Papers › Simple Training Strategies and Model Scaling for Object Detection
Simple Training Strategies and Model Scaling for Object Detection
Xianzhi Du, Barret Zoph, Wei-Chih Hung, Tsung-Yi Lin
The speed-accuracy Pareto curve of object detection systems have advanced through a combination of better model architectures, training and inference methods. In this paper, we methodically evaluate a variety of these techniques to understand where most of the improvements in modern detection systems come from. We benchmark these improvements on the vanilla ResNet-FPN backbone with RetinaNet and RCNN detectors. The vanilla detectors are improved by 7.7% in accuracy while being 30% faster in speed. We further provide simple scaling strategies to generate family of models that form two Pareto curves, named RetinaNet-RS and Cascade RCNN-RS. These simple rescaled detectors explore the speed-accuracy trade-off between the one-stage RetinaNet detectors and two-stage RCNN detectors. Our largest Cascade RCNN-RS models achieve 52.9% AP with a ResNet152-FPN backbone and 53.6% with a SpineNet143L backbone. Finally, we show the ResNet architecture, with three minor architectural changes, outperforms EfficientNet as the backbone for object detection and instance segmentation systems.
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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 |
|---|---|---|---|---|---|---|---|
| Object Detection | COCO minival | Cascade RCNN-RS (SpineNet-143L, single scale) | APL | 70.6 | #59 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Cascade RCNN-RS (SpineNet-143L, single scale) | APM | 56.7 | #59 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Cascade RCNN-RS (SpineNet-143L, single scale) | APS | 34.5 | #59 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Cascade RCNN-RS (SpineNet-143L, single scale) | box AP | 53.6 | #59 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Cascade RCNN-RS (ResNet-200, single scale) | APL | 70.3 | #62 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Cascade RCNN-RS (ResNet-200, single scale) | APM | 56.2 | #62 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Cascade RCNN-RS (ResNet-200, single scale) | APS | 33.9 | #62 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Cascade RCNN-RS (ResNet-200, single scale) | box AP | 53.1 | #62 of 220 | 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
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