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COCO test-dev Benchmark (Instance Segmentation)
Instance Segmentation is a computer vision task that involves identifying and separating individual objects within an image, including detecting the boundaries of each object and assigning a unique label to each object. The goal of instance segmentation is to produce a pixel-wise segmentation map of the image, where each pixel is assigned to a specific object instance.
The archive carries no text for this table; the description above is the archive's text for the task Instance Segmentation. archive 2025-07-28
Over time archive 2025-07-28
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Direction inferred from the metric name, not from the archive: mask AP (higher is better). Not inferred (points only, no best-so-far line): AP50, AP75, APS, APM, APL. Points are placed at the row's paper date; 112 of 112 rows carry one.
Results archive 2025-07-28
Archive rows end at the archive snapshot, 2025-07-28: no result published after that date is in this table. Rank is the archive's row order at that snapshot; not re-ranked here. Metric values are the archive's strings. Column headers sort the table in your browser; each row keeps its archive rank.
| Paper | Code | Ran Syntology | Report | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Co-DETR | 57.1 | 80.2 | 63.4 | 41.6 | 60.1 | 72.0 | ✓ | Paper | Code | 2022 | 0 of 5 ran · 5 unverified | report |
| 2 | CBNetV2 (EVA02, single-scale) | 56.1 | 80.3 | 62.1 | 39.7 | 59.3 | 70.9 | ✓ | Paper | Code | 2021 | 2 of 3 ran · 1 unverified | report |
| 3 | EVA | 55.5 | 80.0 | 36.3 | 58.0 | 72.4 | ✓ | Paper | Code | 2022 | 1 of 3 ran · 2 unverified | report | |
| 4 | FD-SwinV2-G | 55.4 | ✓ | Paper | Code | 2022 | 2 of 8 ran · 6 unverified | report | |||||
| 5 | Mask Frozen-DETR | 55.3 | 79.3 | 61.4 | 37.8 | 58.4 | 70.4 | ✓ | Paper | – | 2023 | no code linked | report |
| 6 | BEiT-3 | 54.8 | – | Paper | Code | 2022 | linked, not harvested | report | |||||
| 7 | MasK DINO (SwinL, multi-scale) | 54.7 | ✓ | Paper | Code | 2022 | 11 of 13 ran · 2 unverified | report | |||||
| 8 | ViT-Adapter-L (HTC++, BEiTv2, O365, multi-scale) | 54.5 | ✓ | Paper | Code | 2022 | linked, not harvested | report | |||||
| 9 | GLEE-Pro | 54.5 | ✓ | Paper | Code | 2023 | 8 of 13 ran · 5 unverified | report | |||||
| 10 | SwinV2-G (HTC++) | 54.4 | ✓ | Paper | Code | 2021 | 3 of 30 ran · 27 unverified | report | |||||
| 11 | GLEE-Plus | 53.3 | ✓ | Paper | Code | 2023 | 8 of 13 ran · 5 unverified | report | |||||
| 12 | Soft Teacher + Swin-L (HTC++, multi-scale) | 53.0 | ✓ | Paper | Code | 2021 | linked, not harvested | report | |||||
| 13 | ViT-Adapter-L (HTC++, BEiTv2 pretrain, multi-scale) | 53.0 | – | Paper | Code | 2022 | linked, not harvested | report | |||||
| 14 | Mask DINO (SwinL, single -scale) | 52.8 | – | Paper | Code | 2022 | 11 of 13 ran · 2 unverified | report | |||||
| 15 | ViT-Adapter-L (HTC++, BEiT pretrain, multi-scale) | 52.5 | – | Paper | Code | 2022 | linked, not harvested | report | |||||
| 16 | CBNetV2 (Dual-Swin-L HTC, multi-scale) | 52.3 | – | Paper | Code | 2021 | 2 of 3 ran · 1 unverified | report | |||||
| 17 | UNINEXT-H | 51.8 | 76.2 | 56.7 | 33.3 | 55.9 | 67.5 | ✓ | Paper | Code | 2023 | 3 of 4 ran · 1 unverified | report |
| 18 | CBNetV2 (Dual-Swin-L HTC, single-scale) | 51.6 | – | Paper | Code | 2021 | 2 of 3 ran · 1 unverified | report | |||||
| 19 | Focal-L (HTC++, multi-scale) | 51.3 | 75.4 | 56.5 | 35.6 | 64.2 | – | Paper | Code | 2021 | 3 of 3 ran · 0 unverified | report | |
| 20 | Swin-L (HTC++, multi scale) | 51.1 | – | Paper | Code | 2021 | 108 of 207 ran · 99 unverified | report | |||||
| 21 | Mask2Former (Swin-L, single scale) | 50.5 | 74.9 | 54.9 | 29.1 | 53.8 | 71.2 | – | Paper | Code | 2021 | 2 of 8 ran · 6 unverified | report |
| 22 | Swin-L (HTC++, single scale) | 50.2 | – | Paper | Code | 2021 | 108 of 207 ran · 99 unverified | report | |||||
| 23 | ISTR-SMT (Swin-L, single scale) | 49.7 | – | Paper | Code | 2021 | linked, not harvested | report | |||||
| 24 | QueryInst (single scale) | 49.1 | 74.2 | 53.8 | 31.5 | 51.8 | 63.2 | – | Paper | Code | 2021 | 1 of 3 ran · 2 unverified | report |
| 25 | Cascade Eff-B7 NAS-FPN (1280, self-training Copy Paste, single-scale) | 49.1 | ✓ | Paper | Code | 2020 | 2 of 2 ran · 0 unverified | report | |||||
| 26 | dBOT ViT-L (CLIP) | 48.8 | – | Paper | Code | 2022 | 5 of 13 ran · 8 unverified | report | |||||
| 27 | MogaNet-XL (Cascade Mask R-CNN) | 48.8 | – | Paper | Code | 2022 | 12 of 15 ran · 3 unverified | report | |||||
| 28 | DetectoRS (ResNeXt-101-64x4d, multi-scale) | 48.5 | 72.0 | 53.3 | 31.6 | 50.9 | 61.5 | – | Paper | Code | 2020 | 0 of 4 ran · 4 unverified | report |
| 29 | dBOT ViT-L | 48.3 | – | Paper | Code | 2022 | 5 of 13 ran · 8 unverified | report | |||||
| 30 | DiffusionInst-SwinL | 48.3 | – | Paper | Code | 2022 | 11 of 16 ran · 5 unverified | report | |||||
| 31 | GLEE-Lite | 48.3 | ✓ | Paper | Code | 2023 | 8 of 13 ran · 5 unverified | report | |||||
| 32 | DiffusionInst-SwinB | 47.6 | – | Paper | Code | 2022 | 11 of 16 ran · 5 unverified | report | |||||
| 33 | DetectoRS (ResNeXt-101-32x4d, multi-scale) | 47.1 | 71.1 | 51.6 | 30.3 | 49.5 | 59.6 | – | Paper | Code | 2020 | 0 of 4 ran · 4 unverified | report |
| 34 | Cascade Eff-B7 NAS-FPN (1280) | 46.9 | – | Paper | Code | 2020 | 2 of 2 ran · 0 unverified | report | |||||
| 35 | SOLQ (Swin-L, single scale) | 46.7 | – | Paper | Code | 2021 | linked, not harvested | report | |||||
| 36 | dBOT ViT-B | 46.3 | – | Paper | Code | 2022 | 5 of 13 ran · 8 unverified | report | |||||
| 37 | dBOT ViT-B (CLIP) | 46.2 | – | Paper | Code | 2022 | 5 of 13 ran · 8 unverified | report | |||||
| 38 | Mask R-CNN (SpineNet-190, 1536x1536) | 46.1 | – | Paper | Code | 2019 | linked, not harvested | report | |||||
| 39 | MogaNet-L (Cascade Mask R-CNN) | 46.1 | – | Paper | Code | 2022 | 12 of 15 ran · 3 unverified | report | |||||
| 40 | MogaNet-B (Cascade Mask R-CNN) | 46 | – | Paper | Code | 2022 | 12 of 15 ran · 3 unverified | report | |||||
| 41 | Swin-B + Cascade Mask R-CNN (tri-layer modelling) | 45.9 | – | Paper | Code | 2022 | linked, not harvested | report | |||||
| 42 | GCNet (ResNeXt-101 + DCN + cascade + GC r4) | 45.4 | 68.9 | 49.6 | – | Paper | Code | 2020 | linked, not harvested | report | |||
| 43 | MogaNet-S (Cascade Mask R-CNN) | 45.1 | – | Paper | Code | 2022 | 12 of 15 ran · 3 unverified | report | |||||
| 44 | gSwin-S | 45.03 | – | Paper | – | 2022 | no code linked | report | |||||
| 45 | iBOT (ViT-B/16) | 44.2 | ✓ | Paper | Code | 2021 | 0 of 1 ran · 1 unverified | report | |||||
| 46 | gSwin-T | 44.16 | – | Paper | – | 2022 | no code linked | report | |||||
| 47 | MogaNet-L (Mask R-CNN 1x) | 44.1 | – | Paper | Code | 2022 | 12 of 15 ran · 3 unverified | report | |||||
| 48 | A2MIM (ViT-B) | 43.5 | – | Paper | Code | 2022 | linked, not harvested | report | |||||
| 49 | Cascade Mask R-CNN (ResNeXt152, CBNet) | 43.3 | – | Paper | Code | 2019 | 0 of 4 ran · 4 unverified | report | |||||
| 50 | MogaNet-B (Mask R-CNN 1x) | 43.2 | – | Paper | Code | 2022 | 12 of 15 ran · 3 unverified | report | |||||
| 51 | ResNeSt101 | 43% | – | Paper | Code | 2020 | 8 of 48 ran · 40 unverified | report | |||||
| 52 | gSwin-VT | 42.87 | – | Paper | – | 2022 | no code linked | report | |||||
| 53 | iBOT (ViT-S/16) | 42.6 | ✓ | Paper | Code | 2021 | 0 of 1 ran · 1 unverified | report | |||||
| 54 | Mask Transfiner(ResNet101-FPN) | 42.2 | – | Paper | Code | 2021 | 1 of 1 ran · 0 unverified | report | |||||
| 55 | MogaNet-S (Mask R-CNN 1x) | 42.2 | – | Paper | Code | 2022 | 12 of 15 ran · 3 unverified | report | |||||
| 56 | PANet | 42.0 | – | Paper | Code | 2018 | 1 of 4 ran · 3 unverified | report | |||||
| 57 | CenterMask + VoVNet99 | 41.8 | 24.4 | 44.4 | 54.3 | – | Paper | Code | 2019 | 1 of 2 ran · 1 unverified | report | ||
| 58 | SOLOv2(Res-DCN-101-FPN) | 41.7 | 63.2 | 45.1 | 18.0 | 45.0 | 61.6 | – | Paper | Code | 2020 | 15 of 38 ran · 23 unverified | report |
| 59 | BCNet(ResNeXt-101 + FPN+ FCOS) | 41.7 | – | Paper | Code | 2021 | 7 of 12 ran · 5 unverified | report | |||||
| 60 | GCNet (ResNeXt-101 + DCN + cascade + GC r16) | 41.5% | – | Paper | Code | 2019 | linked, not harvested | report | |||||
| 61 | DiffusionInst-ResNet101 | 41.5 | – | Paper | Code | 2022 | 11 of 16 ran · 5 unverified | report | |||||
| 62 | BlendMask (ResNet-101 + DCN interval=3) | 41.3 | 63.1 | 44.6 | 22.7 | 44.1 | 54.5 | – | Paper | Code | 2020 | linked, not harvested | report |
| 63 | HTC + ResNeXt-101-FPN + DCN | 41.2 | ✓ | Paper | Code | 2019 | 0 of 11 ran · 11 unverified | report | |||||
| 64 | HTC + ResNeXt-101-FPN | 41.2% | – | Paper | Code | 2019 | 0 of 11 ran · 11 unverified | report | |||||
| 65 | SOLQ (ResNet101, single scale) | 40.9 | – | Paper | Code | 2021 | linked, not harvested | report | |||||
| 66 | VoVNetV1-57 | 40.8% | – | Paper | Code | 2019 | linked, not harvested | report | |||||
| 67 | K-Net-N256 (ResNet-101) | 40.6% | 63.3 | 18.8 | 43.3 | 59 | – | Paper | Code | 2021 | 0 of 1 ran · 1 unverified | report | |
| 68 | CenterMask + VoVNetV2-99 (single-scale) | 40.6 | 62.3 | 44.1 | 20.1 | 42.8 | 57.0 | – | Paper | Code | 2019 | 1 of 2 ran · 1 unverified | report |
| 69 | SOLO(Res-DCN-101-FPN) | 40.4 | 62.7 | 43.3 | 17.6 | 43.3 | 58.9 | – | Paper | Code | 2019 | linked, not harvested | report |
| 70 | SOLO (ResNet-DCN-101-FPN) | 40.4% | 62.7% | 43.3% | 17.6% | 43.3% | 58.9% | – | Paper | Code | 2019 | linked, not harvested | report |
| 71 | D2Det (ResNet-101, single-scale test) | 40.2 | 61.5 | 43.7 | 21.7 | 43.0 | 54.0 | – | Paper | Code | 2020 | linked, not harvested | report |
| 72 | K-Net (ResNet-101) | 40.1% | 62.8 | 18.7 | 42.7 | 58.8 | – | Paper | Code | 2021 | 0 of 1 ran · 1 unverified | report | |
| 73 | ISTR (ResNet101-FPN-3x, single-scale) | 39.9% | 22.8 | 41.9 | 52.3 | – | Paper | Code | 2021 | linked, not harvested | report | ||
| 74 | BCNet(ResNet-101-FPN + Faster RCNN) | 39.8 | 61.5 | 43.1 | 22.7 | 42.4 | 51.1 | – | Paper | Code | 2021 | 7 of 12 ran · 5 unverified | report |
| 75 | VoVNetV1-39 | 39.7% | – | Paper | Code | 2019 | linked, not harvested | report | |||||
| 76 | SOLQ (ResNet50, single scale) | 39.7 | – | Paper | Code | 2021 | linked, not harvested | report | |||||
| 77 | CenterMask + X101-32x8d (single-scale) | 39.6 | 61.2 | 42.9 | 19.7 | – | Paper | Code | 2019 | 1 of 2 ran · 1 unverified | report | ||
| 78 | BCNet(ResNet-101-FPN + FCOS) | 39.6 | 61.2 | 42.7 | 22.3 | 42.3 | 51.0 | – | Paper | Code | 2021 | 7 of 12 ran · 5 unverified | report |
| 79 | MS R-CNN + ResNet-101 DCN + FPN | 39.6% | – | Paper | Code | 2019 | 1 of 3 ran · 2 unverified | report | |||||
| 80 | Cascade R-CNN (ResNet-101-FPN, map-guided) | 39.5% | 61.4% | 42.9% | 21.2% | 42.5% | 52.1% | – | Paper | Code | 2019 | linked, not harvested | report |
| 81 | CPMask | 39.2 | 60.8 | 42.2 | 22.2 | 41.8 | 50.1 | – | Paper | Code | 2020 | linked, not harvested | report |
| 82 | MogaNet-T (Mask R-CNN 1x) | 39.1 | – | Paper | Code | 2022 | 12 of 15 ran · 3 unverified | report | |||||
| 83 | PolarMask++ (ResNeXt-101-DCN) | 38.7 | 64.1 | 40.0 | 22.2 | 40.2 | 52.0 | ✓ | Paper | Code | 2021 | linked, not harvested | report |
| 84 | ISDA (ours) | 38.7 | 62 | 41.1 | 17 | 41.2 | – | Paper | Code | 2022 | linked, not harvested | report | |
| 85 | ISTR (ResNet50-FPN-3x, single-scale) | 38.6% | 22.1 | 40.4 | 50.6 | – | Paper | Code | 2021 | linked, not harvested | report | ||
| 86 | CenterMask + ResNet-101-FPN | 38.3 | – | Paper | Code | 2019 | 1 of 2 ran · 1 unverified | report | |||||
| 87 | SipMask (ResNet-101, single-scale test) | 38.1 | 60.2 | 40.8 | 17.8 | 40.8 | 54.3 | – | Paper | Code | 2020 | linked, not harvested | report |
| 88 | MaskLab+ (ResNet-101, JFT) | 38.1% | ✓ | Paper | – | 2017 | no code linked | report | |||||
| 89 | EmbedMask (ResNet-101-FPN) | 37.7% | 59.1% | 40.3% | 17.9% | 40.4% | 53% | – | Paper | Code | 2019 | linked, not harvested | report |
| 90 | EmbedMask(R-101-FPN) | 37.7 | 59.1 | 40.3 | 17.9 | 40.4 | – | Paper | Code | 2019 | linked, not harvested | report | |
| 91 | MogaNet-XT | 37.6 | – | Paper | Code | 2022 | 12 of 15 ran · 3 unverified | report | |||||
| 92 | TensorMask (ResNet-101-FPN) | 37.3% | – | Paper | Code | 2019 | linked, not harvested | report | |||||
| 93 | Mask R-CNN (ResNeXt-101-FPN) | 37.1 | 60.0 | 39.4 | 16.9 | 39.9 | 53.5 | – | Paper | Code | 2017 | 42 of 140 ran · 98 unverified | report |
| 94 | DiffusionInst-ResNet50 | 37.1 | – | Paper | Code | 2022 | 11 of 16 ran · 5 unverified | report | |||||
| 95 | VirTex Mask R-CNN (ResNet-50-FPN) | 36.9 | 58.4 | 39.7 | – | Paper | Code | 2020 | 0 of 1 ran · 1 unverified | report | |||
| 96 | RDSNet (data aug) | 36.4% | 57.9% | 39.0% | 16.4% | 39.5% | 51.6% | – | Paper | Code | 2019 | linked, not harvested | report |
| 97 | MogaNet-T | 35.8 | – | Paper | Code | 2022 | 12 of 15 ran · 3 unverified | report | |||||
| 98 | A2MIM (ResNet-50 2x) | 34.9 | – | Paper | Code | 2022 | linked, not harvested | report | |||||
| 99 | E2EC DLA-34 | 33.8 | 52.9 | 35.9 | – | Paper | Code | 2022 | 8 of 10 ran · 2 unverified | report | |||
| 100 | FCIS+++ +OHEM | 33.6% | 54.5% | – | Paper | Code | 2016 | 0 of 5 ran · 5 unverified | report | ||||
| 101 | Mask R-CNN (Bottleneck-injected ResNet-50, FPN) | 33.6 | – | Paper | Code | 2020 | 10 of 26 ran · 16 unverified | report | |||||
| 102 | PolarMask (ResNeXt-101-FPN) | 32.9% | 55.4% | 33.8% | 15.5% | 35.1% | 46.3% | – | Paper | Code | 2019 | 0 of 1 ran · 1 unverified | report |
| 103 | PolarMask (ResNet-101-FPN) | 30.4% | 51.9% | 31% | 13.4% | 32.4% | 42.8% | – | Paper | Code | 2019 | 0 of 1 ran · 1 unverified | report |
| 104 | YOLACT (ResNet-50-FPN) | 29.8% | – | Paper | Code | 2019 | 10 of 21 ran · 11 unverified | report | |||||
| 105 | FCIS +OHEM | 29.2% | 49.5% | 7.1% | 31.3% | 50.0% | – | Paper | Code | 2016 | 0 of 5 ran · 5 unverified | report | |
| 106 | MultiPath Network | 25.0% | – | Paper | Code | 2016 | linked, not harvested | report | |||||
| 107 | InternImage-H | 80.8 | 62.2 | 41.0 | 58.9 | 70.3 | ✓ | Paper | Code | 2022 | 2 of 4 ran · 2 unverified | report | |
| 108 | ResNeSt-200 (multi-scale) | 70.2 | 51.5 | 30.0 | 49.6 | 60.6 | – | Paper | Code | 2020 | 8 of 48 ran · 40 unverified | report | |
| 109 | CenterMask + VoVNetV2-99 (multi-scale) | 66.2 | 47.4 | 27.2 | – | Paper | Code | 2019 | 1 of 2 ran · 1 unverified | report | |||
| 110 | CenterMask + VoVNetV2-57 (single-scale) | 60.8 | 19.4 | 41.7 | – | Paper | Code | 2019 | 1 of 2 ran · 1 unverified | report | |||
| 111 | MNC | 44.3% | – | Paper | Code | 2015 | linked, not harvested | report | |||||
| 112 | ISDA (ResNet-50) | 55.7 | – | Paper | Code | 2022 | linked, not harvested | report |
All 112 rows shown. 112 link to a paper page on this site; 20 are marked as using additional training data in the archive. No GitHub stars are tracked; "Code" is the first repository the archive lists for the row. The archive carries no row tags, review links or community-submitted rows for this table; none are shown. archive 2025-07-28
Syntology Ran reads "N of M ran · U unverified": of the M code samples Syntology harvested from repositories linked to that row's paper (joined by arXiv id), N executed on a synthesized input and the other U = M−N are unverified (harvested, no recorded run). It counts code from repositories linked to that row's paper, not this result: the row's number was not reproduced and nothing here is a correctness claim. The other cell texts mean no graph line for the row: "linked, not harvested" (the archive links code, Syntology has not harvested it), "no code linked" (no code link in the archive), "not matched" (the row's paper URL matched no paper on this site). 72 rows have a graph line, from 36 distinct papers; 57 rows (27 papers) have at least one sample that ran. Counting each paper once: Syntology ran 280 of 683 samples; 403 unverified. Separately, 160 of those 683 are pointer-only (licence): the site points at that code rather than redistributing it, a licence property recorded for ran and unverified samples alike; each cell's tooltip carries the row's own pointer-only count. Read from the graph 2026-09-24. Per-sample status is on the paper page.
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