What's this

Implementation of Densely Connected Convolutional Networks (DCCN) by chainer

Dependencies

git clone https://github.com/nutszebra/dense_net.git
cd dense_net
git submodule init
git submodule update

How to run

python main.py -p ./ -e 300 -b 64 -g 0 -s 1 -trb 4 -teb 4 -lr 0.1

Details about my implementation

All hyperparameters and network architecture are the same as in [1] except for data-augmentation.

• Data augmentation
  Train: Pictures are randomly resized in the range of [32, 36], then 32x32 patches are extracted randomly and are normalized locally. Horizontal flipping is applied with 0.5 probability.
  Test: Pictures are resized to 32x32, then they are normalized locally. Single image test is used to calculate total accuracy.

Cifar10 result

As a result, I could confirm 95.12% total accuracy at epoch 250 and this result has almost the same accuracy as reported by [1].

network	depth	k	total accuracy (%)
DCCN [1]	40	12	94.76
my implementation	40	12	95.12
DCCN [1]	100	12	95.9
DCCN [1]	100	24	96.26

# References Densely Connected Convolutional Networks [[1]][Paper]