Papers › Training Region-based Object Detectors with Online Hard Example Mining
Training Region-based Object Detectors with Online Hard Example Mining
Abhinav Shrivastava, Abhinav Gupta, Ross Girshick
The field of object detection has made significant advances riding on the wave of region-based ConvNets, but their training procedure still includes many heuristics and hyperparameters that are costly to tune. We present a simple yet surprisingly effective online hard example mining (OHEM) algorithm for training region-based ConvNet detectors. Our motivation is the same as it has always been -- detection datasets contain an overwhelming number of easy examples and a small number of hard examples. Automatic selection of these hard examples can make training more effective and efficient. OHEM is a simple and intuitive algorithm that eliminates several heuristics and hyperparameters in common use. But more importantly, it yields consistent and significant boosts in detection performance on benchmarks like PASCAL VOC 2007 and 2012. Its effectiveness increases as datasets become larger and more difficult, as demonstrated by the results on the MS COCO dataset. Moreover, combined with complementary advances in the field, OHEM leads to state-of-the-art results of 78.9% and 76.3% mAP on PASCAL VOC 2007 and 2012 respectively.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Face Identification | Trillion Pairs Dataset | HM-Softmax | Accuracy | 36.75 | #6 of 6 | Archive leaderboard | report |
| Face Verification | Trillion Pairs Dataset | HM-Softmax | Accuracy | 34.46 | #6 of 6 | Archive leaderboard | report |
| Object Detection | PASCAL VOC 2007 | OHEM | MAP | 78.9% | #14 of 30 | 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.
Methods
Introduced by this paper: OHEM
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