This is an implementation of DenseNet-BC. The difference between DenseNet-BC and original DenseNet is using 1x1 convolutional layer before each 3x3 convolutional layer in DenseBlock.

3 kinds of DenseNet is defined in densenet.py: DenseNetCifar, DenseNetImagenet, & DenseNet. DenseNetCifar and DenseNetImagenet consists of 4 DenseBlocks, while you can change # of blocks and # of layers for each block by passing n_layers which is an argument of DenseNet.

• Python 3.5.2
• Chainer 2.0.0
• ChainerCV 0.5.1
• OpenCV w/ contrib 3.2.0.7

FYI: You can install OpenCV with or without contrib for python via pip: pip install opencv-python or pip install opencv-contrib-python.

• opencv-python
• opencv-contrib-python

Whole training log is in result_cifar100/log.json

In my environment (GTX 1080), one epoch including validation took around 210 seconds.

As to DenseNetImagenet or DenseNet in densenet.py, examples of # of layers are below.