Model-Compression-Keras

cnn compression for keras

English is not my native language; please excuse typing errors.

The implementation for "deep compression"(only apply weight pruning and quantization).

This implementation is a little different from original mathod in the paper.

Pruning small weights for each convolution filter. The pruning threshold is set by handcraft.

How to use

1. Train the model (this repo currently support normal-cnn, ResNet, Inception, Densenet, Unet, PointNet)

For CIFAR-10 and compression rate=0.8:

python train_and_compress_cnn.py --model='vgg' --data='c10' --compress-rate=0.8

For CIFAR-100 and compression rate=0.8:

python train_and_compress_cnn.py --model='vgg' --data='c100' --compress-rate=0.8

For UNet and compression rate=0.8:

python train_and_compress_unet.py --compress-rate=0.8

For PointNet and compression rate=0.8:

python train_and_compress_pointnet.py --compress-rate=0.8

2. Decode and evaluation:

   python decode_and_evaluate_cnn.py --model='vgg'

   python decode_and_evaluate_unet.py

   python decode_and_evaluate_pointnet.py

Results

CIFAR-10

VGG like net parameter:1.34M, val acc:85.16%, storage:5273KB

ResNet-50 parameter:0.762M, val acc:93.79%, storage:3366KB

Person segmentation

Refered repo: Person-Segmentation-Keras

Unet parameter:9.55M, val acc:98.46%, storage:37410KB

PointNet

Refered repo:PointNet-Keras

PointNet parameter:3.49M, val acc:88.23%, storage:13772KB