https://github.com/mil-tokyo/bc_learning_image
Chainer implementation of between-class learning for Image classification https://arxiv.org/abs/1711.10284
https://github.com/mil-tokyo/bc_learning_image
Last synced: 5 months ago
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Chainer implementation of between-class learning for Image classification https://arxiv.org/abs/1711.10284
- Host: GitHub
- URL: https://github.com/mil-tokyo/bc_learning_image
- Owner: mil-tokyo
- Created: 2017-12-12T02:38:11.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2017-12-13T12:31:51.000Z (over 7 years ago)
- Last Synced: 2024-08-03T23:12:32.862Z (9 months ago)
- Language: Python
- Homepage:
- Size: 6.84 KB
- Stars: 62
- Watchers: 16
- Forks: 19
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
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README
BC learning for images
=========================
Implementation of [Between-class Learning for Image Classification](https://arxiv.org/abs/1711.10284) by Yuji Tokozume, Yoshitaka Ushiku, and Tatsuya Harada.
Our preliminary experimental results on CIFAR-10 and ImageNet-1K were already presented in ILSVRC2017 on July 26, 2017.
#### Between-class (BC) learning:
- We generate between-class examples by mixing two training examples belonging to different classes with a random ratio.
- We then input the mixed data to the model and
train the model to output the mixing ratio.
- Original paper: [Learning from Between-class Examples for Deep Sound Recognition](https://arxiv.org/abs/1711.10282) by us ([github](https://github.com/mil-tokyo/bc_learning_sound))
## Contents
- BC learning for images
- BC: mix two images simply using internal divisions.
- BC+: mix two images treating them as waveforms.
- Training of 11-layer CNN on CIFAR datasets
## Setup
- Install [Chainer](https://chainer.org/) v1.24 on a machine with CUDA GPU.
- Prepare CIFAR datasets.
## Training
- Template:
python main.py --dataset [cifar10 or cifar100] --netType convnet --data path/to/dataset/directory/ (--BC) (--plus)
- Recipes:
- Standard learning on CIFAR-10 (around 6.1% error):
python main.py --dataset cifar10 --netType convnet --data path/to/dataset/directory/
- BC learning on CIFAR-10 (around 5.4% error):
python main.py --dataset cifar10 --netType convnet --data path/to/dataset/directory/ --BC
- BC+ learning on CIFAR-10 (around 5.2% error):
python main.py --dataset cifar10 --netType convnet --data path/to/dataset/directory/ --BC --plus
- Notes:
- It uses the same data augmentation scheme as [fb.resnet.torch](https://github.com/facebook/fb.resnet.torch).
- By default, it runs training 10 times. You can specify the number of trials by using --nTrials command.
- Please check [opts.py](https://github.com/mil-tokyo/bc_learning_image/blob/master/opts.py) for other command line arguments.
## Results
Error rate (average of 10 trials)
| Learning | CIFAR-10 | CIFAR-100 |
|:--|:-:|:-:|
| Standard | 6.07 | 26.68 |
| BC (ours) | 5.40 | 24.28 |
| BC+ (ours) | **5.22** | **23.68** |
- Other results (please see [paper](https://arxiv.org/abs/1711.10284)):
- The performance of [Shake-Shake Regularization](https://github.com/xgastaldi/shake-shake) [[1]](#1) on CIFAR-10 was improved from 2.86% to 2.26%.
- The performance of [ResNeXt](https://github.com/facebookresearch/ResNeXt) [[2]](#2) on ImageNet-1K was improved from 20.4% to 19.4% (single-crop top-1 validation error).
---
#### Reference
[1] X. Gastaldi. Shake-shake regularization. In *ICLR Workshop*, 2017.
[2] S. Xie, R. Girshick, P. Dollar, Z. Tu, and K. He. Aggregated residual transformations for deep neural networks. In *CVPR*, 2017.