Abstract
This software provides methods and functions for training deep image classification models based on the
principle of anchoring. It features a user-friendly PyTorch wrapper that facilitates the easy conversion of
any model into an anchored model. The software supports various standard datasets and includes scripts
for conducting evaluations. Developed with PyTorch, it is compatible with common neural network
architectures used for image data. Additionally, it offers tools for computing evaluation metrics to assess
model performance.
- Developers:
-
Narayanaswamy, Vivek Sivaraman [1]
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Release Date:
- 2024-10-30
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Version:
- 0.1
- Licenses:
-
MIT License
- Sponsoring Org.:
-
USDOE National Nuclear Security Administration (NNSA)Primary Award/Contract Number:AC52-07NA27344
- Code ID:
- 153374
- Site Accession Number:
- LLNL-CODE-2003995
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Country of Origin:
- United States
Citation Formats
Narayanaswamy, Vivek Sivaraman.
Anchoring.
Computer Software.
https://github.com/LLNL/anchoring.
USDOE National Nuclear Security Administration (NNSA).
30 Oct. 2024.
Web.
doi:10.11578/dc.20250331.1.
Narayanaswamy, Vivek Sivaraman.
(2024, October 30).
Anchoring.
[Computer software].
https://github.com/LLNL/anchoring.
https://doi.org/10.11578/dc.20250331.1.
Narayanaswamy, Vivek Sivaraman.
"Anchoring." Computer software.
October 30, 2024.
https://github.com/LLNL/anchoring.
https://doi.org/10.11578/dc.20250331.1.
@misc{
doecode_153374,
title = {Anchoring},
author = {Narayanaswamy, Vivek Sivaraman},
abstractNote = {This software provides methods and functions for training deep image classification models based on the
principle of anchoring. It features a user-friendly PyTorch wrapper that facilitates the easy conversion of
any model into an anchored model. The software supports various standard datasets and includes scripts
for conducting evaluations. Developed with PyTorch, it is compatible with common neural network
architectures used for image data. Additionally, it offers tools for computing evaluation metrics to assess
model performance.},
doi = {10.11578/dc.20250331.1},
url = {https://doi.org/10.11578/dc.20250331.1},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20250331.1}},
year = {2024},
month = {oct}
}