BIGBALLON / CIFAR-ZOO

PyTorch implementation of CNNs for CIFAR benchmark

Awesome CIFAR Zoo

Status: Archive ( Final test with PyTorch 1.7 and no longer maintained, I would recommend you to use pycls powered by FAIR, which is a simple and flexible codebase for image classification )

This repository contains the pytorch code for multiple CNN architectures and improve methods based on the following papers, hope the implementation and results will helpful for your research!!

• Architecure
  • (lenet) LeNet-5, convolutional neural networks
  • (alexnet) ImageNet Classification with Deep Convolutional Neural Networks
  • (vgg) Very Deep Convolutional Networks for Large-Scale Image Recognition
  • (resnet) Deep Residual Learning for Image Recognition
  • (preresnet) Identity Mappings in Deep Residual Networks
  • (resnext) Aggregated Residual Transformations for Deep Neural Networks
  • (densenet) Densely Connected Convolutional Networks
  • (senet) Squeeze-and-Excitation Networks
  • (bam) BAM: Bottleneck Attention Module
  • (cbam) CBAM: Convolutional Block Attention Module
  • (genet) Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks
  • (sknet) SKNet: Selective Kernel Networks
• Regularization
  • (shake-shake) Shake-Shake regularization
  • (cutout) Improved Regularization of Convolutional Neural Networks with Cutout
  • (mixup) mixup: Beyond Empirical Risk Minimization
• Learning Rate Scheduler
  • (cos_lr) SGDR: Stochastic Gradient Descent with Warm Restarts
  • (htd_lr) Stochastic Gradient Descent with Hyperbolic-Tangent Decay on Classification

Requirements and Usage

Requirements

• Python (>=3.6)
• PyTorch (>=1.1.0)
• Tensorboard(>=1.4.0) (for visualization)
• Other dependencies (pyyaml, easydict)

pip install -r requirements.txt

Usage

simply run the cmd for the training:

## 1 GPU for lenet
CUDA_VISIBLE_DEVICES=0 python -u train.py --work-path ./experiments/cifar10/lenet

## resume from ckpt
CUDA_VISIBLE_DEVICES=0 python -u train.py --work-path ./experiments/cifar10/lenet --resume

## 2 GPUs for resnet1202
CUDA_VISIBLE_DEVICES=0,1 python -u train.py --work-path ./experiments/cifar10/preresnet1202

## 4 GPUs for densenet190bc
CUDA_VISIBLE_DEVICES=0,1,2,3 python -u train.py --work-path ./experiments/cifar10/densenet190bc

## 1 GPU for vgg19 inference
CUDA_VISIBLE_DEVICES=0 python -u eval.py --work-path ./experiments/cifar10/vgg19

We use yaml file config.yaml to save the parameters, check any files in ./experimets for more details.
You can see the training curve via tensorboard, tensorboard --logdir path-to-event --port your-port.
The training log will be dumped via logging, check log.txt in your work path.

Results on CIFAR

Vanilla architectures

architecture	params	batch size	epoch	C10 test acc (%)	C100 test acc (%)
Lecun	62K	128	250	67.46	34.10
alexnet	2.4M	128	250	75.56	38.67
vgg19	20M	128	250	93.00	72.07
preresnet20	0.27M	128	250	91.88	67.03
preresnet110	1.7M	128	250	94.24	72.96
preresnet1202	19.4M	128	250	94.74	75.28
densenet100bc	0.76M	64	300	95.08	77.55
densenet190bc	25.6M	64	300	96.11	82.59
resnext29_16x64d	68.1M	128	300	95.94	83.18
se_resnext29_16x64d	68.6M	128	300	96.15	83.65
cbam_resnext29_16x64d	68.7M	128	300	96.27	83.62
ge_resnext29_16x64d	70.0M	128	300	96.21	83.57

With additional regularization

PS: the default data augmentation methods are RandomCrop + RandomHorizontalFlip + Normalize,
and the √ means which additional method be used. 🍰

architecture	epoch	cutout	mixup	C10 test acc (%)
preresnet20	250			91.88
preresnet20	250	√		92.57
preresnet20	250		√	92.71
preresnet20	250	√	√	92.66
preresnet110	250			94.24
preresnet110	250	√		94.67
preresnet110	250		√	94.94
preresnet110	250	√	√	95.66
se_resnext29_16x64d	300			96.15
se_resnext29_16x64d	300	√		96.60
se_resnext29_16x64d	300		√	96.86
se_resnext29_16x64d	300	√	√	97.03
cbam_resnext29_16x64d	300	√	√	97.16
ge_resnext29_16x64d	300	√	√	97.19
--	--	--	--	--
shake_resnet26_2x64d	1800			96.94
shake_resnet26_2x64d	1800	√		97.20
shake_resnet26_2x64d	1800		√	97.42
shake_resnet26_2x64d	1800	√	√	97.71

PS: shake_resnet26_2x64d achieved 97.71% test accuracy with cutout and mixup!!
It's cool, right?

With different LR scheduler

architecture	epoch	step decay	cosine	htd(-6,3)	cutout	mixup	C10 test acc (%)
preresnet20	250	√					91.88
preresnet20	250		√				92.13
preresnet20	250			√			92.44
preresnet20	250			√	√	√	93.30
preresnet110	250	√					94.24
preresnet110	250		√				94.48
preresnet110	250			√			94.82
preresnet110	250			√	√	√	95.88

Acknowledgments

Provided codes were adapted from

• kuangliu/pytorch-cifar
• bearpaw/pytorch-classification
• timgaripov/swa
• xgastaldi/shake-shake
• uoguelph-mlrg/Cutout
• facebookresearch/mixup-cifar10
• BIGBALLON/cifar-10-cnn
• BayesWatch/pytorch-GENet
• Jongchan/attention-module
• pppLang/SKNet

Feel free to contact me if you have any suggestions or questions, issues are welcome,
create a PR if you find any bugs or you want to contribute. 😊

Citation

@misc{bigballon2019cifarzoo,
  author = {Wei Li},
  title = {CIFAR-ZOO: PyTorch implementation of CNNs for CIFAR dataset},
  howpublished = {\url{https://github.com/BIGBALLON/CIFAR-ZOO}},
  year = {2019}
}