TensorFlow2 Classification Model Zoo. I'm playing with TensorFlow2 on the CIFAR-10 dataset.
- LeNet
- AlexNet
- VGG [11, 13, 16, 19]
- ResNet [18, 34, 50, 101, 152]
- DenseNet [121, 169, 201]
- PreAct-ResNet [18, 34, 50, 101, 152]
- SENet
- SE-ResNet [18, 34, 50, 101, 152]
- SE-PreAct-ResNet [18, 34, 50, 101, 152]
- MobileNet
- MobileNetV2
- Python 3.8+
- TensorFlow 2.4.0+
Start training with:
python train.py --model resnet18You can manually resume the training with:
python train.py --model resnet18 --resumepython test.py --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 train.py --model sepreactresnet18If you suffer from loss=nan issue, you can circumvent it by using a smaller learning rate, i.e.
python train.py --model sepreactresnet18 --lr 5e-2