Train various models on CIFAR10 with Chainer

Requirements

• Python 3.5.1+ (not tested with Python2)
• pip packages:
  • chainer>=3.1.0
  • chainercv>=0.8.0
  • numpy>=1.10.1
  • matplotlib>=2.0.0
  • scikit-image>=0.13.1
  • opencv-python>=3.3.0
  • tabulate>=0.8.2

Quick Start

MPLBACKEND=Agg python train.py

With full arguments:

MPLBACKEND=Agg python train.py \
--model_file models/wide_resnet.py \
--model_name WideResNet \
--batchsize 128 \
--training_epoch 500 \
--initial_lr 0.05 \
--lr_decay_rate 0.5 \
--lr_decay_epoch 70 \
--weight_decay 0.0005 \
--random_angle 15.0 \
--pca_sigma 25.5 \
--expand_ratio 1.2 \
--crop_size 28 28 \
--seed 0 \
--gpus 0 

About data augmentation

It performs various data augmentation using ChainerCV. Provided operations are:

• Random rotating (using OpenCV or scikit-image)
• Random lighting
• Random LR-flipping
• Random zomming (a.k.a. expansion)
• Random cropping

See the details at transform function in train.py.

Exprimental Results