Classification & generative models on MNIST, implemented by Keras.
Units: accuracy %
| Model | Validation | Test | Comment |
|---|---|---|---|
| Simple MLP | 0.0% | 0.0% | |
| Simple convnet | 0.0% | 0.0% | |
| VGG-like convnet | 0.0% | 0.0% | |
| VGG16 | 99.61% | 99.68% | Batch size: 64, Epoch: 200, Image standardization, Data augmentation: rotating(15), width/height shift(0.1), shearing(0.2), zooming(0.1) |
| Mobilenet | 99.63% | 99.68% | Batch size: 64, Epoch: 200, Image standardization, Data augmentation: rotating(15), width/height shift(0.1), shearing(0.2), zooming(0.1) |
| Resnet164 | 99.72% | 99.70% | Batch size: 128, Epoch: 200, Image standardization, Data augmentation: rotating(15), width/height shift(0.1), shearing(0.2), zooming(0.1) |
| WideResnet28-10 | 99.72% | 99.76% | Batch size: 128, Epoch: 200, Image standardization, Data augmentation: rotating(15), width/height shift(0.1), shearing(0.2), zooming(0.1) |
| Model | Sample | Comment |
|---|---|---|
| GAN | ||
| DCGAN | ||
| cGAN |