Methods › Computer Vision › One-Stage Object Detection Models › RetinaNet-RS
RetinaNet-RS
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
RetinaNet-RS is an object detection model produced through a model scaling method based on changing the the input resolution and ResNet backbone depth. For RetinaNet, we scale up input resolution from 512 to 768 and the ResNet backbone depth from 50 to 152. As RetinaNet performs dense one-stage object detection, the authors find scaling up input resolution leads to large resolution feature maps hence more anchors to process. This results in a higher capacity dense prediction heads and expensive NMS. Scaling stops at input resolution 768 × 768 for RetinaNet.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Optimizing Anchor-based Detectors for Autonomous Driving Scenes 11 Aug 2022 · 0 repositories · arXiv:2208.06062
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Simple Training Strategies and Model Scaling for Object Detection 30 Jun 2021 · 1 repository · arXiv:2107.00057
Tasks archive 2025-07-28
7 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Autonomous Driving | 1 |
| GPU | 1 |
| Instance Segmentation | 1 |
| Object | 1 |
| Object Detection | 1 |
| Semantic Segmentation | 1 |
| object-detection | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections