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garyfanhku / pytorch_image_classification

PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST

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PyTorch Image Classification

Following papers are implemented using PyTorch.

  • ResNet (1512.03385)
  • ResNet-preact (1603.05027)
  • WRN (1605.07146)
  • DenseNet (1608.06993)
  • PyramidNet (1610.02915)
  • ResNeXt (1611.05431)
  • shake-shake (1705.07485)
  • Cutout (1708.04552)
  • Random Erasing (1708.04896)
  • SENet (1709.01507)
  • Mixup (1710.09412)
  • RICAP (1811.09030)

Requirements

  • Python >= 3.6
  • PyTorch >= 1.0.0
  • torchvision
  • tensorboardX (optional)

Usage

$ ./main.py --arch resnet_preact --depth 56 --outdir results

Use Cutout

$ ./main.py --arch resnet_preact --depth 56 --outdir results --use_cutout

Use RandomErasing

$ ./main.py --arch resnet_preact --depth 56 --outdir results --use_random_erasing

Use Mixup

$ ./main.py --arch resnet_preact --depth 56 --outdir results --use_mixup

Use cosine annealing

$ ./main.py --arch wrn --outdir results --scheduler cosine

Results on CIFAR-10

Results using almost same settings as papers

Model Test Error (median of 3 runs) Test Error (in paper) Training Time
VGG-like (depth 15, w/ BN, channel 64) 7.29 N/A 1h20m
ResNet-110 6.52 6.43 (best), 6.61 ± 0.16 3h06m
ResNet-preact-110 6.47 6.37 (median of 5 runs) 3h05m
ResNet-preact-164 bottleneck 5.90 5.46 (median of 5 runs) 4h01m
ResNet-preact-1001 bottleneck 4.62 (median of 5 runs), 4.69 ± 0.20
WRN-28-10 4.03 4.00 (median of 5 runs) 16h10m
WRN-28-10 w/ dropout 3.89 (median of 5 runs)
DenseNet-100 (k=12) 3.87 (1 run) 4.10 (1 run) 24h28m*
DenseNet-100 (k=24) 3.74 (1 run)
DenseNet-BC-100 (k=12) 4.69 4.51 (1 run) 15h20m
DenseNet-BC-250 (k=24) 3.62 (1 run)
DenseNet-BC-190 (k=40) 3.46 (1 run)
PyramidNet-110 (alpha=84) 4.40 4.26 ± 0.23 11h40m
PyramidNet-110 (alpha=270) 3.92 (1 run) 3.73 ± 0.04 24h12m*
PyramidNet-164 bottleneck (alpha=270) 3.44 (1 run) 3.48 ± 0.20 32h37m*
PyramidNet-272 bottleneck (alpha=200) 3.31 ± 0.08
ResNeXt-29 4x64d 3.89 ~3.75 (from Figure 7) 31h17m
ResNeXt-29 8x64d 3.97 (1 run) 3.65 (average of 10 runs) 42h50m*
ResNeXt-29 16x64d 3.58 (average of 10 runs)
shake-shake-26 2x32d (S-S-I) 3.68 3.55 (average of 3 runs) 33h49m
shake-shake-26 2x64d (S-S-I) 2.88 (1 run) 2.98 (average of 3 runs) 78h48m
shake-shake-26 2x96d (S-S-I) 2.90 (1 run) 2.86 (average of 5 runs) 101h32m*

Notes

  • Differences with papers in training settings:
    • Trained WRN-28-10 with batch size 64 (128 in paper).
    • Trained DenseNet-BC-100 (k=12) with batch size 32 and initial learning rate 0.05 (batch size 64 and initial learning rate 0.1 in paper).
    • Trained ResNeXt-29 4x64d with a single GPU, batch size 32 and initial learning rate 0.025 (8 GPUs, batch size 128 and initial learning rate 0.1 in paper).
    • Trained shake-shake models with a single GPU (2 GPUs in paper).
    • Trained shake-shake 26 2x64d (S-S-I) with batch size 64, and initial learning rate 0.1.
  • Test errors reported above are the ones at last epoch.
  • Experiments with only 1 run are done on different computer from the one used for experiments with 3 runs.
  • GeForce GTX 980 was used in these experiments.

VGG-like

$ python -u main.py --arch vgg --seed 7 --outdir results/vgg_15_BN_64/00

ResNet

$ python -u main.py --arch resnet --depth 110 --block_type basic --seed 7 --outdir results/resnet_basic_110/00

ResNet-preact

$ python -u main.py --arch resnet_preact --depth 110 --block_type basic --seed 7 --outdir results/resnet_preact_basic_110/00


$ python -u main.py --arch resnet_preact --depth 164 --block_type bottleneck --seed 7 --outdir results/resnet_preact_bottleneck_164/00

WRN

$ python -u main.py --arch wrn --depth 28 --widening_factor 10 --seed 7 --outdir results/wrn_28_10/00

DenseNet

$ python -u main.py --arch densenet --depth 100 --block_type bottleneck --growth_rate 12 --compression_rate 0.5 --batch_size 32 --base_lr 0.05 --seed 7 --outdir results/densenet_BC_100_12/00

PyramidNet

$ python -u main.py --arch pyramidnet --depth 110 --block_type basic --pyramid_alpha 84 --seed 7 --outdir results/pyramidnet_basic_110_84/00


$ python -u main.py --arch pyramidnet --depth 110 --block_type basic --pyramid_alpha 270 --seed 7 --outdir results/pyramidnet_basic_110_270/00

ResNeXt

$ python -u main.py --arch resnext --depth 29 --cardinality 4 --base_channels 64 --batch_size 32 --base_lr 0.025 --seed 7 --outdir results/resnext_29_4x64d/00


$ python -u main.py --arch resnext --depth 29 --cardinality 8 --base_channels 64 --batch_size 64 --base_lr 0.05 --seed 7 --outdir results/resnext_29_8x64d/00

shake-shake

$ python -u main.py --arch shake_shake --depth 26 --base_channels 32 --shake_forward True --shake_backward True --shake_image True --seed 7 --outdir results/shake_shake_26_2x32d_SSI/00


$ python -u main.py --arch shake_shake --depth 26 --base_channels 64 --shake_forward True --shake_backward True --shake_image True --batch_size 64 --base_lr 0.1 --seed 7 --outdir results/shake_shake_26_2x64d_SSI/00


$ python -u main.py --arch shake_shake --depth 26 --base_channels 96 --shake_forward True --shake_backward True --shake_image True --seed 7 --outdir results/shake_shake_26_2x96d_SSI/00

Results

Model Test Error (1 run) # of Epochs Training Time
WRN-28-10, Cutout 16 3.19 200 16h23m
shake-shake-26 2x64d, Cutout 16 2.64 1800 78h55m 
python -u main.py --arch wrn --depth 28 --outdir results/wrn_28_10_cutout16 --epochs 200 --scheduler cosine --base_lr 0.1 --batch_size 64 --seed 17 --use_cutout --cutout_size 16


python -u main.py --arch shake_shake --depth 26 --base_channels 64 --outdir results/shake_shake_26_2x64d_SSI_cutout16 --epochs 300 --scheduler cosine --base_lr 0.1 --batch_size 64 --seed 17 --use_cutout --cutout_size 16

Results on FashionMNIST

Model Test Error (1 run) # of Epochs Training Time
ResNet-preact-20, widening factor 4, Cutout 12 4.17 200 1h32m
ResNet-preact-20, widening factor 4, Cutout 14 4.11 200 1h32m
ResNet-preact-50, Cutout 12 4.45 200 57m
ResNet-preact-50, Cutout 14 4.38 200 57m
ResNet-preact-50, widening factor 4,Cutout 12 4.07 200 3h37m
ResNet-preact-50, widening factor 4,Cutout 14 4.13 200 3h39m
shake-shake-26 2x32d (S-S-I), Cutout 12 4.08 400 3h41m
shake-shake-26 2x32d (S-S-I), Cutout 14 4.05 400 3h39m
shake-shake-26 2x96d (S-S-I), Cutout 12 3.72 400 13h46m
shake-shake-26 2x96d (S-S-I), Cutout 14 3.85 400 13h39m
shake-shake-26 2x96d (S-S-I), Cutout 12 3.65 800 26h42m
shake-shake-26 2x96d (S-S-I), Cutout 14 3.60 800 26h42m
Model Test Error (median of 3 runs) # of Epochs Training Time
ResNet-preact-20 5.04 200 26m
ResNet-preact-20, Cutout 6 4.84 200 26m
ResNet-preact-20, Cutout 8 4.64 200 26m
ResNet-preact-20, Cutout 10 4.74 200 26m
ResNet-preact-20, Cutout 12 4.68 200 26m
ResNet-preact-20, Cutout 14 4.64 200 26m
ResNet-preact-20, Cutout 16 4.49 200 26m
ResNet-preact-20, RandomErasing 4.61 200 26m
ResNet-preact-20, Mixup 4.92 200 26m
ResNet-preact-20, Mixup 4.64 400 52m

Note

  • Results reported in the tables are the test errors at last epochs.
  • All models are trained using cosine annealing with initial learning rate 0.2.
  • Following data augmentations are applied to the training data:
    • Images are padded with 4 pixels on each side, and 28x28 patches are randomly cropped from the padded images.
    • Images are randomly flipped horizontally.
  • GeForce GTX 1080 Ti was used in these experiments.

Results on MNIST

Model Test Error (median of 3 runs) # of Epochs Training Time
ResNet-preact-20 0.38 40 9m
ResNet-preact-20, Cutout 6 0.40 40 9m
ResNet-preact-20, Cutout 8 0.32 40 9m
ResNet-preact-20, Cutout 10 0.34 40 9m
ResNet-preact-20, Cutout 12 0.30 40 9m
ResNet-preact-20, Cutout 14 0.34 40 9m
ResNet-preact-20, Cutout 16 0.35 40 9m
ResNet-preact-20, RandomErasing 0.36 40 9m
ResNet-preact-20, Mixup (alpha=1) 0.39 40 11m
ResNet-preact-20, Mixup (alpha=1) 0.37 80 21m
ResNet-preact-20, Mixup (alpha=0.5) 0.33 40 11m
ResNet-preact-20, Mixup (alpha=0.5) 0.38 80 21m
ResNet-preact-20, widening factor 4, Cutout 12 0.29 40 40m
ResNet-preact-50 0.39 40 22m
ResNet-preact-50, Cutout 12 0.31 40 22m
ResNet-preact-50, widening factor 4, Cutout 12 0.29 (1 run) 40 1h40m
shake-shake-26 2x32d (S-S-I), Cutout 12 0.29 100 1h48m

Note

  • Results reported in the table are the test errors at last epochs.
  • All models are trained using cosine annealing with initial learning rate 0.2.
  • GeForce GTX 980 was used in these experiments.

Experiments

Experiment on residual units, learning rate scheduling, and data augmentation

In this experiment, the effects of the following on classification accuracy are investigated:

  • PyramidNet-like residual units
  • Cosine annealing of learning rate
  • Cutout
  • Random Erasing
  • Mixup
  • Preactivation of shortcuts after downsampling

ResNet-preact-56 is trained on CIFAR-10 with initial learning rate 0.2 in this experiment.

Note

  • PyramidNet paper (1610.02915) showed that removing first ReLU in residual units and adding BN after last convolutions in residual units both improve classification accuracy.
  • SGDR paper (1608.03983) showed cosine annealing improves classification accuracy even without restarting.

Results

  • PyramidNet-like units works.
    • It might be better not to preactivate shortcuts after downsampling when using PyramidNet-like units.
  • Cosine annealing slightly improves accuracy.
  • Cutout, RandomErasing, and Mixup all work great.
    • Mixup needs longer training.

Model Test Error (median of 5 runs) Training Time
w/ 1st ReLU, w/o last BN, preactivate shortcut after downsampling 6.45 95 min
w/ 1st ReLU, w/o last BN 6.47 95 min
w/o 1st ReLU, w/o last BN 6.14 89 min
w/ 1st ReLU, w/ last BN 6.43 104 min
w/o 1st ReLU, w/ last BN 5.85 98 min
w/o 1st ReLU, w/ last BN, preactivate shortcut after downsampling 6.27 98 min
w/o 1st ReLU, w/ last BN, Cosine annealing 5.72 98 min
w/o 1st ReLU, w/ last BN, Cutout 4.96 98 min
w/o 1st ReLU, w/ last BN, RandomErasing 5.22 98 min
w/o 1st ReLU, w/ last BN, Mixup (300 epochs) 5.11 191 min
preactivate shortcut after downsampling
$ python -u main.py --arch resnet_preact --depth 56 --block_type basic --base_lr 0.2 --preact_stage '[true, true, true]' --remove_first_relu false --add_last_bn false --seed 7 --outdir results/experiments/00_preact_after_downsampling/00

w/ 1st ReLU, w/o last BN
$ python -u main.py --arch resnet_preact --depth 56 --block_type basic --base_lr 0.2 --preact_stage '[true, false, false]' --remove_first_relu false --add_last_bn false --seed 7 --outdir results/experiments/01_w_relu_wo_bn/00

w/o 1st ReLU, w/o last BN
$ python -u main.py --arch resnet_preact --depth 56 --block_type basic --base_lr 0.2 --preact_stage '[true, false, false]' --remove_first_relu true --add_last_bn false --seed 7 --outdir results/experiments/02_wo_relu_wo_bn/00

w/ 1st ReLU, w/ last BN
$ python -u main.py --arch resnet_preact --depth 56 --block_type basic --base_lr 0.2 --preact_stage '[true, false, false]' --remove_first_relu false --add_last_bn true --seed 7 --outdir results/experiments/03_w_relu_w_bn/00

w/o 1st ReLU, w/ last BN
$ python -u main.py --arch resnet_preact --depth 56 --block_type basic --base_lr 0.2 --preact_stage '[true, false, false]' --remove_first_relu true --add_last_bn true --seed 7 --outdir results/experiments/04_wo_relu_w_bn/00

w/o 1st ReLU, w/ last BN, preactivate shortcut after downsampling
$ python -u main.py --arch resnet_preact --depth 56 --block_type basic --base_lr 0.2 --preact_stage '[true, true, true]' --remove_first_relu true --add_last_bn true --seed 7 --outdir results/experiments/05_preact_after_downsampling/00

w/o 1st ReLU, w/ last BN, cosine annealing
$ python -u main.py --arch resnet_preact --depth 56 --block_type basic --base_lr 0.2 --preact_stage '[true, false, false]' --remove_first_relu true --add_last_bn true --scheduler cosine --seed 7 --outdir results/experiments/06_cosine_annealing/00

w/o 1st ReLU, w/ last BN, Cutout
$ python -u main.py --arch resnet_preact --depth 56 --block_type basic --base_lr 0.2 --preact_stage '[true, false, false]' --remove_first_relu true --add_last_bn true --use_cutout --seed 7 --outdir results/experiments/07_cutout/00

w/o 1st ReLU, w/ last BN, RandomErasing
$ python -u main.py --arch resnet_preact --depth 56 --block_type basic --base_lr 0.2 --preact_stage '[true, false, false]' --remove_first_relu true --add_last_bn true --use_random_erasing --seed 7 --outdir results/experiments/08_random_erasing/00

w/o 1st ReLU, w/ last BN, Mixup
$ python -u main.py --arch resnet_preact --depth 56 --block_type basic --base_lr 0.2 --preact_stage '[true, false, false]' --remove_first_relu true --add_last_bn true --use_mixup --seed 7 --outdir results/experiments/09_mixup/00

References

  • He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. "Deep Residual Learning for Image Recognition." The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. link, arXiv:1512.03385
  • He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. "Identity Mappings in Deep Residual Networks." In European Conference on Computer Vision (ECCV). 2016. arXiv:1603.05027, Torch implementation
  • Zagoruyko, Sergey, and Nikos Komodakis. "Wide Residual Networks." Proceedings of the British Machine Vision Conference (BMVC), 2016. arXiv:1605.07146, Torch implementation
  • Loshchilov, Ilya, and Frank Hutter. "SGDR: Stochastic Gradient Descent with Warm Restarts." In International Conference on Learning Representations (ICLR), 2017. link, arXiv:1608.03983, Lasagne implementation
  • Huang, Gao, Zhuang Liu, Kilian Q Weinberger, and Laurens van der Maaten. "Densely Connected Convolutional Networks." The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. link, arXiv:1608.06993, Torch implementation
  • Han, Dongyoon, Jiwhan Kim, and Junmo Kim. "Deep Pyramidal Residual Networks." The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. link, arXiv:1610.02915, Torch implementation, Caffe implementation, PyTorch implementation
  • Xie, Saining, Ross Girshick, Piotr Dollar, Zhuowen Tu, and Kaiming He. "Aggregated Residual Transformations for Deep Neural Networks." The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. link, arXiv:1611.05431, Torch implementation
  • Gastaldi, Xavier. "Shake-Shake regularization of 3-branch residual networks." In International Conference on Learning Representations (ICLR) Workshop, 2017. link, arXiv:1705.07485, Torch implementation
  • DeVries, Terrance, and Graham W. Taylor. "Improved Regularization of Convolutional Neural Networks with Cutout." arXiv preprint arXiv:1708.04552 (2017). arXiv:1708.04552, PyTorch implementation
  • Zhong, Zhun, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang. "Random Erasing Data Augmentation." arXiv preprint arXiv:1708.04896 (2017). arXiv:1708.04896, PyTorch implementation
  • Hu, Jie, Li Shen, and Gang Sun. "Squeeze-and-Excitation Networks." The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 7132-7141. link, arXiv:1709.01507, Caffe implementation
  • Zhang, Hongyi, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz. "mixup: Beyond Empirical Risk Minimization." In International Conference on Learning Representations (ICLR), 2017. link, arXiv:1710.09412
  • Recht, Benjamin, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar. "Do CIFAR-10 Classifiers Generalize to CIFAR-10?" arXiv preprint arXiv:1806.00451 (2018). arXiv:1806.00451
  • Takahashi, Ryo, Takashi Matsubara, and Kuniaki Uehara. "Data Augmentation using Random Image Cropping and Patching for Deep CNNs." Proceedings of The 10th Asian Conference on Machine Learning (ACML), 2018. link, arXiv:1811.09030

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PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST

License:MIT License

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