Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/mmasana/FACIL

Framework for Analysis of Class-Incremental Learning with 12 state-of-the-art methods and 3 baselines.
https://github.com/mmasana/FACIL

continual-learning deep-learning framework incremental-learning lifelong-learning machine-learning reproducible-research survey

Last synced: 3 months ago
JSON representation

Framework for Analysis of Class-Incremental Learning with 12 state-of-the-art methods and 3 baselines.

Awesome Lists containing this project

README 

        





# Framework for Analysis of Class-Incremental Learning



---





  What is FACIL •

  Key Features •

  How To Use •

  Approaches •

  Datasets •

  Networks •

  License •

  Cite






---



## What is FACIL

FACIL started as code for the paper:  

_**Class-incremental learning: survey and performance evaluation**_  

*Marc Masana, Xialei Liu, Bartlomiej Twardowski, Mikel Menta, Andrew D. Bagdanov, Joost van de Weijer*  

([TPAMI](https://ieeexplore.ieee.org/abstract/document/9915459)) ([arxiv](https://arxiv.org/abs/2010.15277))



It allows to reproduce the results in the paper as well as provide a (hopefully!) helpful framework to develop new

methods for incremental learning and analyse existing ones. Our idea is to expand the available approaches

and tools with the help of the community. To help FACIL grow, don't forget to star this github repository and

share it to friends and coworkers!



## Key Features

We provide a framework based on class-incremental learning. However, task-incremental learning is also fully

supported. Experiments by default provide results on both task-aware and task-agnostic evaluation. Furthermore, if an

experiment runs with one task on one dataset, results would be equivalent to 'common' supervised learning.



| Setting | task-ID at train time | task-ID at test time | # of tasks |

| -----   | ------------------------- | ------------------------ | ------------ |

| [class-incremental learning](https://ieeexplore.ieee.org/abstract/document/9915459) | yes | no | ≥1 |

| [task-incremental learning](https://ieeexplore.ieee.org/abstract/document/9349197) | yes | yes | ≥1 |

| non-incremental supervised learning | yes | yes | 1 |



Current available approaches include:





  Finetuning • Freezing • Joint



  LwF • iCaRL • EWC • PathInt • MAS • RWalk • EEIL • LwM • DMC • BiC • LUCIR • IL2M






## How To Use

Clone this github repository:

```

git clone https://github.com/mmasana/FACIL.git

cd FACIL

```



  Optionally, create an environment to run the code (click to expand).



  ### Using a requirements file

  The library requirements of the code are detailed in [requirements.txt](requirements.txt). You can install them

  using pip with:

  ```

  python3 -m pip install -r requirements.txt

  ```



  ### Using a conda environment

  Development environment based on Conda distribution. All dependencies are in `environment.yml` file.



  #### Create env

  To create a new environment check out the repository and type: 

  ```

  conda env create --file environment.yml --name FACIL

  ```

  *Notice:* set the appropriate version of your CUDA driver for `cudatoolkit` in `environment.yml`.



  #### Environment activation/deactivation

  ```

  conda activate FACIL

  conda deactivate

  ```



To run the basic code:

```

python3 -u src/main_incremental.py

```

More options are explained in the [`src`](./src), including GridSearch usage. Also, more specific options on approaches,

loggers, datasets and networks.



### Scripts

We provide scripts to reproduce the specific scenarios proposed in 

_**Class-incremental learning: survey and performance evaluation**_:



* CIFAR-100 (10 tasks) with ResNet-32 without exemplars

* CIFAR-100 (10 tasks) with ResNet-32 with fixed and growing memory

* _MORE COMING SOON..._



All scripts run 10 times to later calculate mean and standard deviation of the results.

Check out all available in the [scripts](scripts) folder.



## License

Please check the MIT license that is listed in this repository.



## Cite

If you want to cite the framework feel free to use this preprint citation while we await publication:

```bibtex

@article{masana2022class,

  title={Class-Incremental Learning: Survey and Performance Evaluation on Image Classification},

  author={Masana, Marc and Liu, Xialei and Twardowski, Bartłomiej and Menta, Mikel and Bagdanov, Andrew D. and van de Weijer, Joost},

  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},

  doi={10.1109/TPAMI.2022.3213473},

  year={2023},

  volume={45},

  number={5},

  pages={5513-5533}}

}

```



---



The basis of FACIL is made possible thanks to [Marc Masana](https://github.com/mmasana),

[Xialei Liu](https://github.com/xialeiliu), [Bartlomiej Twardowski](https://github.com/btwardow)

and [Mikel Menta](https://github.com/mkmenta). Code structure is inspired by [HAT](https://github.com/joansj/hat.). Feel free to contribute or propose new features by opening an issue!