PyTorch Image Classification

Following papers are implemented using PyTorch.

ResNet (1512.03385)
ResNet-preact (1603.05027)
WRN (1605.07146)
DenseNet (1608.06993, 2001.02394)
PyramidNet (1610.02915)
ResNeXt (1611.05431)
shake-shake (1705.07485)
LARS (1708.03888, 1801.03137)
Cutout (1708.04552)
Random Erasing (1708.04896)
SENet (1709.01507)
Mixup (1710.09412)
Dual-Cutout (1802.07426)
RICAP (1811.09030)
CutMix (1905.04899)

Requirements

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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figures		figures
pytorch_image_classification		pytorch_image_classification
scripts		scripts
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
evaluate.py		evaluate.py
requirements.txt		requirements.txt
train.py		train.py

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PyTorch Image Classification

Requirements

Usage

Results on CIFAR-10

Results using almost same settings as papers

Notes

VGG-like

ResNet

ResNet-preact

WRN

DenseNet

PyramidNet

ResNeXt

shake-shake

Results

Note

Results using multi-GPU

Note

Using 1 GPU

Using 2 GPUs

Using 4 GPUs

Using 8 GPUs

Results on FashionMNIST

Note

Results on MNIST

Note

Results on Kuzushiji-MNIST

Note

Experiments

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

Note

Results

preactivate shortcut after downsampling

w/ 1st ReLU, w/o last BN

w/o 1st ReLU, w/o last BN

w/ 1st ReLU, w/ last BN

w/o 1st ReLU, w/ last BN

w/o 1st ReLU, w/ last BN, preactivate shortcut after downsampling

w/o 1st ReLU, w/ last BN, cosine annealing

w/o 1st ReLU, w/ last BN, Cutout

w/o 1st ReLU, w/ last BN, RandomErasing

w/o 1st ReLU, w/ last BN, Mixup

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

Results on CIFAR-10

Note

Experiments on batch size and learning rate

Linear scaling rule for learning rate

Linear scaling + longer training

Effect of initial learning rate

Good learning rate + longer training

LARS

Ghost BN

No weight decay on BN

Experiments on half-precision, and mixed-precision

FP16 training

Mixed-precision training

Results

Results using Tesla V100

References

Model architecture

Regularization, data augmentation

Large batch

Others

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