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octave-resnet-200-0.125

Use Case and High-Level Description

The octave-resnet-200-0.125 model is a modification of resnet-200 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 25.407
MParams 64.667
Source framework MXNet*

Accuracy

Metric Value
Top 1 79.99%
Top 5 94.866%

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:

  • 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

The model output for octave-resnet-200-0.125 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:

  • 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

Copyright (c) Facebook, Inc. and its affiliates.

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.