A reproduction of Learning Efficient Convolutional Networks through Network Slimming
- -net: net type, default='vgg19'
- -dataset: dataset, default='cifar100'
- -b: batch size for training, default=64
- -tb: batch size for testing, default=256
- -lr: initial learning rate, default=0.1
- -e: epoch, default=160
- -optim: optimizer, default="SGD"
- -momentum: SGD momentum, default=0.9
- -gpu: select GPU, default="0,1"
- -wd: weight decay, default=1e-4
- -l: lambda for sparsity, default=0.0001
- -percent: scale sparse rate, default=0.5
- -save: path to save model and training log, default='./log'
- -trainflag: normal train or not, default=False
- -trainspflag: training with sparsity or not, default=False
- -retrainflag: retrain or not, default=False
- -resumeflag: resume training or not, default=False
- -pruneflag: prune or not, default=False
Tips: Please put your dataset in the data folder or modify your path to dataset in get_data.py before running the following code.
python train.py -trainflag -net densenet40
python train.py -net densenet40 -trainspflag -l 0.00001
python train.py -pruneflag -net densenet40 -percent 0.4
python train.py -retrainflag -net densenet40
| model | params | FLOPs | best_top1 | best_top5 | inference time(ms) |
|---|---|---|---|---|---|
| DenseNet-40 baseline | 1.110M | 287.826M | 74.880% | 94.080% | 0.3457976494639176 |
| DenseNet-40 train with sparsity | 1.110M | 287.826M | 74.760% | 94.000% | 0.28089947460222564 |
| DenseNet-40 40% pruned | 706.364K | 196.729M | 72.960% | 93.110% | 0.27184 |
| DenseNet-40 40% pruned and fine-tune | 706.364K | 196.729M | 74.970% | 93.920% | 0.260371252737705 |
| DenseNet-40 60% pruned | 502.940K | 148.270M | 42.320% | 71.390% | 0.31692 |
| DenseNet-40 60% pruned and fine-tune | 502.940K | 148.270M | 74.670% | 94.170% | 0.285006638637279 |
https://github.com/Eric-mingjie/network-slimming
https://arxiv.org/pdf/1708.06519.pdf