Official implementation of the N-Jet layer from: "Resolution learning in deep convolutional networks using scale-space theory", Silvia L.Pintea, Nergis Tomen, Stanley F. Goes, Marco Loog, Jan C. van Gemert, Transactions on Image Processing, 2021. (archive link )
The code was tested on Linux (Ubuntu 18.04.5 LTS):
- python 3.6.9
- pytorch 1.0.0 (https://pytorch.org/) with additional packages:
- tensorboard
- torchvision
- cuda 10.2
This code is organized as follows:
checkpoints/cifar10/nin_shared_srf -- directory where trained models are saved (contains an example of trained NiN with structured N-Jet layer)
models/ -- the pytorch layer definition of models
nin.py -- an example of the original network-in-network (NiN) model
nin_shared_srf.py -- an example of using the structured N-Jet layer for the network-in-network (NiN) model
srf -- contains the structured N-Jet layer implementation
gaussian_basis_filters.py -- defines the Gaussian derivatives using the Hermite polynomials
structured_conv_layer.py -- defines the N-Jet structured convolutional layer (called SRF) using the Gaussian basis
demo.py -- the "main" script that calls the network training
demo.sh -- the script running the demo and containing an example of hyper-parameters for the N-Jet layer
Third party software (the code is adapted from https://github.com/bearpaw/pytorch-classification):
utils -- code utilities
eval.py -- evaluation code for computing the top-1/top-5 acurracy
dataset.py -- dataset creation (train/val/test) and loading
train_and_test.py
All credit for third party sofware is with the authors.
The output will be saved in:
checkpoints/ -- directory where trained models are saved
cifar10/
nin_shared_srf -- contains an example of trained NiN with structured N-Jet layer)
- Edit the
demo.shfile to match the hyper parameters you want to use. - Run
bash demo.sh
If you use this code, please cite the publication:
@article{pintea2021resolution,
title={Resolution learning in deep convolutional networks using scale-space theory},
author={Pintea, Silvia L and Tomen, Nergis and Goes, Stanley F and Loog, Marco and van Gemert, Jan C},
journal={TIP},
doi={10.1109/TIP.2021.3115001},
year={2021}
}