resnet50 accuracy is a little bit worse than resnet18

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

vgg Very Deep Convolutional Networks for Large-Scale Image Recognition
googlenet Going Deeper with Convolutions
inceptionv3 Rethinking the Inception Architecture for Computer Vision
inceptionv4, inception_resnet_v2 Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
xception Xception: Deep Learning with Depthwise Separable Convolutions
resnet Deep Residual Learning for Image Recognition
resnext Aggregated Residual Transformations for Deep Neural Networks
resnet in resnet Resnet in Resnet: Generalizing Residual Architectures
densenet Densely Connected Convolutional Networks
shufflenet ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
shufflenetv2 ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
mobilenet MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
mobilenetv2 MobileNetV2: Inverted Residuals and Linear Bottlenecks
residual attention network Residual Attention Network for Image Classification
senet Squeeze-and-Excitation Networks
squeezenet SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
nasnet Learning Transferable Architectures for Scalable Image Recognition
wide residual networkWide Residual Networks
stochastic depth networksDeep Networks with Stochastic Depth

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

	class Transition(nn.Module):
	def __init__(self, in_channels, out_channels):
	super().__init__()
	#"""The transition layers used in our experiments
	#consist of a batch normalization layer and an 1×1
	#convolutional layer followed by a 2×2 average pooling
	#layer""".
	self.down_sample = nn.Sequential(
	nn.BatchNorm2d(in_channels),
	nn.Conv2d(in_channels, out_channels, 1, bias=False),
	nn.AvgPool2d(2, stride=2)
	)

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pytorch-cifar100's Introduction

Pytorch-cifar100

Requirements

Usage

1. enter directory

2. dataset

3. run tensorbard(optional)

4. train the model

5. test the model

Implementated NetWork

Training Details

Results

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