Papers › A Short Note on Evaluating RepNet for Temporal Repetition Counting in Videos
A Short Note on Evaluating RepNet for Temporal Repetition Counting in Videos
Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, Andrew Zisserman
We discuss some consistent issues on how RepNet has been evaluated in various papers. As a way to mitigate these issues, we report RepNet performance results on different datasets, and release evaluation code and the RepNet checkpoint to obtain these results. Code URL: https://github.com/google-research/google-research/blob/master/repnet/
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Repetitive Action Counting | Countix | RepNet | MAE | 0.3002 | #3 of 3 | Archive leaderboard | report |
| Repetitive Action Counting | Countix | RepNet | OBO | 0.7047 | #3 of 3 | Archive leaderboard | report |
| Repetitive Action Counting | RepCount | RepNet | MAE | 0.3308 | #3 of 6 | Archive leaderboard | report |
| Repetitive Action Counting | RepCount | RepNet | OBO | 0.5329 | #3 of 6 | Archive leaderboard | report |
| Repetitive Action Counting | UCFRep | RepNet | MAE | 0.2088 | #2 of 2 | Archive leaderboard | report |
| Repetitive Action Counting | UCFRep | RepNet | OBO | 0.7333 | #2 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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