weiaicunzai / pytorch-cifar100

Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet)

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Pytorch-cifar100

practice on cifar100 using pytorch

Requirements

This is my experiment eviroument

  • python3.6
  • pytorch1.6.0+cu101
  • tensorboard 2.2.2(optional)

Usage

1. enter directory

$ cd pytorch-cifar100

2. dataset

I will use cifar100 dataset from torchvision since it's more convenient, but I also kept the sample code for writing your own dataset module in dataset folder, as an example for people don't know how to write it.

3. run tensorbard(optional)

Install tensorboard

$ pip install tensorboard
$ mkdir runs
Run tensorboard
$ tensorboard --logdir='runs' --port=6006 --host='localhost'

4. train the model

You need to specify the net you want to train using arg -net

# use gpu to train vgg16
$ python train.py -net vgg16 -gpu

sometimes, you might want to use warmup training by set -warm to 1 or 2, to prevent network diverge during early training phase.

The supported net args are:

squeezenet
mobilenet
mobilenetv2
shufflenet
shufflenetv2
vgg11
vgg13
vgg16
vgg19
densenet121
densenet161
densenet201
googlenet
inceptionv3
inceptionv4
inceptionresnetv2
xception
resnet18
resnet34
resnet50
resnet101
resnet152
preactresnet18
preactresnet34
preactresnet50
preactresnet101
preactresnet152
resnext50
resnext101
resnext152
attention56
attention92
seresnet18
seresnet34
seresnet50
seresnet101
seresnet152
nasnet
wideresnet
stochasticdepth18
stochasticdepth34
stochasticdepth50
stochasticdepth101

Normally, the weights file with the best accuracy would be written to the disk with name suffix 'best'(default in checkpoint folder).

5. test the model

Test the model using test.py

$ python test.py -net vgg16 -weights path_to_vgg16_weights_file

Implementated NetWork

Training Details

I didn't use any training tricks to improve accuray, if you want to learn more about training tricks, please refer to my another repo, contains various common training tricks and their pytorch implementations.

I follow the hyperparameter settings in paper Improved Regularization of Convolutional Neural Networks with Cutout, which is init lr = 0.1 divide by 5 at 60th, 120th, 160th epochs, train for 200 epochs with batchsize 128 and weight decay 5e-4, Nesterov momentum of 0.9. You could also use the hyperparameters from paper Regularizing Neural Networks by Penalizing Confident Output Distributions and Random Erasing Data Augmentation, which is initial lr = 0.1, lr divied by 10 at 150th and 225th epochs, and training for 300 epochs with batchsize 128, this is more commonly used. You could decrese the batchsize to 64 or whatever suits you, if you dont have enough gpu memory.

You can choose whether to use TensorBoard to visualize your training procedure

Results

The result I can get from a certain model, since I use the same hyperparameters to train all the networks, some networks might not get the best result from these hyperparameters, you could try yourself by finetuning the hyperparameters to get better result.

dataset network params top1 err top5 err epoch(lr = 0.1) epoch(lr = 0.02) epoch(lr = 0.004) epoch(lr = 0.0008) total epoch
cifar100 mobilenet 3.3M 34.02 10.56 60 60 40 40 200
cifar100 mobilenetv2 2.36M 31.92 09.02 60 60 40 40 200
cifar100 squeezenet 0.78M 30.59 8.36 60 60 40 40 200
cifar100 shufflenet 1.0M 29.94 8.35 60 60 40 40 200
cifar100 shufflenetv2 1.3M 30.49 8.49 60 60 40 40 200
cifar100 vgg11_bn 28.5M 31.36 11.85 60 60 40 40 200
cifar100 vgg13_bn 28.7M 28.00 9.71 60 60 40 40 200
cifar100 vgg16_bn 34.0M 27.07 8.84 60 60 40 40 200
cifar100 vgg19_bn 39.0M 27.77 8.84 60 60 40 40 200
cifar100 resnet18 11.2M 24.39 6.95 60 60 40 40 200
cifar100 resnet34 21.3M 23.24 6.63 60 60 40 40 200
cifar100 resnet50 23.7M 22.61 6.04 60 60 40 40 200
cifar100 resnet101 42.7M 22.22 5.61 60 60 40 40 200
cifar100 resnet152 58.3M 22.31 5.81 60 60 40 40 200
cifar100 preactresnet18 11.3M 27.08 8.53 60 60 40 40 200
cifar100 preactresnet34 21.5M 24.79 7.68 60 60 40 40 200
cifar100 preactresnet50 23.9M 25.73 8.15 60 60 40 40 200
cifar100 preactresnet101 42.9M 24.84 7.83 60 60 40 40 200
cifar100 preactresnet152 58.6M 22.71 6.62 60 60 40 40 200
cifar100 resnext50 14.8M 22.23 6.00 60 60 40 40 200
cifar100 resnext101 25.3M 22.22 5.99 60 60 40 40 200
cifar100 resnext152 33.3M 22.40 5.58 60 60 40 40 200
cifar100 attention59 55.7M 33.75 12.90 60 60 40 40 200
cifar100 attention92 102.5M 36.52 11.47 60 60 40 40 200
cifar100 densenet121 7.0M 22.99 6.45 60 60 40 40 200
cifar100 densenet161 26M 21.56 6.04 60 60 60 40 200
cifar100 densenet201 18M 21.46 5.9 60 60 40 40 200
cifar100 googlenet 6.2M 21.97 5.94 60 60 40 40 200
cifar100 inceptionv3 22.3M 22.81 6.39 60 60 40 40 200
cifar100 inceptionv4 41.3M 24.14 6.90 60 60 40 40 200
cifar100 inceptionresnetv2 65.4M 27.51 9.11 60 60 40 40 200
cifar100 xception 21.0M 25.07 7.32 60 60 40 40 200
cifar100 seresnet18 11.4M 23.56 6.68 60 60 40 40 200
cifar100 seresnet34 21.6M 22.07 6.12 60 60 40 40 200
cifar100 seresnet50 26.5M 21.42 5.58 60 60 40 40 200
cifar100 seresnet101 47.7M 20.98 5.41 60 60 40 40 200
cifar100 seresnet152 66.2M 20.66 5.19 60 60 40 40 200
cifar100 nasnet 5.2M 22.71 5.91 60 60 40 40 200
cifar100 wideresnet-40-10 55.9M 21.25 5.77 60 60 40 40 200
cifar100 stochasticdepth18 11.22M 31.40 8.84 60 60 40 40 200
cifar100 stochasticdepth34 21.36M 27.72 7.32 60 60 40 40 200
cifar100 stochasticdepth50 23.71M 23.35 5.76 60 60 40 40 200
cifar100 stochasticdepth101 42.69M 21.28 5.39 60 60 40 40 200
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Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet)


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