This package provides a way for adapting the different datasets (currently supports *CIFAR-100* and *ImageNet*) to the *iirc* setup and the *class incremental learning* setup, and loading them in a standardized manner.
Project description
iirc package
This package provides a way for adapting the different datasets (currently supports CIFAR-100 and ImageNet) to the iirc setup and the class incremental learning setup, and loading them in a standardized manner.
The documentation and usage guide are available here
Homepage | Paper | Documentation
Installation
you can install this package using the following command
pip install iirc
Dataset Downloading Instructions
CIFAR-100
To be able to run the code with CIFAR-100 derived datasets, just download the dataset from the official website and extract it, or use the ./utils/download_cifar.py file.
ImageNet
In the case of ImageNet, it has to be downloaded manually, and be arranged in the following manner:
- dataset folder
- train
- n01440764
- n01443537
- …
- val
- n01440764
- n01443537
- …
- train
Contributing
If you think you can help us make the iirc package more useful for the lifelong learning community, please don't hesistate to submit an issue or send a pull request.
Citation
If you find this work useful for your research, this is the way to cite it:
@misc{abdelsalam2021iirc,
title = {IIRC: Incremental Implicitly-Refined Classification},
author={Mohamed Abdelsalam and Mojtaba Faramarzi and Shagun Sodhani and Sarath Chandar},
year={2021}, eprint={2012.12477}, archivePrefix={arXiv},
primaryClass={cs.CV} }
Project details
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