Papers › Sparse R-CNN: End-to-End Object Detection with Learnable Proposals
Sparse R-CNN: End-to-End Object Detection with Learnable Proposals
Peize Sun, Rufeng Zhang, Yi Jiang, Tao Kong, Chenfeng Xu, Wei Zhan, Masayoshi Tomizuka, Lei LI, Zehuan Yuan, Changhu Wang, Ping Luo
We present Sparse R-CNN, a purely sparse method for object detection in images. Existing works on object detection heavily rely on dense object candidates, such as k anchor boxes pre-defined on all grids of image feature map of size H×W. In our method, however, a fixed sparse set of learned object proposals, total length of N, are provided to object recognition head to perform classification and location. By eliminating HWk (up to hundreds of thousands) hand-designed object candidates to N (e.g. 100) learnable proposals, Sparse R-CNN completely avoids all efforts related to object candidates design and many-to-one label assignment. More importantly, final predictions are directly output without non-maximum suppression post-procedure. Sparse R-CNN demonstrates accuracy, run-time and training convergence performance on par with the well-established detector baselines on the challenging COCO dataset, e.g., achieving 45.0 AP in standard 3× training schedule and running at 22 fps using ResNet-50 FPN model. We hope our work could inspire re-thinking the convention of dense prior in object detectors. The code is available at: https://github.com/PeizeSun/SparseR-CNN.
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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 |
|---|---|---|---|---|---|---|---|
| 2D Object Detection | CeyMo | Sparse R-CNN | mAP | 47.3 | #5 of 5 | Archive leaderboard | report |
| 2D Object Detection | SARDet-100K | Sparse R-CNN | box mAP | 38.1 | #12 of 13 | Archive leaderboard | report |
| Object Detection | COCO minival | Sparse R-CNN (ResNet-101, learnable proposals, random crop aug, FPN) | AP50 | 64.6 | #112 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Sparse R-CNN (ResNet-101, learnable proposals, random crop aug, FPN) | AP75 | 49.5 | #112 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Sparse R-CNN (ResNet-101, learnable proposals, random crop aug, FPN) | APL | 61.6 | #112 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Sparse R-CNN (ResNet-101, learnable proposals, random crop aug, FPN) | APM | 48.3 | #112 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Sparse R-CNN (ResNet-101, learnable proposals, random crop aug, FPN) | APS | 28.3 | #112 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Sparse R-CNN (ResNet-101, learnable proposals, random crop aug, FPN) | box AP | 45.6 | #112 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Sparse R-CNN (ResNet-50, learnable proposals, random crop aug, FPN) | AP50 | 63.4 | #128 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Sparse R-CNN (ResNet-50, learnable proposals, random crop aug, FPN) | AP75 | 48.2 | #128 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Sparse R-CNN (ResNet-50, learnable proposals, random crop aug, FPN) | APL | 59.5 | #128 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Sparse R-CNN (ResNet-50, learnable proposals, random crop aug, FPN) | APM | 47.2 | #128 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Sparse R-CNN (ResNet-50, learnable proposals, random crop aug, FPN) | APS | 26.9 | #128 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Sparse R-CNN (ResNet-50, learnable proposals, random crop aug, FPN) | box AP | 44.5 | #128 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Sparse R-CNN (ResNet-101, FPN) | AP50 | 62.1 | #138 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Sparse R-CNN (ResNet-101, FPN) | AP75 | 47.2 | #138 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Sparse R-CNN (ResNet-101, FPN) | APL | 59.7 | #138 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Sparse R-CNN (ResNet-101, FPN) | APM | 46.3 | #138 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Sparse R-CNN (ResNet-101, FPN) | APS | 26.1 | #138 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Sparse R-CNN (ResNet-101, FPN) | box AP | 43.5 | #138 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Sparse R-CNN (ResNet-50, FPN) | AP50 | 61.2 | #155 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Sparse R-CNN (ResNet-50, FPN) | AP75 | 45.7 | #155 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Sparse R-CNN (ResNet-50, FPN) | APL | 57.6 | #155 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Sparse R-CNN (ResNet-50, FPN) | APM | 44.6 | #155 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Sparse R-CNN (ResNet-50, FPN) | APS | 26.7 | #155 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Sparse R-CNN (ResNet-50, FPN) | box AP | 42.3 | #155 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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