A Comprehensive Overhaul of Feature Distillation

Accepted at ICCV 2019

Official PyTorch implementation of "A Comprehensive Overhaul of Feature Distillation" | paper | project page | blog

Byeongho Heo, Jeesoo Kim, Sangdoo Yun, Hyojin Park, Nojun Kwak, Jin Young Choi

Clova AI Research, NAVER Corp. \ Seoul National University

Requirements

Python3
PyTorch (> 0.4.1)
torchvision
numpy
scipy

Updates

19 Nov 2019 Segmentation released

10 Sep 2019 Initial upload

CIFAR-100

Settings

We provide the code of the experimental settings specified in the paper.

Setup	Compression type	Teacher	Student	Teacher size	Student size	Size ratio
(a)	Depth	WRN 28-4	WRN 16-4	5.87M	2.77M	47.2%
(b)	Channel	WRN 28-4	WRN 28-2	5.87M	1.47M	25.0%
(c)	Depth & channel	WRN 28-4	WRN 16-2	5.87M	0.70M	11.9%
(d)	Architecture	WRN 28-4	ResNet 56	5.87M	0.86M	14.7%
(e)	Architecture	Pyramid-200	WRN 28-4	26.84M	5.87M	21.9%
(f)	Architecture	Pyramid-200	Pyramid-110	26.84M	3.91M	14.6%

Teacher models

Download following pre-trained teacher network and put them into ./data directory

Training

Run CIFAR-100/train_with_distillation.py with setting alphabet (a - f)

cd CIFAR-100
python train_with_distillation.py \
--setting a \
--epochs 200 \
--batch_size 128 \
--lr 0.1 \
--momentum 0.9 \
--weight_decay 5e-4

For pyramid teacher (e, f), we used batch-size 64 to save gpu memory.

cd CIFAR-100
python train_with_distillation.py \
--setting e \
--epochs 200 \
--batch_size 64 \
--lr 0.1 \
--momentum 0.9 \
--weight_decay 5e-4

Experimental results

Performance measure is classification error rate (%)

Setup	Teacher	Student	Original	Proposed	Improvement
(a)	WRN 28-4	WRN 16-4	22.72%	20.89%	1.83%
(b)	WRN 28-4	WRN 28-2	24.88%	21.98%	2.90%
(c)	WRN 28-4	WRN 16-2	27.32%	24.08%	3.24%
(d)	WRN 28-4	ResNet 56	27.68%	24.44%	3.24%
(f)	Pyramid-200	WRN 28-4	21.09%	17.80%	3.29%
(g)	Pyramid-200	Pyramid-110	22.58%	18.89%	3.69%

ImageNet

Settings

Setup	Compression type	Teacher	Student	Teacher size	Student size	Size ratio
(a)	Depth	ResNet 152	ResNet 50	60.19M	25.56M	42.47%
(b)	Architecture	ResNet 50	MobileNet	25.56M	4.23M	16.55%

In case of ImageNet, teacher model will be automatically downloaded from PyTorch sites.

Training

(a) : ResNet152 to ResNet50

cd ImageNet
python train_with_distillation.py \
--data_path your/path/to/ImageNet \
--net_type resnet \
--epochs 100 \
--lr 0.1 \
--batch_size 256

(b) : ResNet50 to MobileNet

cd ImageNet
python train_with_distillation.py \
--data_path your/path/to/ImageNet \
--net_type mobilenet \
--epochs 100 \
--lr 0.1 \
--batch_size 256

Experimental results

ResNet 50

Network	Method	Top1-error	Top5-error
ResNet 152	Teacher	21.69	5.95
ResNet 50	Original	23.72	6.97
ResNet 50	Proposed	21.65	5.83

MobileNet

Network	Method	Top1-error	Top5-error
ResNet 50	Teacher	23.84	7.14
Mobilenet	Original	31.13	11.24
Mobilenet	Proposed	28.75	9.66

Segmentation - Pascal VOC

Our segmentation code is based on pytorch-deeplab-xception.

Additional requirements

tqdm
matplotlib
pillow

Settings

Teacher	Student	Teacher size	Student size	Size ratio
ResNet 101	ResNet 18	59.3M	16.6	28.0%
ResNet 101	MobileNetV2	59.3M	5.8M	9.8%

Teacher models

Download following pre-trained teacher network and put it into ./Segmentation/pretrained directory

ResNet101-DeepLabV3+

We used pre-trained model in pytorch-deeplab-xception for teacher network.

Training

First, move to segmentation folder : cd Segmentation
Next, configure your dataset path on Segmentation/mypath.py

Without distillation

ResNet 18

CUDA_VISIBLE_DEVICES=0,1 python train.py --backbone resnet18 --gpu-ids 0,1 --dataset pascal --use-sbd --nesterov

MobileNetV2

CUDA_VISIBLE_DEVICES=0,1 python train.py --backbone mobilenet --gpu-ids 0,1 --dataset pascal --use-sbd --nesterov

Distillation

ResNet 18

CUDA_VISIBLE_DEVICES=0,1 python train_with_distillation.py --backbone resnet18 --gpu-ids 0,1 --dataset pascal --use-sbd --nesterov

-MobileNetV2

CUDA_VISIBLE_DEVICES=0,1 python train_with_distillation.py --backbone mobilenet --gpu-ids 0,1 --dataset pascal --use-sbd --nesterov

Experimental results

This numbers are based validation performance of our code.

ResNet 18

Network	Method	mIOU
ResNet 101	Teacher	77.89
ResNet 18	Original	72.07
ResNet 18	Proposed	73.98

MobileNetV2

Network	Method	mIOU
ResNet 101	Teacher	77.89
MobileNetV2	Original	68.46
MobileNetV2	Proposed	71.19

In the paper, we reported performance on the test set, but our code measures the performance on the val set. Therefore, the performance on code is not same as the paper. If you want accurate measure, please measure performance on test set with Pascal VOC evaluation server.

Citation

@inproceedings{heo2019overhaul,
  title={A Comprehensive Overhaul of Feature Distillation},
  author={Heo, Byeongho and Kim, Jeesoo and Yun, Sangdoo and Park, Hyojin and Kwak, Nojun and Choi, Jin Young},
  booktitle = {International Conference on Computer Vision (ICCV)},
  year={2019}
}

License

Copyright (c) 2019-present NAVER Corp.

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