WRN-pytorch

WideResNet: Wide Residual Network

A pytorch implementation of WRN(Zagoruyko, Sergey, and Nikos Komodakis. "Wide residual networks." arXiv preprint arXiv:1605.07146 (2016).)

Support & Requirements

• 🔥pytorch >= 0.4.0

• 🐍python 3.6.5

• 📈tensorboardX 1.8

• ‼ multi GPU support!

Training

git clone & change DIR

$ git clone https://github.com/J911/WRN-pytorch
$ cd WRN-pytorch

training

$ python train.py

optional arguments:

• --lr LR
• --resume RESUME
• --layer LAYER
• --widen_factor WIDEN_FACTOR
• --batch_size BATCH_SIZE
• --batch_size_test BATCH_SIZE_TEST
• --momentum MOMENTUM
• --weight_decay WEIGHT_DECAY
• --drop_rate DROP_RATE
• --epoch EPOCH
• --num_worker NUM_WORKER
• --logdir LOGDIR

Results

• red: resnet num_block 5 | best test loss: 6.58
• pink: num_widen_factor 2 num_block 5 | best test loss: 6.45
• grey : num_widen_factor 10 num_block 4 | best test loss: 6.19