octave-resnet-50-0.125

.125_octave-resnet-50-0.125

Use Case and High-Level Description

The octave-resnet-50-0.125 model is a modification of 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. The model is originally 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 1x3x224x224 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-resnet-50-0.125 is the typical object classifier output for the 1000 different classifications matching those in the ImageNet database.

Example

Specification

Metric Value
Type Classification
GFLOPs 7.221
MParams 25.551
Source framework MXNet*

Accuracy

Metric Value
Top 1 78.19%
Top 5 93.862%

Performance

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

Legal Information

The original model is distributed under the following license:

MIT License
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