Generic library for prototype-based classifiers
Project description
CaBRNet is an open source library that offers an API to use state-of-the-art prototype-based architectures (also called case-based reasoning models), or easily add a new one.
Currently, CaBRNet supports the following architectures:
- ProtoPNet, as described in Chaofan Chen, Oscar Li, Chaofan Tao, Alina Jade Barnett, Jonathan Su and Cynthia Rudin. This Looks like That: Deep Learning for Interpretable Image Recognition. Proceedings of the 33rd International Conference on Neural Information Processing Systems, page 8930–8941, 2019.
- ProtoTree, as described in Meike Nauta, Ron van Bree and Christin Seifert. Neural Prototype Trees for Interpretable Fine-grained Image Recognition. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 14928–14938, 2021.
- ProtoPool, as described in Dawid Rymarczyk, Lukasz Struski, Michal Gorszczak, Koryna Lewandowska, Jacek Tabor and Bartosz Zielinski. Interpretable Image Classification with Differentiable Prototypes Assignment. 2021 European Conference on Computer Vision (ECCV).
Install
- To install the package:
python3 -m pip install --upgrade cabrnet
- To install development related dependencies
python3 -m pip install --upgrade cabrnet[dev]
- To install documentation related dependencies
python3 -m pip install --upgrade cabrnet[doc]
- To install legacy testing related dependencies
python3 -m pip install --upgrade cabrnet[legacy]
Links
Authors
This library is collaboratively maintained by members of CEA-LIST. The current point of contact is Romain Xu-Darme. The following authors contributed in a significant manner to the code base and/or the documentation of the library:
- Romain Xu-Darme (CEA-LIST)
- Aymeric Varasse (CEA-LIST)
- Alban Grastien (CEA-LIST)
- Julien Girard-Satabin (CEA-LIST)
The following authors contributed in a significant manner to the experiments and the publication of trained models:
- Jules Soria (CEA-LIST)
- Alban Grastien (CEA-LIST)
- Romain Xu-Darme (CEA-LIST)
Reference and Citation
Please refer to our work when using CaBRNet:
Romain Xu-Darme, Aymeric Varasse, Alban Grastien, Julien Girard-Satabin, Zakaria Chihani. "CaBRNet, an open-source library for developing and evaluating Case-Based Reasoning Models", xAI-2024 Late-breaking Work, Demos and Doctoral Consortium at the 2nd World Conference on eXplainable Artificial Intelligence.
BibTex citation:
@article{xudarme2024cabrnet,
title={CaBRNet, an open-source library for developing and evaluating Case-Based Reasoning Models},
author={Romain Xu-Darme and Aymeric Varasse and Alban Grastien and Julien Girard and Zakaria Chihani},
booktitle={Proceedings of the xAI-2024 Late-breaking Work, Demos and Doctoral Consortium at the 2nd World Conference on eXplainable Artificial Intelligence},
year={2024},
}
License
This project is licensed under the LGPL-2.1 license.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file cabrnet-1.2.tar.gz.
File metadata
- Download URL: cabrnet-1.2.tar.gz
- Upload date:
- Size: 138.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.0.1 CPython/3.10.19
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
3b09ee85f71cfe0d084f3e1ea02c62e5614755cb7183d5d6c7502a3136a45ca3
|
|
| MD5 |
7d1d0984024520f42203676a68a3db7e
|
|
| BLAKE2b-256 |
6b9d5d3ad2d6ce2c452143ac76daf7f6aadcc28242b89897b0c9dcbf69f1afa6
|
File details
Details for the file cabrnet-1.2-py3-none-any.whl.
File metadata
- Download URL: cabrnet-1.2-py3-none-any.whl
- Upload date:
- Size: 175.1 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.0.1 CPython/3.10.19
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
537ca3975021138ff069a381bd7c1f8e6bfd191aabcd99ed0a5d2b647f41489e
|
|
| MD5 |
9809b1224fb04d0d4a927c54820248de
|
|
| BLAKE2b-256 |
3b5effdef1873f18cba52df0486e8227aa460f97301bb199754db14d21ff017b
|