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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

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.

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.

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