This project demonstrates some personal examples with PyTorch on CIFAR10 dataset.
The CIFAR10 dataset is 32x32 size, 50000 train images and 10000 test images. The dataset is divided into 40000 train images, 10000 validation images, and 10000 images.
- Test for many models, each model is a a little different from orgin for 32*32 input, and will contiune to add new model.
- Visdom realtime visualization of loss, acc, port 8097.
- Use torchnet for training.
- Use jupyter book for recording echo model training process.
- Run visdom first, python -m visdom.server &
- Open the jupyter file for the corresponding model, and then run all cells.
- torch
- torchvision
- numpy
- torchnet
- visdom
All result is tested on 10000 test images.You can lookup the jupyter for more details.
| Model | Accuracy |
|---|---|
| SVM | 34.27% |
| Softmax | 35.67% |
| small-ResNet20 | 91.38% |
| small-ResNet32 | 92.53% |
| small-ResNet56 | 93.31% |
| vgg11 | 91.25% |
| vgg13 | 92.84% |
| vgg16 | 92.94%% |
| MobileNetV1 | 92.45% |
| MobileNetV2 | 92.47% |