Awesome

Distillation-Based Channel Pruning

This repository is official pytorch implementation of the paper "Lightweight Alpha Matting Network Using Distillation-Based Channel Pruning" (ACCV 2022) which is a follow-up research of the paper of SPKD (IEEE SPL 2020).

Donggeun Yoon, Jinsun Park, Donghyeon Cho

Introduction

We have proposed a distillation-based channel pruning method for lightening a deep image matting network. In the pruning step, we train a student network that has the same architecture with a teacher network using the distillation-based sparsification loss. Then, we train the pruned student network using the same distillation loss utilized in the pruning step.

Prepare

Dataset

1. Please contact authors requesting for the Adobe Image Matting dataset.
2. Download images from the COCO and Pascal VOC datasets in folder data and Run the following command to composite images.

$ python pre_process.py

3. Run the following command to seperate the composited datasets with training set and valid set.

$ python data_gen.py

Pre-trained model

Download pretrained teacher model before train and place in folder pretrained.

Pruning Stage

Our method consists of two stages: Pruning stage and Training Stage. First, Run the following command to get pruned model suitable for SPKD.

$ python train_prune.py --config configs/train_SPKD.yaml

Training Stage

Second, Train pruned model. The model cfg is saved in result/SPKD/pruned.tar.

$ python train_pruned.py --config configs/train_SPKD.yaml

Citation

@article{yoon2022lightweight,
  title={Lightweight Alpha Matting Network Using Distillation-Based Channel Pruning},
  author={Yoon, Donggeun and Park, Jinsun and Cho, Donghyeon},
  journal={arXiv preprint arXiv:2210.07760},
  year={2022}
}

Acknowledgement

The code is built upon Deep image matting (pytorch).