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COCO minival 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
The chart needs JavaScript; the table below carries every value.
Direction inferred from the metric name, not from the archive: mask AP (higher is better), Params (M) (lower is better), box AP (higher is better). Not inferred (points only, no best-so-far line): AP50, AP75, APL, APM, APS, GFLOPs. Points are placed at the row's paper date; 93 of 93 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 | 56.6 | 79.7 | 62.8 | 74.6 | 59.7 | 38.9 | ✓ | Paper | Code | 2022 | 0 of 5 ran · 5 unverified | report | |||
| 2 | ViT-CoMer-L (Mask RCNN, DINOv2) | 55.9 | – | Paper | Code | 2024 | linked, not harvested | report | ||||||||
| 3 | InternImage-H | 55.4 | 80.1 | 61.5 | 74.4 | 58.4 | 37.9 | ✓ | Paper | Code | 2022 | 2 of 4 ran · 2 unverified | report | |||
| 4 | EVA | 55.0 | 79.4 | 60.9 | 72.0 | 58.4 | 37.6 | ✓ | Paper | Code | 2022 | 1 of 3 ran · 2 unverified | report | |||
| 5 | Mask Frozen-DETR | 54.9 | 78.9 | 60.8 | 72.9 | 58.4 | 37.2 | ✓ | Paper | – | 2023 | no code linked | report | |||
| 6 | MasK DINO (SwinL, multi-scale) | 54.5 | ✓ | Paper | Code | 2022 | 11 of 13 ran · 2 unverified | report | ||||||||
| 7 | ViT-Adapter-L (HTC++, BEiTv2, O365, multi-scale) | 54.2 | ✓ | Paper | Code | 2022 | linked, not harvested | report | ||||||||
| 8 | GLEE-Pro | 54.2 | ✓ | Paper | Code | 2023 | 8 of 13 ran · 5 unverified | report | ||||||||
| 9 | SwinV2-G (HTC++) | 53.7 | ✓ | Paper | Code | 2021 | 3 of 30 ran · 27 unverified | report | ||||||||
| 10 | ViTDet, ViT-H Cascade (multiscale) | 53.1 | – | Paper | Code | 2022 | 1 of 4 ran · 3 unverified | report | ||||||||
| 11 | GLEE-Plus | 53.0 | ✓ | Paper | Code | 2023 | 8 of 13 ran · 5 unverified | report | ||||||||
| 12 | Mask DINO (SwinL) | 52.6 | – | Paper | Code | 2022 | 11 of 13 ran · 2 unverified | report | ||||||||
| 13 | Soft Teacher + Swin-L(HTC++, multi-scale) | 52.5 | ✓ | Paper | Code | 2021 | linked, not harvested | report | ||||||||
| 14 | ViT-Adapter-L (HTC++, BEiTv2 pretrain, multi-scale) | 52.5 | – | Paper | Code | 2022 | linked, not harvested | report | ||||||||
| 15 | ViT-Adapter-L (HTC++, BEiT pretrain, multi-scale) | 52.2 | – | Paper | Code | 2022 | linked, not harvested | report | ||||||||
| 16 | ViTDet, ViT-H Cascade | 52 | – | Paper | Code | 2022 | 1 of 4 ran · 3 unverified | report | ||||||||
| 17 | Soft Teacher + Swin-L(HTC++, single-scale) | 51.9 | ✓ | Paper | Code | 2021 | linked, not harvested | report | ||||||||
| 18 | CBNetV2 (Dual-Swin-L HTC, multi-scale) | 51.8 | – | Paper | Code | 2021 | 2 of 3 ran · 1 unverified | report | ||||||||
| 19 | Frozen Backbone, SwinV2-G-ext22K (HTC) | 51.6 | – | Paper | – | 2022 | no code linked | report | ||||||||
| 20 | CBNetV2 (Dual-Swin-L HTC, multi-scale) | 51 | – | Paper | Code | 2021 | 2 of 3 ran · 1 unverified | report | ||||||||
| 21 | Focal-L (HTC++, multi-scale) | 50.9 | – | Paper | Code | 2021 | 3 of 3 ran · 0 unverified | report | ||||||||
| 22 | DiNAT-L (single-scale, Mask2Former) | 50.8 | 75.0 | – | Paper | Code | 2022 | linked, not harvested | report | |||||||
| 23 | MViTv2-L (Cascade Mask R-CNN, multi-scale, IN21k pre-train) | 50.5 | – | Paper | Code | 2021 | linked, not harvested | report | ||||||||
| 24 | Swin-L (HTC++, multi scale) | 50.4 | – | Paper | Code | 2021 | 108 of 207 ran · 99 unverified | report | ||||||||
| 25 | MOAT-3 (IN-22K pretraining, single-scale) | 50.3 | – | Paper | Code | 2022 | linked, not harvested | report | ||||||||
| 26 | Mask2Former (Swin-L) | 50.1 | – | Paper | Code | 2021 | 2 of 8 ran · 6 unverified | report | ||||||||
| 27 | Swin-L (HTC++, single scale) | 49.5 | – | Paper | Code | 2021 | 108 of 207 ran · 99 unverified | report | ||||||||
| 28 | MOAT-2 (IN-22K pretraining, single-scale) | 49.3 | – | Paper | Code | 2022 | linked, not harvested | report | ||||||||
| 29 | MOAT-1 (IN-1K pretraining, single-scale) | 49.0 | – | Paper | Code | 2022 | linked, not harvested | report | ||||||||
| 30 | QueryInst (single scale) | 48.9 | 74.0 | 53.9 | 68.3 | 52.6 | 30.8 | – | Paper | Code | 2021 | 1 of 3 ran · 2 unverified | report | |||
| 31 | Cascade Eff-B7 NAS-FPN (1280, self-training Copy Paste, single-scale) | 48.9 | ✓ | Paper | Code | 2020 | 2 of 2 ran · 0 unverified | report | ||||||||
| 32 | InternImage-XL | 48.8 | 1782 | 387 | – | Paper | Code | 2022 | 2 of 4 ran · 2 unverified | report | ||||||
| 33 | CenterNet2 (Swin-L w/ X-Paste + Copy-Paste) | 48.8 | – | Paper | Code | 2022 | 1 of 3 ran · 2 unverified | report | ||||||||
| 34 | Heira-L | 48.6 | – | Paper | Code | 2023 | 0 of 6 ran · 6 unverified | report | ||||||||
| 35 | InternImage-L | 48.5 | 1399 | 277 | 56.1 | – | Paper | Code | 2022 | 2 of 4 ran · 2 unverified | report | |||||
| 36 | MViTv2-H (Cascade Mask R-CNN, single-scale, IN21k pre-train) | 48.5 | – | Paper | Code | 2021 | linked, not harvested | report | ||||||||
| 37 | GLEE-Lite | 48.4 | ✓ | Paper | Code | 2023 | 8 of 13 ran · 5 unverified | report | ||||||||
| 38 | MOAT-0 (IN-1K pretraining, single-scale) | 47.4 | – | Paper | Code | 2022 | linked, not harvested | report | ||||||||
| 39 | MViTv2-L (Cascade Mask R-CNN, single-scale) | 47.1 | – | Paper | Code | 2021 | linked, not harvested | report | ||||||||
| 40 | MPViT-B (Cascade Mask R-CNN, multi-scale, IN1k pre-train) | 47.0 | – | Paper | Code | 2021 | linked, not harvested | report | ||||||||
| 41 | tiny-MOAT-3 (IN-1K pretraining, single-scale) | 47.0 | – | Paper | Code | 2022 | linked, not harvested | report | ||||||||
| 42 | Cascade Eff-B7 NAS-FPN (1280) | 46.8 | – | Paper | Code | 2020 | 2 of 2 ran · 0 unverified | report | ||||||||
| 43 | ResNeSt-200 (multi-scale) | 46.25 | – | Paper | Code | 2020 | 8 of 48 ran · 40 unverified | report | ||||||||
| 44 | MViT-L (Mask R-CNN, single-scale) | 46.2 | – | Paper | Code | 2021 | linked, not harvested | report | ||||||||
| 45 | RetinaNet (SpineNet-190, 1536x1536) | 46.1 | – | Paper | Code | 2019 | linked, not harvested | report | ||||||||
| 46 | MPViT-B (Cascade R-CNN, sinlge-scale, IN-1K pre-train) | 45.8 | – | Paper | Code | 2021 | linked, not harvested | report | ||||||||
| 47 | Mask R-CNN (ViL Base, multi-scale, 3x lr) | 45.7 | 49.9 | – | Paper | Code | 2021 | 6 of 10 ran · 4 unverified | report | |||||||
| 48 | Mask R-CNN (ViL Base, 1x lr) | 45.1 | 67.2 | 49.3 | – | Paper | Code | 2021 | 6 of 10 ran · 4 unverified | report | ||||||
| 49 | tiny-MOAT-2 (IN-1K pretraining, single-scale) | 45.0 | – | Paper | Code | 2022 | linked, not harvested | report | ||||||||
| 50 | GCNet (ResNeXt-101 + DCN + cascade + GC r4) | 44.7 | 67.9 | 48.4 | – | Paper | Code | 2020 | linked, not harvested | report | ||||||
| 51 | tiny-MOAT-1 (IN-1K pretraining, single-scale) | 44.6 | – | Paper | Code | 2022 | linked, not harvested | report | ||||||||
| 52 | InternImage-S | 44.5 | 340 | 69 | 49.7 | – | Paper | Code | 2022 | 2 of 4 ran · 2 unverified | report | |||||
| 53 | ResNeSt-200-DCN (single-scale) | 44.5 | – | Paper | Code | 2020 | 8 of 48 ran · 40 unverified | report | ||||||||
| 54 | ELSA-S (Cascade Mask RCNN) | 44.4 | 67.8 | 47.8 | – | Paper | Code | 2021 | 1 of 1 ran · 0 unverified | report | ||||||
| 55 | BoTNet 200 (Mask R-CNN, single scale, 72 epochs) | 44.4 | – | Paper | Code | 2021 | 26 of 49 ran · 23 unverified | report | ||||||||
| 56 | DaViT-T (Mask R-CNN, 36 epochs) | 44.3 | – | Paper | Code | 2022 | 8 of 15 ran · 7 unverified | report | ||||||||
| 57 | ResNeSt-200 (single-scale) | 44.21 | – | Paper | Code | 2020 | 8 of 48 ran · 40 unverified | report | ||||||||
| 58 | InternImage-T | 43.7 | 270 | 49 | 49.1 | – | Paper | Code | 2022 | 2 of 4 ran · 2 unverified | report | |||||
| 59 | BoTNet 152 (Mask R-CNN, single scale, 72 epochs) | 43.7 | – | Paper | Code | 2021 | 26 of 49 ran · 23 unverified | report | ||||||||
| 60 | XCiT-M24/8 | 43.7 | – | Paper | Code | 2021 | 3 of 14 ran · 11 unverified | report | ||||||||
| 61 | tiny-MOAT-0 (IN-1K pretraining, single-scale) | 43.3 | – | Paper | Code | 2022 | linked, not harvested | report | ||||||||
| 62 | ELSA-S (Mask RCNN) | 43.0 | 67.3 | 46.4 | – | Paper | Code | 2021 | 1 of 1 ran · 0 unverified | report | ||||||
| 63 | XCiT-S24/8 | 43.0 | – | Paper | Code | 2021 | 3 of 14 ran · 11 unverified | report | ||||||||
| 64 | CenterMask-VoVNetV2-99 (multi-scale) | 42.5 | – | Paper | Code | 2019 | 1 of 2 ran · 1 unverified | report | ||||||||
| 65 | ResNeSt-101 (single-scale) | 41.56 | – | Paper | Code | 2020 | 8 of 48 ran · 40 unverified | report | ||||||||
| 66 | SIW | 41.4 | – | Paper | – | 2022 | no code linked | report | ||||||||
| 67 | Res2Net-101+HTC | 41.3 | – | Paper | Code | 2019 | 3 of 9 ran · 6 unverified | report | ||||||||
| 68 | HTC (HRNetV2p-W48) | 41.0 | – | Paper | Code | 2019 | 8 of 25 ran · 17 unverified | report | ||||||||
| 69 | HTC (HRNetV2p-W48) | 41.0 | – | Paper | Code | 2019 | 3 of 34 ran · 31 unverified | report | ||||||||
| 70 | GCNet (ResNeXt-101 + DCN + cascade + GC r16) | 40.9 | – | Paper | Code | 2019 | linked, not harvested | report | ||||||||
| 71 | BoTNet 50 (72 epochs) | 40.7 | – | Paper | Code | 2021 | 26 of 49 ran · 23 unverified | report | ||||||||
| 72 | R3-CNN (ResNet-50-FPN, DCN) | 40.4 | 61.3 | 44 | 56.1 | 43.6 | 22.3 | – | Paper | Code | 2021 | linked, not harvested | report | |||
| 73 | Mask R-CNN (ResNext-152, +1 NL) | 40.3 | – | Paper | Code | 2017 | 3 of 4 ran · 1 unverified | report | ||||||||
| 74 | Mask R-CNN-FPN (AOGNet-40M) | 40.2 | 63.2 | 43.3 | – | Paper | Code | 2019 | linked, not harvested | report | ||||||
| 75 | R3-CNN (ResNet-50-FPN, GC-Net) | 40.2 | 61.1 | 43.5 | 42.8 | 22.6 | – | Paper | Code | 2021 | linked, not harvested | report | ||||
| 76 | CenterMask-VoVNetV2-99-3x | 40.2 | – | Paper | Code | 2019 | 1 of 2 ran · 1 unverified | report | ||||||||
| 77 | R3-CNN (ResNet-50-FPN, GRoIE) | 39.1 | 58.8 | 42.3 | 54.3 | 42.1 | 20.7 | – | Paper | Code | 2021 | linked, not harvested | report | |||
| 78 | Mask Scoring R-CNN (ResNet-101-FPN-DCN) | 39.1 | – | Paper | Code | 2019 | 1 of 3 ran · 2 unverified | report | ||||||||
| 79 | Mask R-CNN-FPN (ResNeXt-101, GN+WS) | 38.34 | 61.07 | 40.82 | 56.08 | 41.73 | 18.32 | – | Paper | Code | 2019 | linked, not harvested | report | |||
| 80 | R3-CNN (ResNet-50-FPN) | 38.2 | 58 | 41.4 | 52.8 | 41 | 20.4 | – | Paper | Code | 2021 | linked, not harvested | report | |||
| 81 | HTC (ResNet-50) | 38.2 | – | Paper | Code | 2019 | 0 of 11 ran · 11 unverified | report | ||||||||
| 82 | Mask Scoring R-CNN (ResNet-101 FPN) | 38.2 | – | Paper | Code | 2019 | 1 of 3 ran · 2 unverified | report | ||||||||
| 83 | PANet (ResNet-50) | 37.8 | – | Paper | Code | 2018 | 1 of 4 ran · 3 unverified | report | ||||||||
| 84 | GCnet (ResNet-50-FPN, GRoIE) | 37.2 | 59.3 | 39.8 | 51.2 | 41 | 20.2 | – | Paper | Code | 2020 | linked, not harvested | report | |||
| 85 | Mask R-CNN (FPN, X-volution, SA) | 37.2 | 53.1 | 40 | 19.2 | – | Paper | – | 2021 | no code linked | report | |||||
| 86 | Mask R-CNN (ResNet-101, +1 NL) | 37.1 | – | Paper | Code | 2017 | 3 of 4 ran · 1 unverified | report | ||||||||
| 87 | Mask Scoring R-CNN (ResNet-50 FPN) | 36.0 | – | Paper | Code | 2019 | 1 of 3 ran · 2 unverified | report | ||||||||
| 88 | Mask R-CNN (ResNet-50-FPN, GRoIE) | 35.8 | 57.1 | 38.0 | 48.7 | 39 | 19.1 | – | Paper | Code | 2020 | linked, not harvested | report | |||
| 89 | Faster R-CNN (Res2Net-50) | 35.6 | 57.6 | 53.7 | 37.9 | 15.7 | – | Paper | Code | 2019 | 3 of 9 ran · 6 unverified | report | ||||
| 90 | Mask R-CNN (ResNet-50, +1 NL) | 35.5 | – | Paper | Code | 2017 | 3 of 4 ran · 1 unverified | report | ||||||||
| 91 | Mask R-CNN (ResNet-50, ACNet) | 35.2 | – | Paper | Code | 2019 | linked, not harvested | report | ||||||||
| 92 | YOLACT-550 (ResNet-50) | 29.9 | – | Paper | Code | 2019 | 10 of 21 ran · 11 unverified | report | ||||||||
| 93 | InternImage-B | 501 | 115 | – | Paper | Code | 2022 | 2 of 4 ran · 2 unverified | report |
All 93 rows shown. 93 link to a paper page on this site; 13 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). 56 rows have a graph line, from 30 distinct papers; 53 rows (27 papers) have at least one sample that ran. Counting each paper once: Syntology ran 227 of 557 samples; 330 unverified. Separately, 133 of those 557 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.
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