If you find the code useful in your research, please consider citing:
@InProceedings{He_2017_ICCV,
author = {He, Yihui and Zhang, Xiangyu and Sun, Jian},
title = {Channel Pruning for Accelerating Very Deep Neural Networks},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}
specify caffe path in cfgs.py and use plot.py to generate beautful loss plots.
python plot.py PATH/TO/LOGS
Results are consistent with original paper. seems there's no much difference between resnet-20 and plain-20. However, from the second plot, you can see that plain-110 have difficulty to converge.
How I generate prototxts:
use net_generator.py to generate solver.prototxt and trainval.prototxt, you can generate resnet or plain net of depth 20/32/44/56/110, or even deeper if you want. you just need to change n according to depth=6n+2
How I generate lmdb data:
./create_cifar.sh
create 4 pixel padded training LMDB and testing LMDB, then create a soft link ln -s cifar-10-batches-py in this folder.