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PyTorch implementation of [1611.06440 Pruning Convolutional Neural Networks for Resource Efficient Inference]

This demonstrates pruning a VGG16 based classifier that classifies a small dog/cat dataset.

This was able to reduce the CPU runtime by x3 and the model size by x4.

For more details you can read the blog post.

At each pruning step 512 filters are removed from the network.

Usage

This repository uses the PyTorch ImageFolder loader, so it assumes that the images are in a different directory for each category.

Train

......... dogs

......... cats

Test

......... dogs

......... cats

The images were taken from here but you should try training this on your own data and see if it works!

Training: python finetune.py --train

Pruning: python finetune.py --prune

TBD