TorchMetrics - Measuring Reproducibility in PyTorch

Python Submitted 17 December 2021Published 11 February 2022

Editor: @taless474 (all papers)
Reviewers: @inpefess (all reviews), @richrobe (all reviews), @reneraab (all reviews)


Nicki Skafte Detlefsen (0000-0002-8133-682X), Jiri Borovec (0000-0001-7437-824X), Justus Schock (0000-0003-0512-3053), Ananya Harsh Jha, Teddy Koker, Luca Di Liello, Daniel Stancl, Changsheng Quan, Maxim Grechkin, William Falcon


Detlefsen et al., (2022). TorchMetrics - Measuring Reproducibility in PyTorch. Journal of Open Source Software, 7(70), 4101,

@article{Detlefsen2022, doi = {10.21105/joss.04101}, url = {}, year = {2022}, publisher = {The Open Journal}, volume = {7}, number = {70}, pages = {4101}, author = {Nicki Skafte Detlefsen and Jiri Borovec and Justus Schock and Ananya Harsh Jha and Teddy Koker and Luca Di Liello and Daniel Stancl and Changsheng Quan and Maxim Grechkin and William Falcon}, title = {TorchMetrics - Measuring Reproducibility in PyTorch}, journal = {Journal of Open Source Software} }
Copy citation string · Copy BibTeX  

python deep learning pytorch

Markdown badge



Authors of JOSS papers retain copyright.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Creative Commons License

Table of Contents
Public user content licensed CC BY 4.0 unless otherwise specified.
ISSN 2475-9066