*

Accuracy

Metric	Value
Top 1	76.076%
Top 5	92.584%

Input

Original Model

Image, name: data, shape: 1, 3, 224, 224, format: 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: 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

Download a Model and Convert it into OpenVINO™ IR Format

You can download models and if necessary convert them into OpenVINO™ IR format using the Model Downloader and other automation tools as shown in the examples below.

An example of using the Model Downloader:

omz_downloader --name <model_name>

An example of using the Model Converter:

omz_converter --name <model_name>

Demo usage

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:

• Classification Benchmark C++ Demo

• Classification Python* Demo

Legal Information

The original model is distributed under the following license:

MIT License

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