octave-resnet-101-0.125

Use Case and High-Level Description

The octave-resnet-101-0.125 model is a modification of resnet-101 with Octave convolutions from Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution with alpha=0.125. Like the original model, this model is designed for image classification. For details about family of Octave Convolution models, check out the repository.

Example

Specification

Metric Value
Type Classification
GFLOPs 13.387
MParams 44.543
Source framework MXNet*

Accuracy

Metric Value
Top 1 79.182%
Top 5 94.42%

Performance

Input

A blob that consists of a single image of 1x3x224x224 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.

Original Model

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

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:

Channel order is BGR.

Output

The model output for octave-resnet-101-0.125 is a typical object-classifier output for 1000 different classifications matching those in the ImageNet database.

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 following license:

MIT License
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