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Awesome Open Source

PyTorch Image Classification

Following papers are implemented using PyTorch.

Requirements

  • Ubuntu (It's only tested on Ubuntu, so it may not work on Windows.)
  • Python >= 3.7
  • PyTorch >= 1.4.0
  • torchvision
  • NVIDIA Apex
pip install -r requirements.txt

Usage

python train.py --config configs/cifar/resnet_preact.yaml

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 train.py --config configs/cifar/vgg.yaml

ResNet

python train.py --config configs/cifar/resnet.yaml

ResNet-preact

python train.py --config configs/cifar/resnet_preact.yaml \
    train.output_dir experiments/resnet_preact_basic_110/exp00

python train.py --config configs/cifar/resnet_preact.yaml \
    model.resnet_preact.depth 164 \
    model.resnet_preact.block_type bottleneck \
    train.output_dir experiments/resnet_preact_bottleneck_164/exp00

WRN

python train.py --config configs/cifar/wrn.yaml

DenseNet

python train.py --config configs/cifar/densenet.yaml

PyramidNet

python train.py --config configs/cifar/pyramidnet.yaml \
    model.pyramidnet.depth 110 \
    model.pyramidnet.block_type basic \
    model.pyramidnet.alpha 84 \
    train.output_dir experiments/pyramidnet_basic_110_84/exp00

python train.py --config configs/cifar/pyramidnet.yaml \
    model.pyramidnet.depth 110 \
    model.pyramidnet.block_type basic \
    model.pyramidnet.alpha 270 \
    train.output_dir experiments/pyramidnet_basic_110_270/exp00

ResNeXt

python train.py --config configs/cifar/resnext.yaml \
    model.resnext.cardinality 4 \
    train.batch_size 32 \
    train.base_lr 0.025 \
    train.output_dir experiments/resnext_29_4x64d/exp00

python train.py --config configs/cifar/resnext.yaml \
    train.batch_size 64 \
    train.base_lr 0.05 \
    train.output_dir experiments/resnext_29_8x64d/exp00

shake-shake

python train.py --config configs/cifar/shake_shake.yaml \
    model.shake_shake.initial_channels 32 \
    train.output_dir experiments/shake_shake_26_2x32d_SSI/exp00

python train.py --config configs/cifar/shake_shake.yaml \
    model.shake_shake.initial_channels 64 \
    train.batch_size 64 \
    train.base_lr 0.1 \
    train.output_dir experiments/shake_shake_26_2x64d_SSI/exp00

python train.py --config configs/cifar/shake_shake.yaml \
    model.shake_shake.initial_channels 96 \
    train.batch_size 64 \
    train.base_lr 0.1 \
    train.output_dir experiments/shake_shake_26_2x96d_SSI/exp00

Results

Model Test Error (1 run) # of Epochs Training Time
ResNet-preact-20, widening factor 4 4.91 200 1h26m
ResNet-preact-20, widening factor 4 4.01 400 2h53m
ResNet-preact-20, widening factor 4 3.99 1800 12h53m
ResNet-preact-20, widening factor 4, Cutout 16 3.71 200 1h26m
ResNet-preact-20, widening factor 4, Cutout 16 3.46 400 2h53m
ResNet-preact-20, widening factor 4, Cutout 16 3.76 1800 12h53m
ResNet-preact-20, widening factor 4, RICAP (beta=0.3) 3.45 200 1h26m
ResNet-preact-20, widening factor 4, RICAP (beta=0.3) 3.11 400 2h53m
ResNet-preact-20, widening factor 4, RICAP (beta=0.3) 3.15 1800 12h53m
Model Test Error (1 run) # of Epochs Training Time
WRN-28-10, Cutout 16 3.19 200 6h35m
WRN-28-10, mixup (alpha=1) 3.32 200 6h35m
WRN-28-10, RICAP (beta=0.3) 2.83 200 6h35m
WRN-28-10, Dual-Cutout (alpha=0.1) 2.87 200 12h42m
WRN-28-10, Cutout 16 3.07 400 13h10m
WRN-28-10, mixup (alpha=1) 3.04 400 13h08m
WRN-28-10, RICAP (beta=0.3) 2.71 400 13h08m
WRN-28-10, Dual-Cutout (alpha=0.1) 2.76 400 25h20m
shake-shake-26 2x64d, Cutout 16 2.64 1800 78h55m*
shake-shake-26 2x64d, mixup (alpha=1) 2.63 1800 35h56m
shake-shake-26 2x64d, RICAP (beta=0.3) 2.29 1800 35h10m
shake-shake-26 2x64d, Dual-Cutout (alpha=0.1) 2.64 1800 68h34m
shake-shake-26 2x96d, Cutout 16 2.50 1800 60h20m
shake-shake-26 2x96d, mixup (alpha=1) 2.36 1800 60h20m
shake-shake-26 2x96d, RICAP (beta=0.3) 2.10 1800 60h20m
shake-shake-26 2x96d, Dual-Cutout (alpha=0.1) 2.41 1800 113h09m
shake-shake-26 2x128d, Cutout 16 2.58 1800 85h04m
shake-shake-26 2x128d, RICAP (beta=0.3) 1.97 1800 85h06m

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 1080 Ti was used in these experiments, except ones with *, which are done using GeForce GTX 980.
python train.py --config configs/cifar/wrn.yaml \
    train.batch_size 64 \
    train.output_dir experiments/wrn_28_10_cutout16 \
    scheduler.type cosine \
    augmentation.use_cutout True

python train.py --config configs/cifar/shake_shake.yaml \
    model.shake_shake.initial_channels 64 \
    train.batch_size 64 \
    train.base_lr 0.1 \
    scheduler.epochs 300 \
    train.output_dir experiments/shake_shake_26_2x64d_SSI_cutout16/exp00 \
    augmentation.use_cutout True

Results using multi-GPU

Model batch size #GPUs Test Error (1 run) # of Epochs Training Time*
WRN-28-10, RICAP (beta=0.3) 512 1 2.63 200 3h41m
WRN-28-10, RICAP (beta=0.3) 256 2 2.71 200 2h14m
WRN-28-10, RICAP (beta=0.3) 128 4 2.89 200 1h01m
WRN-28-10, RICAP (beta=0.3) 64 8 2.75 200 34m

Note

  • Tesla V100 was used in these experiments.
Using 1 GPU
python train.py --config configs/cifar/wrn.yaml \
    train.base_lr 0.2 \
    train.batch_size 512 \
    scheduler.epochs 200 \
    scheduler.type cosine \
    train.output_dir experiments/wrn_28_10_ricap_1gpu/exp00 \
    augmentation.use_ricap True \
    augmentation.use_random_crop False
Using 2 GPUs
python -m torch.distributed.launch --nproc_per_node 2 \
    train.py --config configs/cifar/wrn.yaml \
    train.distributed True \
    train.base_lr 0.2 \
    train.batch_size 256 \
    scheduler.epochs 200 \
    scheduler.type cosine \
    train.output_dir experiments/wrn_28_10_ricap_2gpus/exp00 \
    augmentation.use_ricap True \
    augmentation.use_random_crop False
Using 4 GPUs
python -m torch.distributed.launch --nproc_per_node 4 \
    train.py --config configs/cifar/wrn.yaml \
    train.distributed True \
    train.base_lr 0.2 \
    train.batch_size 128 \
    scheduler.epochs 200 \
    scheduler.type cosine \
    train.output_dir experiments/wrn_28_10_ricap_4gpus/exp00 \
    augmentation.use_ricap True \
    augmentation.use_random_crop False
Using 8 GPUs
python -m torch.distributed.launch --nproc_per_node 8 \
    train.py --config configs/cifar/wrn.yaml \
    train.distributed True \
    train.base_lr 0.2 \
    train.batch_size 64 \
    scheduler.epochs 200 \
    scheduler.type cosine \
    train.output_dir experiments/wrn_28_10_ricap_8gpus/exp00 \
    augmentation.use_ricap True \
    augmentation.use_random_crop False

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 0.40 100 12m
ResNet-preact-20, Cutout 6 0.32 100 12m
ResNet-preact-20, Cutout 8 0.25 100 12m
ResNet-preact-20, Cutout 10 0.27 100 12m
ResNet-preact-20, Cutout 12 0.26 100 12m
ResNet-preact-20, Cutout 14 0.26 100 12m
ResNet-preact-20, Cutout 16 0.25 100 12m
ResNet-preact-20, Mixup (alpha=1) 0.40 100 12m
ResNet-preact-20, Mixup (alpha=0.5) 0.38 100 12m
ResNet-preact-20, widening factor 4, Cutout 14 0.26 100 45m
ResNet-preact-50, Cutout 14 0.29 100 28m
ResNet-preact-50, widening factor 4, Cutout 14 0.25 100 1h50m
shake-shake-26 2x96d (S-S-I), Cutout 14 0.24 100 3h22m

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 1080 Ti was used in these experiments.

Results on Kuzushiji-MNIST

Model Test Error (median of 3 runs) # of Epochs Training Time
ResNet-preact-20, Cutout 14 0.82 (best 0.67) 200 24m
ResNet-preact-20, widening factor 4, Cutout 14 0.72 (best 0.67) 200 1h30m
PyramidNet-110-270, Cutout 14 0.72 (best 0.70) 200 10h05m
shake-shake-26 2x96d (S-S-I), Cutout 14 0.66 (best 0.63) 200 6h46m

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 1080 Ti 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 train.py --config configs/cifar/resnet_preact.yaml \
    train.base_lr 0.2 \
    model.resnet_preact.depth 56 \
    model.resnet_preact.preact_stage '[True, True, True]' \
    model.resnet_preact.remove_first_relu False \
    model.resnet_preact.add_last_bn False \
    train.output_dir experiments/resnet_preact_after_downsampling/exp00

w/ 1st ReLU, w/o last BN
python train.py --config configs/cifar/resnet_preact.yaml \
    train.base_lr 0.2 \
    model.resnet_preact.depth 56 \
    model.resnet_preact.preact_stage '[True, False, False]' \
    model.resnet_preact.remove_first_relu False \
    model.resnet_preact.add_last_bn False \
    train.output_dir experiments/resnet_preact_w_relu_wo_bn/exp00

w/o 1st ReLU, w/o last BN
python train.py --config configs/cifar/resnet_preact.yaml \
    train.base_lr 0.2 \
    model.resnet_preact.depth 56 \
    model.resnet_preact.preact_stage '[True, False, False]' \
    model.resnet_preact.remove_first_relu True \
    model.resnet_preact.add_last_bn False \
    train.output_dir experiments/resnet_preact_wo_relu_wo_bn/exp00

w/ 1st ReLU, w/ last BN
python train.py --config configs/cifar/resnet_preact.yaml \
    train.base_lr 0.2 \
    model.resnet_preact.depth 56 \
    model.resnet_preact.preact_stage '[True, False, False]' \
    model.resnet_preact.remove_first_relu False \
    model.resnet_preact.add_last_bn True \
    train.output_dir experiments/resnet_preact_w_relu_w_bn/exp00

w/o 1st ReLU, w/ last BN
python train.py --config configs/cifar/resnet_preact.yaml \
    train.base_lr 0.2 \
    model.resnet_preact.depth 56 \
    model.resnet_preact.preact_stage '[True, False, False]' \
    model.resnet_preact.remove_first_relu True \
    model.resnet_preact.add_last_bn True \
    train.output_dir experiments/resnet_preact_wo_relu_w_bn/exp00

w/o 1st ReLU, w/ last BN, preactivate shortcut after downsampling
python train.py --config configs/cifar/resnet_preact.yaml \
    train.base_lr 0.2 \
    model.resnet_preact.depth 56 \
    model.resnet_preact.preact_stage '[True, True, True]' \
    model.resnet_preact.remove_first_relu True \
    model.resnet_preact.add_last_bn True \
    train.output_dir experiments/resnet_preact_after_downsampling_wo_relu_w_bn/exp00

w/o 1st ReLU, w/ last BN, cosine annealing
python train.py --config configs/cifar/resnet_preact.yaml \
    train.base_lr 0.2 \
    model.resnet_preact.depth 56 \
    model.resnet_preact.preact_stage '[True, False, False]' \
    model.resnet_preact.remove_first_relu True \
    model.resnet_preact.add_last_bn True \
    scheduler.type cosine \
    train.output_dir experiments/resnet_preact_wo_relu_w_bn_cosine/exp00

w/o 1st ReLU, w/ last BN, Cutout
python train.py --config configs/cifar/resnet_preact.yaml \
    train.base_lr 0.2 \
    model.resnet_preact.depth 56 \
    model.resnet_preact.preact_stage '[True, False, False]' \
    model.resnet_preact.remove_first_relu True \
    model.resnet_preact.add_last_bn True \
    augmentation.use_cutout True \
    train.output_dir experiments/resnet_preact_wo_relu_w_bn_cutout/exp00

w/o 1st ReLU, w/ last BN, RandomErasing
python train.py --config configs/cifar/resnet_preact.yaml \
    train.base_lr 0.2 \
    model.resnet_preact.depth 56 \
    model.resnet_preact.preact_stage '[True, False, False]' \
    model.resnet_preact.remove_first_relu True \
    model.resnet_preact.add_last_bn True \
    augmentation.use_random_erasing True \
    train.output_dir experiments/resnet_preact_wo_relu_w_bn_random_erasing/exp00

w/o 1st ReLU, w/ last BN, Mixup
python train.py --config configs/cifar/resnet_preact.yaml \
    train.base_lr 0.2 \
    model.resnet_preact.depth 56 \
    model.resnet_preact.preact_stage '[True, False, False]' \
    model.resnet_preact.remove_first_relu True \
    model.resnet_preact.add_last_bn True \
    augmentation.use_mixup True \
    train.output_dir experiments/resnet_preact_wo_relu_w_bn_mixup/exp00

Experiments on label smoothing, Mixup, RICAP, and Dual-Cutout

Results on CIFAR-10

Model Test Error (median of 3 runs) # of Epochs Training Time
ResNet-preact-20 7.60 200 24m
ResNet-preact-20, label smoothing (epsilon=0.001) 7.51 200 25m
ResNet-preact-20, label smoothing (epsilon=0.01) 7.21 200 25m
ResNet-preact-20, label smoothing (epsilon=0.1) 7.57 200 25m
ResNet-preact-20, mixup (alpha=1) 7.24 200 26m
ResNet-preact-20, RICAP (beta=0.3), w/ random crop 6.88 200 28m
ResNet-preact-20, RICAP (beta=0.3) 6.77 200 28m
ResNet-preact-20, Dual-Cutout 16 (alpha=0.1) 6.24 200 45m
ResNet-preact-20 7.05 400 49m
ResNet-preact-20, label smoothing (epsilon=0.001) 7.20 400 49m
ResNet-preact-20, label smoothing (epsilon=0.01) 6.97 400 49m
ResNet-preact-20, label smoothing (epsilon=0.1) 7.16 400 49m
ResNet-preact-20, mixup (alpha=1) 6.66 400 51m
ResNet-preact-20, RICAP (beta=0.3), w/ random crop 6.30 400 56m
ResNet-preact-20, RICAP (beta=0.3) 6.19 400 56m
ResNet-preact-20, Dual-Cutout 16 (alpha=0.1) 5.55 400 1h36m

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 1080 Ti was used in these experiments.

Experiments on batch size and learning rate

  • Following experiments are done on CIFAR-10 dataset using GeForce 1080 Ti.
  • Results reported in the table are the test errors at last epochs.

Linear scaling rule for learning rate

Model batch size initial lr lr schedule # of Epochs Test Error (1 run) Training Time
ResNet-preact-20 4096 3.2 cosine 200 10.57 22m
ResNet-preact-20 2048 1.6 cosine 200 8.87 21m
ResNet-preact-20 1024 0.8 cosine 200 8.40 21m
ResNet-preact-20 512 0.4 cosine 200 8.22 20m
ResNet-preact-20 256 0.2 cosine 200 8.61 22m
ResNet-preact-20 128 0.1 cosine 200 8.09 24m
ResNet-preact-20 64 0.05 cosine 200 8.22 28m
ResNet-preact-20 32 0.025 cosine 200 8.00 43m
ResNet-preact-20 16 0.0125 cosine 200 7.75 1h17m
ResNet-preact-20 8 0.006125 cosine 200 7.70 2h32m
Model batch size initial lr lr schedule # of Epochs Test Error (1 run) Training Time
ResNet-preact-20 4096 3.2 multistep 200 28.97 22m
ResNet-preact-20 2048 1.6 multistep 200 9.07 21m
ResNet-preact-20 1024 0.8 multistep 200 8.62 21m
ResNet-preact-20 512 0.4 multistep 200 8.23 20m
ResNet-preact-20 256 0.2 multistep 200 8.40 21m
ResNet-preact-20 128 0.1 multistep 200 8.28 24m
ResNet-preact-20 64 0.05 multistep 200 8.13 28m
ResNet-preact-20 32 0.025 multistep 200 7.58 43m
ResNet-preact-20 16 0.0125 multistep 200 7.93 1h18m
ResNet-preact-20 8 0.006125 multistep 200 8.31 2h34m

Linear scaling + longer training

Model batch size initial lr lr schedule # of Epochs Test Error (1 run) Training Time
ResNet-preact-20 4096 3.2 cosine 400 8.97 44m
ResNet-preact-20 2048 1.6 cosine 400 7.85 43m
ResNet-preact-20 1024 0.8 cosine 400 7.20 42m
ResNet-preact-20 512 0.4 cosine 400 7.83 40m
ResNet-preact-20 256 0.2 cosine 400 7.65 42m
ResNet-preact-20 128 0.1 cosine 400 7.09 47m
ResNet-preact-20 64 0.05 cosine 400 7.17 44m
ResNet-preact-20 32 0.025 cosine 400 7.24 2h11m
ResNet-preact-20 16 0.0125 cosine 400 7.26 4h10m
ResNet-preact-20 8 0.006125 cosine 400 7.02 7h53m
Model batch size initial lr lr schedule # of Epochs Test Error (1 run) Training Time
ResNet-preact-20 4096 3.2 cosine 800 8.14 1h29m
ResNet-preact-20 2048 1.6 cosine 800 7.74 1h23m
ResNet-preact-20 1024 0.8 cosine 800 7.15 1h31m
ResNet-preact-20 512 0.4 cosine 800 7.27 1h25m
ResNet-preact-20 256 0.2 cosine 800 7.22 1h26m
ResNet-preact-20 128 0.1 cosine 800 6.68 1h35m
ResNet-preact-20 64 0.05 cosine 800 7.18 2h20m
ResNet-preact-20 32 0.025 cosine 800 7.03 4h16m
ResNet-preact-20 16 0.0125 cosine 800 6.78 8h37m
ResNet-preact-20 8 0.006125 cosine 800 6.89 16h47m

Effect of initial learning rate

Model batch size initial lr lr schedule # of Epochs Test Error (1 run) Training Time
ResNet-preact-20 4096 3.2 cosine 200 10.57 22m
ResNet-preact-20 4096 1.6 cosine 200 10.32 22m
ResNet-preact-20 4096 0.8 cosine 200 10.71 22m
Model batch size initial lr lr schedule # of Epochs Test Error (1 run) Training Time
ResNet-preact-20 2048 3.2 cosine 200 11.34 21m
ResNet-preact-20 2048 2.4 cosine 200 8.69 21m
ResNet-preact-20 2048 2.0 cosine 200 8.81 21m
ResNet-preact-20 2048 1.6 cosine 200 8.73 22m
ResNet-preact-20 2048 0.8 cosine 200 9.62 21m
Model batch size initial lr lr schedule # of Epochs Test Error (1 run) Training Time
ResNet-preact-20 1024 3.2 cosine 200 9.12 21m
ResNet-preact-20 1024 2.4 cosine 200 8.42 22m
ResNet-preact-20 1024 2.0 cosine 200 8.38 22m
ResNet-preact-20 1024 1.6 cosine 200 8.07 22m
ResNet-preact-20 1024 1.2 cosine 200 8.25 21m
ResNet-preact-20 1024 0.8 cosine 200 8.08 22m
ResNet-preact-20 1024 0.4 cosine 200 8.49 22m
Model batch size initial lr lr schedule # of Epochs Test Error (1 run) Training Time
ResNet-preact-20 512 3.2 cosine 200 8.51 21m
ResNet-preact-20 512 1.6 cosine 200 7.73 20m
ResNet-preact-20 512 0.8 cosine 200 7.73 21m
ResNet-preact-20 512 0.4 cosine 200 8.22 20m
Model batch size initial lr lr schedule # of Epochs Test Error (1 run) Training Time
ResNet-preact-20 256 3.2 cosine 200 9.64 22m
ResNet-preact-20 256 1.6 cosine 200 8.32 22m
ResNet-preact-20 256 0.8 cosine 200 7.45 21m
ResNet-preact-20 256 0.4 cosine 200 7.68 22m
ResNet-preact-20 256 0.2 cosine 200 8.61 22m
Model batch size initial lr lr schedule # of Epochs Test Error (1 run) Training Time
ResNet-preact-20 128 1.6 cosine 200 9.03 24m
ResNet-preact-20 128 0.8 cosine 200 7.54 24m
ResNet-preact-20 128 0.4 cosine 200 7.28 24m
ResNet-preact-20 128 0.2 cosine 200 7.96 24m
ResNet-preact-20 128 0.1 cosine 200 8.09 24m
ResNet-preact-20 128 0.05 cosine 200 8.81 24m
ResNet-preact-20 128 0.025 cosine 200 10.07 24m
Model batch size initial lr lr schedule # of Epochs Test Error (1 run) Training Time
ResNet-preact-20 64 0.4 cosine 200 7.42 35m
ResNet-preact-20 64 0.2 cosine 200 7.52 36m
ResNet-preact-20 64 0.1 cosine 200 7.78 37m
ResNet-preact-20 64 0.05 cosine 200 8.22 28m
Model batch size initial lr lr schedule # of Epochs Test Error (1 run) Training Time
ResNet-preact-20 32 0.2 cosine 200 7.64 1h05m
ResNet-preact-20 32 0.1 cosine 200 7.25 1h08m
ResNet-preact-20 32 0.05 cosine 200 7.45 1h07m
ResNet-preact-20 32 0.025 cosine 200 8.00 43m

Good learning rate + longer training

Model batch size initial lr lr schedule # of Epochs Test Error (1 run) Training Time
ResNet-preact-20 4096 1.6 cosine 200 10.32 22m
ResNet-preact-20 2048 1.6 cosine 200 8.73 22m
ResNet-preact-20 1024 1.6 cosine 200 8.07 22m
ResNet-preact-20 1024 0.8 cosine 200 8.08 22m
ResNet-preact-20 512 1.6 cosine 200 7.73 20m
ResNet-preact-20 512 0.8 cosine 200 7.73 21m
ResNet-preact-20 256 0.8 cosine 200 7.45 21m
ResNet-preact-20 128 0.4 cosine 200 7.28 24m
ResNet-preact-20 128 0.2 cosine 200 7.96 24m
ResNet-preact-20 128 0.1 cosine 200 8.09 24m
Model batch size initial lr lr schedule # of Epochs Test Error (1 run) Training Time
ResNet-preact-20 4096 1.6 cosine 800 8.36 1h33m
ResNet-preact-20 2048 1.6 cosine 800 7.53 1h27m
ResNet-preact-20 1024 1.6 cosine 800 7.30 1h30m
ResNet-preact-20 1024 0.8 cosine 800 7.42 1h30m
ResNet-preact-20 512 1.6 cosine 800 6.69 1h26m
ResNet-preact-20 512 0.8 cosine 800 6.77 1h26m
ResNet-preact-20 256 0.8 cosine 800 6.84 1h28m
ResNet-preact-20 128 0.4 cosine 800 6.86 1h35m
ResNet-preact-20 128 0.2 cosine 800 7.05 1h38m
ResNet-preact-20 128 0.1 cosine 800 6.68 1h35m
Model batch size initial lr lr schedule # of Epochs Test Error (1 run) Training Time
ResNet-preact-20 4096 1.6 cosine 1600 8.25 3h10m
ResNet-preact-20 2048 1.6 cosine 1600 7.34 2h50m
ResNet-preact-20 1024 1.6 cosine 1600 6.94 2h52m
ResNet-preact-20 512 1.6 cosine 1600 6.99 2h44m
ResNet-preact-20 256 0.8 cosine 1600 6.95 2h50m
ResNet-preact-20 128 0.4 cosine 1600 6.64 3h09m
Model batch size initial lr lr schedule # of Epochs Test Error (1 run) Training Time
ResNet-preact-20 4096 1.6 cosine 3200 9.52 6h15m
ResNet-preact-20 2048 1.6 cosine 3200 6.92 5h42m
ResNet-preact-20 1024 1.6 cosine 3200 6.96 5h43m
Model batch size initial lr lr schedule # of Epochs Test Error (1 run) Training Time
ResNet-preact-20 2048 1.6 cosine 6400 7.45 11h44m

LARS

  • In the original papers (1708.03888, 1801.03137), they used polynomial decay learning rate scheduling, but cosine annealing is used in these experiments.
  • In this implementation, LARS coefficient is not used, so learning rate should be adjusted accordingly.
python train.py --config configs/cifar/resnet_preact.yaml \
    model.resnet_preact.depth 20 \
    train.optimizer lars \
    train.base_lr 0.02 \
    train.batch_size 4096 \
    scheduler.type cosine \
    train.output_dir experiments/resnet_preact_lars/exp00

Model optimizer batch size initial lr lr schedule # of Epochs Test Error (median of 3 runs) Training Time
ResNet-preact-20 SGD 4096 3.2 cosine 200 10.57 (1 run) 22m
ResNet-preact-20 SGD 4096 1.6 cosine 200 10.20 22m
ResNet-preact-20 SGD 4096 0.8 cosine 200 10.71 (1 run) 22m
ResNet-preact-20 LARS 4096 0.04 cosine 200 9.58 22m
ResNet-preact-20 LARS 4096 0.03 cosine 200 8.46 22m
ResNet-preact-20 LARS 4096 0.02 cosine 200 8.21 22m
ResNet-preact-20 LARS 4096 0.015 cosine 200 8.47 22m
ResNet-preact-20 LARS 4096 0.01 cosine 200 9.33 22m
ResNet-preact-20 LARS 4096 0.005 cosine 200 14.31 22m
Model optimizer batch size initial lr lr schedule # of Epochs Test Error (median of 3 runs) Training Time
ResNet-preact-20 SGD 2048 3.2 cosine 200 11.34 (1 run) 21m
ResNet-preact-20 SGD 2048 2.4 cosine 200 8.69 (1 run) 21m
ResNet-preact-20 SGD 2048 2.0 cosine 200 8.81 (1 run) 21m
ResNet-preact-20 SGD 2048 1.6 cosine 200 8.73 (1 run) 22m
ResNet-preact-20 SGD 2048 0.8 cosine 200 9.62 (1 run) 21m
ResNet-preact-20 LARS 2048 0.04 cosine 200 11.58 21m
ResNet-preact-20 LARS 2048 0.02 cosine 200 8.05 22m
ResNet-preact-20 LARS 2048 0.01 cosine 200 8.07 22m
ResNet-preact-20 LARS 2048 0.005 cosine 200 9.65 22m
Model optimizer batch size initial lr lr schedule # of Epochs Test Error (median of 3 runs) Training Time
ResNet-preact-20 SGD 1024 3.2 cosine 200 9.12 (1 run) 21m
ResNet-preact-20 SGD 1024 2.4 cosine 200 8.42 (1 run) 22m
ResNet-preact-20 SGD 1024 2.0 cosine 200 8.38 (1 run) 22m
ResNet-preact-20 SGD 1024 1.6 cosine 200 8.07 (1 run) 22m
ResNet-preact-20 SGD 1024 1.2 cosine 200 8.25 (1 run) 21m
ResNet-preact-20 SGD 1024 0.8 cosine 200 8.08 (1 run) 22m
ResNet-preact-20 SGD 1024 0.4 cosine 200 8.49 (1 run) 22m
ResNet-preact-20 LARS 1024 0.02 cosine 200 9.30 22m
ResNet-preact-20 LARS 1024 0.01 cosine 200 7.68 22m
ResNet-preact-20 LARS 1024 0.005 cosine 200 8.88 23m
Model optimizer batch size initial lr lr schedule # of Epochs Test Error (median of 3 runs) Training Time
ResNet-preact-20 SGD 512 3.2 cosine 200 8.51 (1 run) 21m
ResNet-preact-20 SGD 512 1.6 cosine 200 7.73 (1 run) 20m
ResNet-preact-20 SGD 512 0.8 cosine 200 7.73 (1 run) 21m
ResNet-preact-20 SGD 512 0.4 cosine 200 8.22 (1 run) 20m
ResNet-preact-20 LARS 512 0.015 cosine 200 9.84 23m
ResNet-preact-20 LARS 512 0.01 cosine 200 8.05 23m
ResNet-preact-20 LARS 512 0.0075 cosine 200 7.58 23m
ResNet-preact-20 LARS 512 0.005 cosine 200 7.96 23m
ResNet-preact-20 LARS 512 0.0025 cosine 200 8.83 23m
Model optimizer batch size initial lr lr schedule # of Epochs Test Error (median of 3 runs) Training Time
ResNet-preact-20 SGD 256 3.2 cosine 200 9.64 (1 run) 22m
ResNet-preact-20 SGD 256 1.6 cosine 200 8.32 (1 run) 22m
ResNet-preact-20 SGD 256 0.8 cosine 200 7.45 (1 run) 21m
ResNet-preact-20 SGD 256 0.4 cosine 200 7.68 (1 run) 22m
ResNet-preact-20 SGD 256 0.2 cosine 200 8.61 (1 run) 22m
ResNet-preact-20 LARS 256 0.01 cosine 200 8.95 27m
ResNet-preact-20 LARS 256 0.005 cosine 200 7.75 28m
ResNet-preact-20 LARS 256 0.0025 cosine 200 8.21 28m
Model optimizer batch size initial lr lr schedule # of Epochs Test Error (median of 3 runs) Training Time
ResNet-preact-20 SGD 128 1.6 cosine 200 9.03 (1 run) 24m
ResNet-preact-20 SGD 128 0.8 cosine 200 7.54 (1 run) 24m
ResNet-preact-20 SGD 128 0.4 cosine 200 7.28 (1 run) 24m
ResNet-preact-20 SGD 128 0.2 cosine 200 7.96 (1 run) 24m
ResNet-preact-20 LARS 128 0.005 cosine 200 7.96 37m
ResNet-preact-20 LARS 128 0.0025 cosine 200 7.98 37m
ResNet-preact-20 LARS 128 0.00125 cosine 200 9.21 37m
Model optimizer batch size initial lr lr schedule # of Epochs Test Error (median of 3 runs) Training Time
ResNet-preact-20 SGD 4096 1.6 cosine 200 10.20 22m
ResNet-preact-20 SGD 4096 1.6 cosine 800 8.36 (1 run) 1h33m
ResNet-preact-20 SGD 4096 1.6 cosine 1600 8.25 (1 run) 3h10m
ResNet-preact-20 LARS 4096 0.02 cosine 200 8.21 22m
ResNet-preact-20 LARS 4096 0.02 cosine 400 7.53 44m
ResNet-preact-20 LARS 4096 0.02 cosine 800 7.48 1h29m
ResNet-preact-20 LARS 4096 0.02 cosine 1600 7.37 (1 run) 2h58m

Ghost BN

python train.py --config configs/cifar/resnet_preact.yaml \
    model.resnet_preact.depth 20 \
    train.base_lr 1.5 \
    train.batch_size 4096 \
    train.subdivision 32 \
    scheduler.type cosine \
    train.output_dir experiments/resnet_preact_ghost_batch/exp00
Model batch size ghost batch size initial lr lr schedule # of Epochs Test Error (1 run) Training Time
ResNet-preact-20 8192 N/A 1.6 cosine 200 12.35 25m*
ResNet-preact-20 4096 N/A 1.6 cosine 200 10.32 22m
ResNet-preact-20 2048 N/A 1.6 cosine 200 8.73 22m
ResNet-preact-20 1024 N/A 1.6 cosine 200 8.07 22m
ResNet-preact-20 128 N/A 0.4 cosine 200 7.28 24m
Model batch size ghost batch size initial lr lr schedule # of Epochs Test Error (1 run) Training Time
ResNet-preact-20 8192 128 1.6 cosine 200 11.51 27m
ResNet-preact-20 4096 128 1.6 cosine 200 9.73 25m
ResNet-preact-20 2048 128 1.6 cosine 200 8.77 24m
ResNet-preact-20 1024 128 1.6 cosine 200 7.82 22m
Model batch size ghost batch size initial lr lr schedule # of Epochs Test Error (1 run) Training Time
ResNet-preact-20 8192 N/A 1.6 cosine 1600
ResNet-preact-20 4096 N/A 1.6 cosine 1600 8.25 3h10m
ResNet-preact-20 2048 N/A 1.6 cosine 1600 7.34 2h50m
ResNet-preact-20 1024 N/A 1.6 cosine 1600 6.94 2h52m
Model batch size ghost batch size initial lr lr schedule # of Epochs Test Error (1 run) Training Time
ResNet-preact-20 8192 128 1.6 cosine 1600 11.83 3h37m
ResNet-preact-20 4096 128 1.6 cosine 1600 8.95 3h15m
ResNet-preact-20 2048 128 1.6 cosine 1600 7.23 3h05m
ResNet-preact-20 1024 128 1.6 cosine 1600 7.08 2h59m

No weight decay on BN

python train.py --config configs/cifar/resnet_preact.yaml \
    model.resnet_preact.depth 20 \
    train.base_lr 1.6 \
    train.batch_size 4096 \
    train.no_weight_decay_on_bn True \
    train.weight_decay 5e-4 \
    scheduler.type cosine \
    train.output_dir experiments/resnet_preact_no_weight_decay_on_bn/exp00

Model weight decay on BN weight decay batch size initial lr lr schedule # of Epochs Test Error (median of 3 runs) Training Time
ResNet-preact-20 yes 5e-4 4096 1.6 cosine 200 10.81 22m
ResNet-preact-20 yes 4e-4 4096 1.6 cosine 200 10.88 22m
ResNet-preact-20 yes 3e-4 4096 1.6 cosine 200 10.96 22m
ResNet-preact-20 yes 2e-4 4096 1.6 cosine 200 9.30 22m
ResNet-preact-20 yes 1e-4 4096 1.6 cosine 200 10.20 22m
ResNet-preact-20 no 5e-4 4096 1.6 cosine 200 8.78 22m
ResNet-preact-20 no 4e-4 4096 1.6 cosine 200 9.83 22m
ResNet-preact-20 no 3e-4 4096 1.6 cosine 200 9.90 22m
ResNet-preact-20 no 2e-4 4096 1.6 cosine 200 9.64 22m
ResNet-preact-20 no 1e-4 4096 1.6 cosine 200 10.38 22m
Model weight decay on BN weight decay batch size initial lr lr schedule # of Epochs Test Error (median of 3 runs) Training Time
ResNet-preact-20 yes 5e-4 2048 1.6 cosine 200 8.46 20m
ResNet-preact-20 yes 4e-4 2048 1.6 cosine 200 8.35 20m
ResNet-preact-20 yes 3e-4 2048 1.6 cosine 200 7.76 20m
ResNet-preact-20 yes 2e-4 2048 1.6 cosine 200 8.09 20m
ResNet-preact-20 yes 1e-4 2048 1.6 cosine 200 8.83 20m
ResNet-preact-20 no 5e-4 2048 1.6 cosine 200 8.49 20m
ResNet-preact-20 no 4e-4 2048 1.6 cosine 200 7.98 20m
ResNet-preact-20 no 3e-4 2048 1.6 cosine 200 8.26 20m
ResNet-preact-20 no 2e-4 2048 1.6 cosine 200 8.47 20m
ResNet-preact-20 no 1e-4 2048 1.6 cosine 200 9.27 20m
Model weight decay on BN weight decay batch size initial lr lr schedule # of Epochs Test Error (median of 3 runs) Training Time
ResNet-preact-20 yes 5e-4 1024 1.6 cosine 200 8.45 21m
ResNet-preact-20 yes 4e-4 1024 1.6 cosine 200 7.91 21m
ResNet-preact-20 yes 3e-4 1024 1.6 cosine 200 7.81 21m
ResNet-preact-20 yes 2e-4 1024 1.6 cosine 200 7.69 21m
ResNet-preact-20 yes 1e-4 1024 1.6 cosine 200 8.26 21m
ResNet-preact-20 no 5e-4 1024 1.6 cosine 200 8.08 21m
ResNet-preact-20 no 4e-4 1024 1.6 cosine 200 7.73 21m
ResNet-preact-20 no 3e-4 1024 1.6 cosine 200 7.92 21m
ResNet-preact-20 no 2e-4 1024 1.6 cosine 200 7.93 21m
ResNet-preact-20 no 1e-4 1024 1.6 cosine 200 8.53 21m

Experiments on half-precision, and mixed-precision

  • Following experiments need NVIDIA Apex.
  • Following experiments are done on CIFAR-10 dataset using GeForce 1080 Ti, which doesn't have Tensor Cores.
  • Results reported in the table are the test errors at last epochs.

FP16 training

python train.py --config configs/cifar/resnet_preact.yaml \
    model.resnet_preact.depth 20 \
    train.base_lr 1.6 \
    train.batch_size 4096 \
    train.precision O3 \
    scheduler.type cosine \
    train.output_dir experiments/resnet_preact_fp16/exp00

Mixed-precision training

python train.py --config configs/cifar/resnet_preact.yaml \
    model.resnet_preact.depth 20 \
    train.base_lr 1.6 \
    train.batch_size 4096 \
    train.precision O1 \
    scheduler.type cosine \
    train.output_dir experiments/resnet_preact_mixed_precision/exp00

Results

Model precision batch size initial lr lr schedule # of Epochs Test Error (1 run) Training Time
ResNet-preact-20 FP32 8192 1.6 cosine 200
ResNet-preact-20 FP32 4096 1.6 cosine 200 10.32 22m
ResNet-preact-20 FP32 2048 1.6 cosine 200 8.73 22m
ResNet-preact-20 FP32 1024 1.6 cosine 200 8.07 22m
ResNet-preact-20 FP32 512 0.8 cosine 200 7.73 21m
ResNet-preact-20 FP32 256 0.8 cosine 200 7.45 21m
ResNet-preact-20 FP32 128 0.4 cosine 200 7.28 24m
Model precision batch size initial lr lr schedule # of Epochs Test Error (1 run) Training Time
ResNet-preact-20 FP16 8192 1.6 cosine 200 48.52 33m
ResNet-preact-20 FP16 4096 1.6 cosine 200 49.84 28m
ResNet-preact-20 FP16 2048 1.6 cosine 200 75.63 27m
ResNet-preact-20 FP16 1024 1.6 cosine 200 19.09 27m
ResNet-preact-20 FP16 512 0.8 cosine 200 7.89 26m
ResNet-preact-20 FP16 256 0.8 cosine 200 7.40 28m
ResNet-preact-20 FP16 128 0.4 cosine 200 7.59 32m
Model precision batch size initial lr lr schedule # of Epochs Test Error (1 run) Training Time
ResNet-preact-20 mixed 8192 1.6 cosine 200 11.78 28m
ResNet-preact-20 mixed 4096 1.6 cosine 200 10.48 27m
ResNet-preact-20 mixed 2048 1.6 cosine 200 8.98 26m
ResNet-preact-20 mixed 1024 1.6 cosine 200 8.05 26m
ResNet-preact-20 mixed 512 0.8 cosine 200 7.81 28m
ResNet-preact-20 mixed 256 0.8 cosine 200 7.58 32m
ResNet-preact-20 mixed 128 0.4 cosine 200 7.37 41m

Results using Tesla V100

Model precision batch size initial lr lr schedule # of Epochs Test Error (1 run) Training Time
ResNet-preact-20 FP32 8192 1.6 cosine 200 12.35 25m
ResNet-preact-20 FP32 4096 1.6 cosine 200 9.88 19m
ResNet-preact-20 FP32 2048 1.6 cosine 200 8.87 17m
ResNet-preact-20 FP32 1024 1.6 cosine 200 8.45 18m
ResNet-preact-20 mixed 8192 1.6 cosine 200 11.92 25m
ResNet-preact-20 mixed 4096 1.6 cosine 200 10.16 19m
ResNet-preact-20 mixed 2048 1.6 cosine 200 9.10 17m
ResNet-preact-20 mixed 1024 1.6 cosine 200 7.84 16m

References

Model architecture

  • 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
  • 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
  • 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
  • Huang, Gao, Zhuang Liu, Geoff Pleiss, Laurens van der Maaten, and Kilian Q. Weinberger. "Convolutional Networks with Dense Connectivity." IEEE transactions on pattern analysis and machine intelligence (2019). arXiv:2001.02394

Regularization, data augmentation

  • Szegedy, Christian, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. "Rethinking the Inception Architecture for Computer Vision." The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. link, arXiv:1512.00567
  • 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
  • Abu-El-Haija, Sami. "Proportionate Gradient Updates with PercentDelta." arXiv preprint arXiv:1708.07227 (2017). arXiv:1708.07227
  • 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
  • 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
  • Kawaguchi, Kenji, Yoshua Bengio, Vikas Verma, and Leslie Pack Kaelbling. "Towards Understanding Generalization via Analytical Learning Theory." arXiv preprint arXiv:1802.07426 (2018). arXiv:1802.07426, PyTorch implementation
  • 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
  • Yun, Sangdoo, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo. "CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features." arXiv preprint arXiv:1905.04899 (2019). arXiv:1905.04899

Large batch

  • Keskar, Nitish Shirish, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang. "On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima." In International Conference on Learning Representations (ICLR), 2017. link, arXiv:1609.04836
  • Hoffer, Elad, Itay Hubara, and Daniel Soudry. "Train longer, generalize better: closing the generalization gap in large batch training of neural networks." In Advances in Neural Information Processing Systems (NIPS), 2017. link, arXiv:1705.08741, PyTorch implementation
  • Goyal, Priya, Piotr Dollar, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He. "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour." arXiv preprint arXiv:1706.02677 (2017). arXiv:1706.02677
  • You, Yang, Igor Gitman, and Boris Ginsburg. "Large Batch Training of Convolutional Networks." arXiv preprint arXiv:1708.03888 (2017). arXiv:1708.03888
  • You, Yang, Zhao Zhang, Cho-Jui Hsieh, James Demmel, and Kurt Keutzer. "ImageNet Training in Minutes." arXiv preprint arXiv:1709.05011 (2017). arXiv:1709.05011
  • Smith, Samuel L., Pieter-Jan Kindermans, Chris Ying, and Quoc V. Le. "Don't Decay the Learning Rate, Increase the Batch Size." In International Conference on Learning Representations (ICLR), 2018. link, arXiv:1711.00489
  • Gitman, Igor, Deepak Dilipkumar, and Ben Parr. "Convergence Analysis of Gradient Descent Algorithms with Proportional Updates." arXiv preprint arXiv:1801.03137 (2018). arXiv:1801.03137 TensorFlow implementation
  • Jia, Xianyan, Shutao Song, Wei He, Yangzihao Wang, Haidong Rong, Feihu Zhou, Liqiang Xie, Zhenyu Guo, Yuanzhou Yang, Liwei Yu, Tiegang Chen, Guangxiao Hu, Shaohuai Shi, and Xiaowen Chu. "Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes." arXiv preprint arXiv:1807.11205 (2018). arXiv:1807.11205
  • Shallue, Christopher J., Jaehoon Lee, Joseph Antognini, Jascha Sohl-Dickstein, Roy Frostig, and George E. Dahl. "Measuring the Effects of Data Parallelism on Neural Network Training." arXiv preprint arXiv:1811.03600 (2018). arXiv:1811.03600
  • Ying, Chris, Sameer Kumar, Dehao Chen, Tao Wang, and Youlong Cheng. "Image Classification at Supercomputer Scale." In Advances in Neural Information Processing Systems (NeurIPS) Workshop, 2018. link, arXiv:1811.06992

Others

  • 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
  • Micikevicius, Paulius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, and Hao Wu. "Mixed Precision Training." In International Conference on Learning Representations (ICLR), 2018. link, arXiv:1710.03740
  • 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
  • He, Tong, Zhi Zhang, Hang Zhang, Zhongyue Zhang, Junyuan Xie, and Mu Li. "Bag of Tricks for Image Classification with Convolutional Neural Networks." arXiv preprint arXiv:1812.01187 (2018). arXiv:1812.01187

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