Papers › Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, Tsung-Yi Lin, Ekin D. Cubuk, Quoc V. Le, Barret Zoph
Building instance segmentation models that are data-efficient and can handle rare object categories is an important challenge in computer vision. Leveraging data augmentations is a promising direction towards addressing this challenge. Here, we perform a systematic study of the Copy-Paste augmentation ([13, 12]) for instance segmentation where we randomly paste objects onto an image. Prior studies on Copy-Paste relied on modeling the surrounding visual context for pasting the objects. However, we find that the simple mechanism of pasting objects randomly is good enough and can provide solid gains on top of strong baselines. Furthermore, we show Copy-Paste is additive with semi-supervised methods that leverage extra data through pseudo labeling (e.g. self-training). On COCO instance segmentation, we achieve 49.1 mask AP and 57.3 box AP, an improvement of +0.6 mask AP and +1.5 box AP over the previous state-of-the-art. We further demonstrate that Copy-Paste can lead to significant improvements on the LVIS benchmark. Our baseline model outperforms the LVIS 2020 Challenge winning entry by +3.6 mask AP on rare categories.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Instance Segmentation | COCO minival | Cascade Eff-B7 NAS-FPN (1280, self-training Copy Paste, single-scale) | mask AP | 48.9 | #31 of 93 | Archive leaderboard | report |
| Instance Segmentation | COCO minival | Cascade Eff-B7 NAS-FPN (1280) | mask AP | 46.8 | #42 of 93 | Archive leaderboard | report |
| Instance Segmentation | COCO test-dev | Cascade Eff-B7 NAS-FPN (1280, self-training Copy Paste, single-scale) | mask AP | 49.1 | #25 of 112 | Archive leaderboard | report |
| Instance Segmentation | COCO test-dev | Cascade Eff-B7 NAS-FPN (1280) | mask AP | 46.9 | #34 of 112 | Archive leaderboard | report |
| Instance Segmentation | LVIS v1.0 val | Eff-B7 NAS-FPN (1280, Copy-Paste pre-training)) | mask AP | 38.1 | #11 of 25 | Archive leaderboard | report |
| Object Detection | COCO minival | Cascade Eff-B7 NAS-FPN (1280, self-training Copy Paste, single-scale) | box AP | 57.0 | #43 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Cascade Eff-B7 NAS-FPN (1280) | box AP | 54.5 | #55 of 220 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade Eff-B7 NAS-FPN (1280, self-training Copy Paste, single-scale) | box mAP | 57.3 | #37 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Cascade Eff-B7 NAS-FPN (1280) | box mAP | 54.8 | #52 of 225 | Archive leaderboard | report |
| Object Detection | LVIS v1.0 val | Eff-B7 NAS-FPN (1280, Copy-Paste pre-training)) | box AP | 41.6 | #13 of 15 | Archive leaderboard | report |
| Object Detection | PASCAL VOC 2007 | Cascade Eff-B7 NAS-FPN (Copy Paste pre-training, single-scale) | MAP | 89.3% | #1 of 30 | Archive leaderboard | report |
| Semantic Segmentation | PASCAL VOC 2012 val | Eff-B7 NAS-FPN (Copy-Paste pre-training, single-scale)) | mIoU | 86.6% | #3 of 29 | 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: Copy-Paste
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