Training CIFAR-10 with TensorFlow2(TF2)

TensorFlow 2.4
Python 3.8

TensorFlow2 Classification Model Zoo. I’m playing with TensorFlow2 on the CIFAR-10 dataset.



  • Python 3.8+
  • TensorFlow 2.4.0+


Start training with:

python --model resnet18

You can manually resume the training with:

python --model resnet18 --resume


python --model resnet18


Model Acc. Param.
LeNet 67.85% 0.06M
AlexNet 78.81% 21.6M
VGG11 92.61% 9.2M
VGG13 94.31% 9.4M
VGG16 94.27% 14.7M
VGG19 93.65% 20.1M
ResNet18 95.37% 11.2M
ResNet34 95.48% 21.3M
ResNet50 95.41% 23.6M
ResNet101 95.44% 42.6M
ResNet152 95.29% 58.3M
DenseNet121 95.37% 7.0M
DenseNet169 95.10% 12.7M
DenseNet201 94.79% 18.3M
PreAct-ResNet18 94.08% 11.2M
PreAct-ResNet34 94.76% 21.3M
PreAct-ResNet50 94.81% 23.6M
PreAct-ResNet101 94.95% 42.6M
PreAct-ResNet152 95.07% 58.3M
SE-ResNet18 95.44% 11.3M
SE-ResNet34 95.30% 21.5M
SE-ResNet50 95.76% 26.1M
SE-ResNet101 95.40% 47.3M
SE-ResNet152 95.29% 64.9M
SE-PreAct-ResNet18 94.54% 11.3M
SE-PreAct-ResNet34 95.30% 21.5M
SE-PreAct-ResNet50 94.22% 26.1M
SE-PreAct-ResNet101 94.34% 47.3M
SE-PreAct-ResNet152 94.28% 64.9M
MobileNet 92.34% 3.2M
MobileNetV2 94.03% 2.3M


All abovementioned models are available. To specify the model, please use the model name without the hyphen. For instance, to train with SE-PreAct-ResNet18, you can run the following script:

python --model sepreactresnet18

If you suffer from loss=nan issue, you can circumvent it by using a smaller learning rate, i.e.

python --model sepreactresnet18 --lr 5e-2


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