michelletrinh310 / pytorch_cifar10

Pretrained TorchVision models on CIFAR10 dataset (with weights)

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

Weight and Biases' details report for this project WandB Report

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