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.
Index
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)
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
cabrnet-1.1.tar.gz
(120.8 kB
view hashes)
Built Distribution
cabrnet-1.1-py3-none-any.whl
(154.8 kB
view hashes)