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Code for Plain Self-Ensembles

Video: https://youtu.be/z_3Qv4In2ac Forked from https://github.com/huyvnphan/PyTorch_CIFAR10

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 92.09% 128.813 M 491 MB
2 vgg13_bn 94.29% 128.998 M 492 MB
3 vgg16_bn 93.91% 134.310 M 512 MB
4 vgg19_bn 93.80% 139.622 M 533 MB
5 resnet18 93.33% 11.174 M 43 MB
6 resnet34 92.92% 21.282 M 81 MB
7 resnet50 93.86% 23.521 M 90 MB
8 densenet121 94.14% 6.956 M 27 MB
9 densenet161 94.24% 26.483 M 102 MB
10 densenet169 94.00% 12.493 M 48 MB
11 mobilenet_v2 94.17% 2.237 M 9 MB
12 googlenet 92.73% 5.491 M 21 MB
13 inception_v3 93.76% 21.640 M 83 MB

How to use pretrained models

Automatically download and extract the weights from Box (2.39 GB)

python cifar10_download.py

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

Load model and run

from cifar10_models import *

# Untrained model
my_model = vgg11_bn()

# Pretrained model
my_model = vgg11_bn(pretrained=True)

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

mean = [0.4914, 0.4822, 0.4465]
std = [0.2023, 0.1994, 0.2010]

How to train models from scratch

Check the cifar10_train.py to see all available hyper-parameter choices. To reproduce the same accuracy use the default hyper-parameters

python cifar10_train.py --classifier resnet18 --gpu '0,'

How to test trained models

python cifar10_test.py --classifier resnet18 --gpu '0,'

Output

TEST RESULTS {'Accuracy': 93.33}

Check the TensorBoard logs

To see the training progress, cd to the tensorboard_logs and run TensorBoard there

tensorboard --logdir=. --port=YOUR_PORT_NUMBER

Then go to http://localhost:YOUR_PORT_NUMBER

Requirements

Just to use pretrained models

  • pytorch = 1.5.0

To train & test

  • torchvision = 0.6.0
  • tensorboard = 2.2.1
  • pytorch-lightning = 0.7.6