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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


you can install this package using the following command

pip install iirc

Dataset Downloading Instructions


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/ file.


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


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.


If you find this work useful for your research, this is the way to cite it:

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} }

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iirc-1.0.1.tar.gz (91.5 kB view hashes)

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