lzwwang/vip-scale-invariant-s-cnn

Scale-Steerable CNN Implementation for PyTorch

Scale-steerable filters for the locally-scale invariant convolutional neural network (PyTorch)

Scale-Steerable CNN (SS-CNN) Implementation for PyTorch. This is the official code repository for the paper "Scale Steerable Filters for Locally Scale-Invariant Convolutional Neural Networks" (https://arxiv.org/abs/1906.03861)

Please refer to ScaleSteerableInvariant_Network.py for the network library which contains the SS-CNN layer along with various network architectures. The scale steerable basis functions are being created in scale_steering_lite.py. The dataset splits for MNIST-Scale, FMNIST-Scale and MNIST-Scale-local are provided as well.

The main code where the SS-CNN is trained is in main_test.py. The dataset class is also included in that file, where the images are being resized to twice their size and max-normalized (optional).

To run on the different datasets, one can simply change the dataset_name paramter in the main function of main_test.py, along with the training size, batch size and total_epochs.

Important: Note that there are predefined networks for each of the datasets in ScaleSteerableInvariant_Network.py, any of which can be used by changing the Networks_to_train parameter in main_test.py. Also, note that the current implementation assumes availability of cuda. To change to a non-cuda based implementation, you will need to remove all mentions of .cuda() for a start.