Spatial Transformer Networks in Pytorch.
This repository contains a PyTorch implementation of Spatial Transformer Networks by Jaderberg et al. The results are reported on the CIFAR-10 dataset and SVHN results will be coming up shortly.
Training is made to be very simple. You define your own experiment directory under experiments folder and populate it with a
params.json. Example configs can be found in experiments\base_svhn and experiments\stn_svhn. To train a model in models
folder:
python train.py --param_path <path_to_experiment> --resume_path <last checkpoint>
The code frequently stores the checkpoints as last.pth.tar corresponding to the last epoch run and best.pth.tar corresponding
to the checkpoint which had the best validation set accuracy. Training can be resumed by giving that parameter to the train script.
For example if you want to train a base Network and want to fine tune from the last best checkpoint you can write:
python train.py -- param_path experiments/base_svhn --resume_path best
| Dataset | Model | Hardware | Epochs | Validation Accuracy |
|---|---|---|---|---|
| CIFAR-10 | Base | Gtx-1080 | 150 | 70.9% |
| CIFAR-10 | STN-Net | Gtx-1080 | 150 | 76.96% |