pytorch-cifar100：在cifar100上实践（ResNet，DenseNet，VGG，GoogleNet，Ince...

# 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 ```bash $ 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 ```bash $ 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 ```bash # 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 ```bash $ python test.py -net vgg16 -weights path_to_vgg16_weights_file ``` ## Implementated NetWork - vgg [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556v6) - googlenet [Going Deeper with Convolutions](https://arxiv.org/abs/1409.4842v1) - inceptionv3 [Rethinking the Inception Architecture for Computer Vision](https://arxiv.org/abs/1512.00567v3) - inceptionv4, inception_resnet_v2 [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://arxiv.org/abs/1602.07261) - xception [Xception: Deep Learning with Depthwise Separable Convolutions](https://arxiv.org/abs/1610.02357) - resnet [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385v1) - resnext [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/abs/1611.05431v2) - resnet in resnet [Resnet in Resnet: Generalizing Residual Architectures](https://arxiv.org/abs/1603.08029v1) - densenet [Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993v5) - shufflenet [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://arxiv.org/abs/1707.01083v2) - shufflenetv2 [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://arxiv.org/abs/1807.11164v1) - mobilenet [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) - mobilenetv2 [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) - residual attention network [Residual Attention Network for Image Classification](https://arxiv.org/abs/1704.06904) - senet [Squeeze-and-Excitation Networks](https://arxiv.org/abs/1709.01507) - squeezenet [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://arxiv.org/abs/1602.07360v4) - nasnet [Learning Transferable Architectures for Scalable Image Recognition](https://arxiv.org/abs/1707.07012v4) - wide residual network[Wide Residual Networks](https://arxiv.org/abs/1605.07146) - stochastic depth networks[Deep Networks with Stochastic Depth](https://arxiv.org/abs/1603.09382) ## 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](https://github.com/weiaicunzai/Bag_of_Tricks_for_Image_Classification_with_Convolutional_Neural_Networks), contains various common training tricks and their pytorch implementations. I follow the hyperparameter settings in paper [Improved Regularization of Convolutional Neural Networks with Cutout](https://arxiv.org/abs/1708.04552v2), 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](https://arxiv.org/abs/1701.06548v1) and [Random Erasing Data Augmentation](https://arxiv.org/abs/1708.04896v2), 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|