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My PyTorch implementation of CNNs. All networks in this repository are using CIFAR-100 dataset for training.

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Pytorch_CNN_Architectures

My PyTorch implementation of CNN models. All models are trained with the CIFAR-100 dataset.

Credits

Some of the codes in this repository are written by weiaicunzai.

Also, please cite me if you use the codes in this repository.

Contribution

Please feel free to contribute to this repository.

Requirements

Below are the specifications of my experiment environment:

  • python3.6
  • pytorch@1.0
  • tensorflow@1.5(optional)
  • cuda@8.0
  • cudnnv5
  • tensorboardX@1.6(optional)

Perhaps, pytorch@>=0.4 would also be fine (I am not 100% sure).

Usage

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.

Running the tensorbard(optional)

Install tensorboardX (a tensorboard wrapper for pytorch)

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

Training the model

Train all the model on a Tesla P40(22912MB)

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

$ python train.py -net vgg16

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

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

Testing the model

Test the model using test.py file. By replacing the network name and file path of the weights file, you could test other networks with your own weights.

$ 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 this repository, which contains various common training tricks and their pytorch implementations.

Basically, I followed 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 memory 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 0.69GB 60 60 40 40 200
cifar100 mobilenetv2 2.36M 31.92 09.02 0.84GB 60 60 40 40 200
cifar100 squeezenet 0.78M 30.59 8.36 0.73GB 60 60 40 40 200
cifar100 shufflenet 1.0M 29.94 8.35 0.84GB 60 60 40 40 200
cifar100 shufflenetv2 1.3M 30.49 8.49 0.78GB 60 60 40 40 200
cifar100 vgg11_bn 28.5M 31.36 11.85 1.98GB 60 60 40 40 200
cifar100 vgg13_bn 28.7M 28.00 9.71 1.98GB 60 60 40 40 200
cifar100 vgg16_bn 34.0M 27.07 8.84 2.03GB 60 60 40 40 200
cifar100 vgg19_bn 39.0M 27.77 8.84 2.08GB 60 60 40 40 200
cifar100 resnet18 11.2M 24.39 6.95 3.02GB 60 60 40 40 200
cifar100 resnet34 21.3M 23.24 6.63 3.22GB 60 60 40 40 200
cifar100 resnet50 23.7M 22.61 6.04 3.40GB 60 60 40 40 200
cifar100 resnet101 42.7M 22.22 5.61 3.72GB 60 60 40 40 200
cifar100 resnet152 58.3M 22.31 5.81 4.36GB 60 60 40 40 200
cifar100 preactresnet18 11.3M 27.08 8.53 3.09GB 60 60 40 40 200
cifar100 preactresnet34 21.5M 24.79 7.68 3.23GB 60 60 40 40 200
cifar100 preactresnet50 23.9M 25.73 8.15 3.42GB 60 60 40 40 200
cifar100 preactresnet101 42.9M 24.84 7.83 3.81GB 60 60 40 40 200
cifar100 preactresnet152 58.6M 22.71 6.62 4.20GB 60 60 40 40 200
cifar100 resnext50 14.8M 22.23 6.00 1.91GB 60 60 40 40 200
cifar100 resnext101 25.3M 22.22 5.99 2.63GB 60 60 40 40 200
cifar100 resnext152 33.3M 22.40 5.58 3.18GB 60 60 40 40 200
cifar100 attention59 55.7M 33.75 12.90 3.47GB 60 60 40 40 200
cifar100 attention92 102.5M 36.52 11.47 3.88GB 60 60 40 40 200
cifar100 densenet121 7.0M 22.99 6.45 1.28GB 60 60 40 40 200
cifar100 densenet161 26M 21.56 6.04 2.10GB 60 60 60 40 200
cifar100 densenet201 18M 21.46 5.9 2.10GB 60 60 40 40 200
cifar100 googlenet 6.2M 21.97 5.94 2.05GB 60 60 40 40 200
cifar100 inceptionv3 22.3M 22.81 6.39 2.26GB 60 60 40 40 200
cifar100 inceptionv4 41.3M 24.14 6.90 4.11GB 60 60 40 40 200
cifar100 inceptionresnetv2 65.4M 27.51 9.11 4.14GB 60 60 40 40 200
cifar100 xception 21.0M 25.07 7.32 1.67GB 60 60 40 40 200
cifar100 seresnet18 11.4M 23.56 6.68 3.12GB 60 60 40 40 200
cifar100 seresnet34 21.6M 22.07 6.12 3.29GB 60 60 40 40 200
cifar100 seresnet50 26.5M 21.42 5.58 3.70GB 60 60 40 40 200
cifar100 seresnet101 47.7M 20.98 5.41 4.39GB 60 60 40 40 200
cifar100 seresnet152 66.2M 20.66 5.19 5.95GB 60 60 40 40 200
cifar100 nasnet 5.2M 22.71 5.91 3.69GB 60 60 40 40 200