PyTorch models trained on CIFAR-10 dataset

• I modified TorchVision official implementation of popular CNN models, and trained those on CIFAR-10 dataset.
• I changed number of class, filter size, stride, and padding in the the original code so that it works with CIFAR-10.
• I also share the weights of these models, so you can just load the weights and use them.
• The code is highly re-producible and readable by using PyTorch-Lightning.

Statistics of supported models

No.	Model	Val. Acc.	No. Params	Size
1	vgg11_bn	92.39%	28.150 M	108 MB
2	vgg13_bn	94.22%	28.334 M	109 MB
3	vgg16_bn	94.00%	33.647 M	129 MB
4	vgg19_bn	93.95%	38.959 M	149 MB
5	resnet18	93.07%	11.174 M	43 MB
6	resnet34	93.34%	21.282 M	82 MB
7	resnet50	93.65%	23.521 M	91 MB
8	densenet121	94.06%	6.956 M	28 MB
9	densenet161	94.07%	26.483 M	103 MB
10	densenet169	94.05%	12.493 M	49 MB
11	mobilenet_v2	93.91%	2.237 M	9 MB
12	googlenet	92.85%	5.491 M	22 MB
13	inception_v3	93.74%	21.640 M	83 MB

Details Report & Run Logs

Weight and Biases' details report for this project WandB Report

Weight and Biases' run logs for this project WandB Run Log. You can see each run hyper-parameters, training accuracy, validation accuracy, loss, time taken.

How To Cite

How to use pretrained models

Automatically download and extract the weights from Box (933 MB)

python train.py --download_weights 1

Or use Google Drive backup link (you have to download and extract manually)

Load model and run

from cifar10_models.vgg import vgg11_bn, vgg13_bn, vgg16_bn, vgg19_bn

# Untrained model
my_model = vgg11_bn()

# Pretrained model
my_model = vgg11_bn(pretrained=True)
my_model.eval() # for evaluation

If you use your own images, all models expect data to be in range [0, 1] then normalized by

mean = [0.4914, 0.4822, 0.4465]
std = [0.2471, 0.2435, 0.2616]

How to train models from scratch

Check the train.py to see all available hyper-parameter choices. To reproduce the same accuracy use the default hyper-parameters

python train.py --classifier resnet18

How to test pretrained models

python train.py --test_phase 1 --pretrained 1 --classifier resnet18

Output

{'acc/test': tensor(93.0689, device='cuda:0')}

Requirements

Just to use pretrained models

• pytorch = 1.7.0

To train & test

• pytorch = 1.7.0
• torchvision = 0.7.0
• tensorboard = 2.2.1
• pytorch-lightning = 1.1.0