Lyken17/pytorch-OpCounter: papers linking to it

GitHub · 5,085 stars · 537 forks · Python · license MIT · created 2018 · github.com/Lyken17/pytorch-OpCounter

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to Lyken17/pytorch-OpCounter (https://github.com/Lyken17/pytorch-OpCounter), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

Key findings#

3 papers in the tracked journals link to the GitHub repository Lyken17/pytorch-OpCounter in their Crossref records, the first in 2020; most of them (1) in 2026.
By category: AI (3); the journals with the most are Expert Systems with Applications (1), Pattern Recognition (1) and Neural Computing and Applications (1).

By year#

20202026

Papers linking to Lyken17/pytorch-OpCounter by publication year
Year	Papers
2020	1
2022	1
2026	1

Journals#

Journals whose papers link to Lyken17/pytorch-OpCounter most
Journal	Papers
Expert Systems with Applications	1
Pattern Recognition	1
Neural Computing and Applications	1

Papers linking to Lyken17/pytorch-OpCounter, most cited first
Published	Title	Journal	Authors	Citations	Link
2022-06-11	Estimating crowd density with edge intelligence based on lightweight convolutional neural networks	Expert Systems with Applications	Shuo Wang, Ziyuan Pu, Qianmu Li et al.	36	cites it
2026-01-24	Domain generalization via domain uncertainty shrinkage	Pattern Recognition	Jun-Zheng Chu, Bin Pan, Tianyang Shi et al.	4	cites it
2020-06-16	Attention deep residual networks for MR image analysis	Neural Computing and Applications	Mengqing Mei, Fazhi He, Shan Xue	3	cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). Lyken17/pytorch-OpCounter: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-lyken17-pytorch-opcounter

@misc{scholar4ai-code-github-lyken17-pytorch-opcounter,
  author = {{Scholar4AI}},
  title = {Lyken17/pytorch-OpCounter: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-lyken17-pytorch-opcounter}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

Free to reuse for non-commercial purposes with attribution (CC BY-NC 4.0).