paper-with-me

Object Detection 벤치마크

Object Detection on COCO minival

1100개 결과 · ⬇ CSV · JSON

box AP

33.2 41.4 49.6 57.8 66 2015-12 2026-09 Cascade Mask R-CNN (ResNet-50) — 46.3 (2015-12-10) GFL (ResNet-50) — 44.5 (2015-12-10) ATSS (ResNet-50) — 43.5 (2015-12-10) Cascade Mask R-CNN (ResNet-50) — 46.3 (2015-12-10) GFL (ResNet-50) — 44.5 (2015-12-10) ATSS (ResNet-50) — 43.5 (2015-12-10) Cascade Mask R-CNN (ResNet-50) — 46.3 (2015-12-10) GFL (ResNet-50) — 44.5 (2015-12-10) ATSS (ResNet-50) — 43.5 (2015-12-10) Cascade Mask R-CNN (ResNet-50) — 46.3 (2015-12-10) GFL (ResNet-50) — 44.5 (2015-12-10) ATSS (ResNet-50) — 43.5 (2015-12-10) Cascade Mask R-CNN (ResNet-50) — 46.3 (2015-12-10) GFL (ResNet-50) — 44.5 (2015-12-10) ATSS (ResNet-50) — 43.5 (2015-12-10) FPN+ — 39.8 (2016-12-09) FPN+ — 39.8 (2016-12-09) FPN+ — 39.8 (2016-12-09) FPN+ — 39.8 (2016-12-09) FPN+ — 39.8 (2016-12-09) Mask R-CNN (ResNet-101-FPN) — 40.0 (2017-03-20) Mask R-CNN (ResNet-50-FPN) — 37.7 (2017-03-20) Mask R-CNN (ResNeXt-101-FPN) — 36.7 (2017-03-20) Mask R-CNN (ResNet-101-FPN) — 40.0 (2017-03-20) Mask R-CNN (ResNet-50-FPN) — 37.7 (2017-03-20) Mask R-CNN (ResNeXt-101-FPN) — 36.7 (2017-03-20) Mask R-CNN (ResNet-101-FPN) — 40.0 (2017-03-20) Mask R-CNN (ResNet-50-FPN) — 37.7 (2017-03-20) Mask R-CNN (ResNeXt-101-FPN) — 36.7 (2017-03-20) Mask R-CNN (ResNet-101-FPN) — 40.0 (2017-03-20) Mask R-CNN (ResNet-50-FPN) — 37.7 (2017-03-20) Mask R-CNN (ResNeXt-101-FPN) — 36.7 (2017-03-20) Mask R-CNN (ResNet-101-FPN) — 40.0 (2017-03-20) Mask R-CNN (ResNet-50-FPN) — 37.7 (2017-03-20) Mask R-CNN (ResNeXt-101-FPN) — 36.7 (2017-03-20) Mask R-CNN (ResNeXt-152 + 1 NL) — 45.0 (2017-11-21) Mask R-CNN (ResNet-101 + 1 NL) — 40.8 (2017-11-21) Mask R-CNN (ResNet-50 + 1 NL) — 39.0 (2017-11-21) Mask R-CNN (ResNeXt-152 + 1 NL) — 45.0 (2017-11-21) Mask R-CNN (ResNet-101 + 1 NL) — 40.8 (2017-11-21) Mask R-CNN (ResNet-50 + 1 NL) — 39.0 (2017-11-21) Mask R-CNN (ResNeXt-152 + 1 NL) — 45.0 (2017-11-21) Mask R-CNN (ResNet-101 + 1 NL) — 40.8 (2017-11-21) Mask R-CNN (ResNet-50 + 1 NL) — 39.0 (2017-11-21) Mask R-CNN (ResNeXt-152 + 1 NL) — 45.0 (2017-11-21) Mask R-CNN (ResNet-101 + 1 NL) — 40.8 (2017-11-21) Mask R-CNN (ResNet-50 + 1 NL) — 39.0 (2017-11-21) Mask R-CNN (ResNeXt-152 + 1 NL) — 45.0 (2017-11-21) Mask R-CNN (ResNet-101 + 1 NL) — 40.8 (2017-11-21) Mask R-CNN (ResNet-50 + 1 NL) — 39.0 (2017-11-21) Cascade R-CNN (ResNet-101-FPN+, cascade) — 42.7 (2017-12-03) Cascade R-CNN (ResNet-50-FPN+) — 40.3 (2017-12-03) Cascade R-CNN (ResNet-101-FPN+, cascade) — 42.7 (2017-12-03) Cascade R-CNN (ResNet-50-FPN+) — 40.3 (2017-12-03) Cascade R-CNN (ResNet-101-FPN+, cascade) — 42.7 (2017-12-03) Cascade R-CNN (ResNet-50-FPN+) — 40.3 (2017-12-03) Cascade R-CNN (ResNet-101-FPN+, cascade) — 42.7 (2017-12-03) Cascade R-CNN (ResNet-50-FPN+) — 40.3 (2017-12-03) Cascade R-CNN (ResNet-101-FPN+, cascade) — 42.7 (2017-12-03) Cascade R-CNN (ResNet-50-FPN+) — 40.3 (2017-12-03) Mask R-CNN (ResNet-101-FPN, GroupNorm, long) — 42.3 (2018-03-22) Mask R-CNN (ResNet-50-FPN, GroupNorm, long) — 40.8 (2018-03-22) Mask R-CNN (ResNet-50-FPN, GroupNorm) — 40.3 (2018-03-22) Mask R-CNN (ResNet-101-FPN, GroupNorm, long) — 42.3 (2018-03-22) Mask R-CNN (ResNet-50-FPN, GroupNorm, long) — 40.8 (2018-03-22) Mask R-CNN (ResNet-50-FPN, GroupNorm) — 40.3 (2018-03-22) Mask R-CNN (ResNet-101-FPN, GroupNorm, long) — 42.3 (2018-03-22) Mask R-CNN (ResNet-50-FPN, GroupNorm, long) — 40.8 (2018-03-22) Mask R-CNN (ResNet-50-FPN, GroupNorm) — 40.3 (2018-03-22) Mask R-CNN (ResNet-101-FPN, GroupNorm, long) — 42.3 (2018-03-22) Mask R-CNN (ResNet-50-FPN, GroupNorm, long) — 40.8 (2018-03-22) Mask R-CNN (ResNet-50-FPN, GroupNorm) — 40.3 (2018-03-22) Mask R-CNN (ResNet-101-FPN, GroupNorm, long) — 42.3 (2018-03-22) Mask R-CNN (ResNet-50-FPN, GroupNorm, long) — 40.8 (2018-03-22) Mask R-CNN (ResNet-50-FPN, GroupNorm) — 40.3 (2018-03-22) CornerNet511 (Hourglass-104) — 38.4 (2018-08-03) CornerNet511 (Hourglass-104) — 38.4 (2018-08-03) CornerNet511 (Hourglass-104) — 38.4 (2018-08-03) CornerNet511 (Hourglass-104) — 38.4 (2018-08-03) CornerNet511 (Hourglass-104) — 38.4 (2018-08-03) M2Det (ResNet-1o1, 320x320) — 34.1 (2018-11-12) M2Det (VGG-16, 320x320) — 33.2 (2018-11-12) M2Det (ResNet-1o1, 320x320) — 34.1 (2018-11-12) M2Det (VGG-16, 320x320) — 33.2 (2018-11-12) M2Det (ResNet-1o1, 320x320) — 34.1 (2018-11-12) M2Det (VGG-16, 320x320) — 33.2 (2018-11-12) M2Det (ResNet-1o1, 320x320) — 34.1 (2018-11-12) M2Det (VGG-16, 320x320) — 33.2 (2018-11-12) M2Det (ResNet-1o1, 320x320) — 34.1 (2018-11-12) M2Det (VGG-16, 320x320) — 33.2 (2018-11-12) GHM-C + GHM-R (RetinaNet-FPN-ResNet-50, M=30) — 35.8 (2018-11-13) GHM-C + GHM-R (RetinaNet-FPN-ResNet-50, M=30) — 35.8 (2018-11-13) GHM-C + GHM-R (RetinaNet-FPN-ResNet-50, M=30) — 35.8 (2018-11-13) GHM-C + GHM-R (RetinaNet-FPN-ResNet-50, M=30) — 35.8 (2018-11-13) GHM-C + GHM-R (RetinaNet-FPN-ResNet-50, M=30) — 35.8 (2018-11-13) Mask R-CNN (ResNeXt-152-FPN, cascade) — 48.6 (2018-11-21) Mask R-CNN (ResNet-101-FPN, GN, Cascade) — 47.4 (2018-11-21) Mask R-CNN (ResNeXt-152-FPN) — 46.4 (2018-11-21) Mask R-CNN (ResNeXt-152-FPN, cascade) — 48.6 (2018-11-21) Mask R-CNN (ResNet-101-FPN, GN, Cascade) — 47.4 (2018-11-21) Mask R-CNN (ResNeXt-152-FPN) — 46.4 (2018-11-21) Mask R-CNN (ResNeXt-152-FPN, cascade) — 48.6 (2018-11-21) Mask R-CNN (ResNet-101-FPN, GN, Cascade) — 47.4 (2018-11-21) Mask R-CNN (ResNeXt-152-FPN) — 46.4 (2018-11-21) Mask R-CNN (ResNeXt-152-FPN, cascade) — 48.6 (2018-11-21) Mask R-CNN (ResNet-101-FPN, GN, Cascade) — 47.4 (2018-11-21) Mask R-CNN (ResNeXt-152-FPN) — 46.4 (2018-11-21) Mask R-CNN (ResNeXt-152-FPN, cascade) — 48.6 (2018-11-21) Mask R-CNN (ResNet-101-FPN, GN, Cascade) — 47.4 (2018-11-21) Mask R-CNN (ResNeXt-152-FPN) — 46.4 (2018-11-21) Mask R-CNN (ResNet-101, DCNv2) — 43.1 (2018-11-27) Faster R-CNN (ResNet-101, DCNv2) — 41.7 (2018-11-27) Mask R-CNN (ResNet-101, DCNv2) — 43.1 (2018-11-27) Faster R-CNN (ResNet-101, DCNv2) — 41.7 (2018-11-27) Mask R-CNN (ResNet-101, DCNv2) — 43.1 (2018-11-27) Faster R-CNN (ResNet-101, DCNv2) — 41.7 (2018-11-27) Mask R-CNN (ResNet-101, DCNv2) — 43.1 (2018-11-27) Faster R-CNN (ResNet-101, DCNv2) — 41.7 (2018-11-27) Mask R-CNN (ResNet-101, DCNv2) — 43.1 (2018-11-27) Faster R-CNN (ResNet-101, DCNv2) — 41.7 (2018-11-27) Grid R-CNN (ResNet-101-FPN) — 41.3 (2018-11-29) Grid R-CNN (ResNet-50-FPN) — 39.6 (2018-11-29) Grid R-CNN (ResNet-101-FPN) — 41.3 (2018-11-29) Grid R-CNN (ResNet-50-FPN) — 39.6 (2018-11-29) Grid R-CNN (ResNet-101-FPN) — 41.3 (2018-11-29) Grid R-CNN (ResNet-50-FPN) — 39.6 (2018-11-29) Grid R-CNN (ResNet-101-FPN) — 41.3 (2018-11-29) Grid R-CNN (ResNet-50-FPN) — 39.6 (2018-11-29) Grid R-CNN (ResNet-101-FPN) — 41.3 (2018-11-29) Grid R-CNN (ResNet-50-FPN) — 39.6 (2018-11-29) TridentNet (ResNet-101) — 42.0 (2019-01-07) TridentNet (ResNet-101) — 42.0 (2019-01-07) TridentNet (ResNet-101) — 42.0 (2019-01-07) TridentNet (ResNet-101) — 42.0 (2019-01-07) TridentNet (ResNet-101) — 42.0 (2019-01-07) RetinaMask (ResNet-101-FPN) — 41.1 (2019-01-10) RetinaMask (ResNet-101-FPN) — 41.1 (2019-01-10) RetinaMask (ResNet-101-FPN) — 41.1 (2019-01-10) RetinaMask (ResNet-101-FPN) — 41.1 (2019-01-10) RetinaMask (ResNet-101-FPN) — 41.1 (2019-01-10) HTC (cascade) — 43.2 (2019-01-22) HTC (cascade) — 43.2 (2019-01-22) HTC (cascade) — 43.2 (2019-01-22) HTC (cascade) — 43.2 (2019-01-22) HTC (cascade) — 43.2 (2019-01-22) ExtremeNet (Hourglass-104, multi-scale) — 43.3 (2019-01-23) ExtremeNet (Hourglass-104, single-scale) — 40.3 (2019-01-23) ExtremeNet (Hourglass-104, multi-scale) — 43.3 (2019-01-23) ExtremeNet (Hourglass-104, single-scale) — 40.3 (2019-01-23) ExtremeNet (Hourglass-104, multi-scale) — 43.3 (2019-01-23) ExtremeNet (Hourglass-104, single-scale) — 40.3 (2019-01-23) ExtremeNet (Hourglass-104, multi-scale) — 43.3 (2019-01-23) ExtremeNet (Hourglass-104, single-scale) — 40.3 (2019-01-23) ExtremeNet (Hourglass-104, multi-scale) — 43.3 (2019-01-23) ExtremeNet (Hourglass-104, single-scale) — 40.3 (2019-01-23) FSAF (ResNeXt-101, anchor-based branches) — 41.6 (2019-03-02) FSAF (ResNet-101, anchor-based branches) — 39.3 (2019-03-02) FSAF (ResNet-101) — 37.9 (2019-03-02) FSAF (ResNet-50) — 35.9 (2019-03-02) FSAF (ResNeXt-101, anchor-based branches) — 41.6 (2019-03-02) FSAF (ResNet-101, anchor-based branches) — 39.3 (2019-03-02) FSAF (ResNet-101) — 37.9 (2019-03-02) FSAF (ResNet-50) — 35.9 (2019-03-02) FSAF (ResNeXt-101, anchor-based branches) — 41.6 (2019-03-02) FSAF (ResNet-101, anchor-based branches) — 39.3 (2019-03-02) FSAF (ResNet-101) — 37.9 (2019-03-02) FSAF (ResNet-50) — 35.9 (2019-03-02) FSAF (ResNeXt-101, anchor-based branches) — 41.6 (2019-03-02) FSAF (ResNet-101, anchor-based branches) — 39.3 (2019-03-02) FSAF (ResNet-101) — 37.9 (2019-03-02) FSAF (ResNet-50) — 35.9 (2019-03-02) FSAF (ResNeXt-101, anchor-based branches) — 41.6 (2019-03-02) FSAF (ResNet-101, anchor-based branches) — 39.3 (2019-03-02) FSAF (ResNet-101) — 37.9 (2019-03-02) FSAF (ResNet-50) — 35.9 (2019-03-02) Mask R-CNN-FPN (ResNeXt-101, GN+WS) — 43.12 (2019-03-25) Mask R-CNN-FPN (ResNeXt-101, GN+WS) — 43.12 (2019-03-25) Mask R-CNN-FPN (ResNeXt-101, GN+WS) — 43.12 (2019-03-25) Mask R-CNN-FPN (ResNeXt-101, GN+WS) — 43.12 (2019-03-25) Mask R-CNN-FPN (ResNeXt-101, GN+WS) — 43.12 (2019-03-25) Res2Net101+HTC — 47.5 (2019-04-02) FCOS (ResNet-50-FPN + improvements) — 38.6 (2019-04-02) Faster R-CNN (Res2Net-50) — 33.7 (2019-04-02) Res2Net101+HTC — 47.5 (2019-04-02) FCOS (ResNet-50-FPN + improvements) — 38.6 (2019-04-02) Faster R-CNN (Res2Net-50) — 33.7 (2019-04-02) Res2Net101+HTC — 47.5 (2019-04-02) FCOS (ResNet-50-FPN + improvements) — 38.6 (2019-04-02) Faster R-CNN (Res2Net-50) — 33.7 (2019-04-02) Res2Net101+HTC — 47.5 (2019-04-02) FCOS (ResNet-50-FPN + improvements) — 38.6 (2019-04-02) Faster R-CNN (Res2Net-50) — 33.7 (2019-04-02) Res2Net101+HTC — 47.5 (2019-04-02) FCOS (ResNet-50-FPN + improvements) — 38.6 (2019-04-02) Faster R-CNN (Res2Net-50) — 33.7 (2019-04-02) Libra R-CNN (ResNet-50 FPN) — 38.5 (2019-04-04) Libra R-CNN (ResNet-50 FPN) — 38.5 (2019-04-04) Libra R-CNN (ResNet-50 FPN) — 38.5 (2019-04-04) Libra R-CNN (ResNet-50 FPN) — 38.5 (2019-04-04) Libra R-CNN (ResNet-50 FPN) — 38.5 (2019-04-04) Mask R-CNN (ResNet-50, ACNet) — 39.5 (2019-04-07) Mask R-CNN (ResNet-50, ACNet) — 39.5 (2019-04-07) Mask R-CNN (ResNet-50, ACNet) — 39.5 (2019-04-07) Mask R-CNN (ResNet-50, ACNet) — 39.5 (2019-04-07) Mask R-CNN (ResNet-50, ACNet) — 39.5 (2019-04-07) FoveaBox (ResNet-101-FPN, 800x800) — 38.9 (2019-04-08) FoveaBox+Retina (ResNet-50) — 38.1 (2019-04-08) FoveaBox (ResNet-101-FPN, 600x600) — 38.0 (2019-04-08) FoveaBox (ResNet-50-FPN, 600x600) — 36.0 (2019-04-08) FoveaBox (ResNet-101-FPN, 800x800) — 38.9 (2019-04-08) FoveaBox+Retina (ResNet-50) — 38.1 (2019-04-08) FoveaBox (ResNet-101-FPN, 600x600) — 38.0 (2019-04-08) FoveaBox (ResNet-50-FPN, 600x600) — 36.0 (2019-04-08) FoveaBox (ResNet-101-FPN, 800x800) — 38.9 (2019-04-08) FoveaBox+Retina (ResNet-50) — 38.1 (2019-04-08) FoveaBox (ResNet-101-FPN, 600x600) — 38.0 (2019-04-08) FoveaBox (ResNet-50-FPN, 600x600) — 36.0 (2019-04-08) FoveaBox (ResNet-101-FPN, 800x800) — 38.9 (2019-04-08) FoveaBox+Retina (ResNet-50) — 38.1 (2019-04-08) FoveaBox (ResNet-101-FPN, 600x600) — 38.0 (2019-04-08) FoveaBox (ResNet-50-FPN, 600x600) — 36.0 (2019-04-08) FoveaBox (ResNet-101-FPN, 800x800) — 38.9 (2019-04-08) FoveaBox+Retina (ResNet-50) — 38.1 (2019-04-08) FoveaBox (ResNet-101-FPN, 600x600) — 38.0 (2019-04-08) FoveaBox (ResNet-50-FPN, 600x600) — 36.0 (2019-04-08) CenterNet511 (Hourglass-52) — 41.3 (2019-04-17) CenterNet511 (Hourglass-52) — 41.3 (2019-04-17) CenterNet511 (Hourglass-52) — 41.3 (2019-04-17) CenterNet511 (Hourglass-52) — 41.3 (2019-04-17) CenterNet511 (Hourglass-52) — 41.3 (2019-04-17) CornerNet-Saccade (Hourglass-54) — 42.6 (2019-04-18) CornerNet-Saccade (Hourglass-104) — 41.4 (2019-04-18) CornerNet-Saccade (Hourglass-54) — 42.6 (2019-04-18) CornerNet-Saccade (Hourglass-104) — 41.4 (2019-04-18) CornerNet-Saccade (Hourglass-54) — 42.6 (2019-04-18) CornerNet-Saccade (Hourglass-104) — 41.4 (2019-04-18) CornerNet-Saccade (Hourglass-54) — 42.6 (2019-04-18) CornerNet-Saccade (Hourglass-104) — 41.4 (2019-04-18) CornerNet-Saccade (Hourglass-54) — 42.6 (2019-04-18) CornerNet-Saccade (Hourglass-104) — 41.4 (2019-04-18) GCNet (ResNeXt-101 + DCN + cascade + GC r16) — 47.9 (2019-04-25) RPDet (ResNeXt-101-DCN, multi-scale) — 46.8 (2019-04-25) RPDet (ResNet-101-DCN, multi-scale) — 46.4 (2019-04-25) RPDet (ResNet-101-DCN, multi-scale train) — 44.8 (2019-04-25) RPDet (ResNeXt-101-DCN) — 44.5 (2019-04-25) RPDet (ResNet-50, multi-scale train) — 40.8 (2019-04-25) GCnet (ResNet-50-FPN, GRoIE) — 40.3 (2019-04-25) RPDet (ResNet-101) — 40.3 (2019-04-25) RPDet (ResNet-50) — 38.6 (2019-04-25) GCNet (ResNeXt-101 + DCN + cascade + GC r16) — 47.9 (2019-04-25) RPDet (ResNeXt-101-DCN, multi-scale) — 46.8 (2019-04-25) RPDet (ResNet-101-DCN, multi-scale) — 46.4 (2019-04-25) RPDet (ResNet-101-DCN, multi-scale train) — 44.8 (2019-04-25) RPDet (ResNeXt-101-DCN) — 44.5 (2019-04-25) RPDet (ResNet-50, multi-scale train) — 40.8 (2019-04-25) GCnet (ResNet-50-FPN, GRoIE) — 40.3 (2019-04-25) RPDet (ResNet-101) — 40.3 (2019-04-25) RPDet (ResNet-50) — 38.6 (2019-04-25) GCNet (ResNeXt-101 + DCN + cascade + GC r16) — 47.9 (2019-04-25) RPDet (ResNeXt-101-DCN, multi-scale) — 46.8 (2019-04-25) RPDet (ResNet-101-DCN, multi-scale) — 46.4 (2019-04-25) RPDet (ResNet-101-DCN, multi-scale train) — 44.8 (2019-04-25) RPDet (ResNeXt-101-DCN) — 44.5 (2019-04-25) RPDet (ResNet-50, multi-scale train) — 40.8 (2019-04-25) GCnet (ResNet-50-FPN, GRoIE) — 40.3 (2019-04-25) RPDet (ResNet-101) — 40.3 (2019-04-25) RPDet (ResNet-50) — 38.6 (2019-04-25) GCNet (ResNeXt-101 + DCN + cascade + GC r16) — 47.9 (2019-04-25) RPDet (ResNeXt-101-DCN, multi-scale) — 46.8 (2019-04-25) RPDet (ResNet-101-DCN, multi-scale) — 46.4 (2019-04-25) RPDet (ResNet-101-DCN, multi-scale train) — 44.8 (2019-04-25) RPDet (ResNeXt-101-DCN) — 44.5 (2019-04-25) RPDet (ResNet-50, multi-scale train) — 40.8 (2019-04-25) GCnet (ResNet-50-FPN, GRoIE) — 40.3 (2019-04-25) RPDet (ResNet-101) — 40.3 (2019-04-25) RPDet (ResNet-50) — 38.6 (2019-04-25) GCNet (ResNeXt-101 + DCN + cascade + GC r16) — 47.9 (2019-04-25) RPDet (ResNeXt-101-DCN, multi-scale) — 46.8 (2019-04-25) RPDet (ResNet-101-DCN, multi-scale) — 46.4 (2019-04-25) RPDet (ResNet-101-DCN, multi-scale train) — 44.8 (2019-04-25) RPDet (ResNeXt-101-DCN) — 44.5 (2019-04-25) RPDet (ResNet-50, multi-scale train) — 40.8 (2019-04-25) GCnet (ResNet-50-FPN, GRoIE) — 40.3 (2019-04-25) RPDet (ResNet-101) — 40.3 (2019-04-25) RPDet (ResNet-50) — 38.6 (2019-04-25) Mask R-CNN-FPN (AOGNet-40M) — 44.9 (2019-08-04) Mask R-CNN-FPN (AOGNet-40M) — 44.9 (2019-08-04) Mask R-CNN-FPN (AOGNet-40M) — 44.9 (2019-08-04) Mask R-CNN-FPN (AOGNet-40M) — 44.9 (2019-08-04) Mask R-CNN-FPN (AOGNet-40M) — 44.9 (2019-08-04) Faster R-CNN (LIP-ResNet-101) — 41.7 (2019-08-12) Faster R-CNN (LIP-ResNet-101) — 41.7 (2019-08-12) Faster R-CNN (LIP-ResNet-101) — 41.7 (2019-08-12) Faster R-CNN (LIP-ResNet-101) — 41.7 (2019-08-12) Faster R-CNN (LIP-ResNet-101) — 41.7 (2019-08-12) HTC (HRNetV2p-W48) — 47.0 (2019-08-20) Mask R-CNN (HRNetV2p-W48, cascade) — 46.0 (2019-08-20) HTC (HRNetV2p-W32) — 45.3 (2019-08-20) Cascade R-CNN (HRNetV2p-W48) — 44.6 (2019-08-20) Cascade R-CNN (HRNetV2p-W32) — 43.7 (2019-08-20) HTC (HRNetV2p-W18) — 43.1 (2019-08-20) Mask R-CNN (HRNetV2p-W32) — 42.3 (2019-08-20) Faster R-CNN (HRNetV2p-W48) — 41.8 (2019-08-20) Cascade R-CNN (HRNetV2p-W18) — 41.3 (2019-08-20) Faster R-CNN (HRNetV2p-W32) — 40.9 (2019-08-20) Mask R-CNN (HRNetV2p-W18) — 39.2 (2019-08-20) Faster R-CNN (HRNetV2p-W18) — 38.0 (2019-08-20) HTC (HRNetV2p-W48) — 47.0 (2019-08-20) Mask R-CNN (HRNetV2p-W48, cascade) — 46.0 (2019-08-20) HTC (HRNetV2p-W32) — 45.3 (2019-08-20) Cascade R-CNN (HRNetV2p-W48) — 44.6 (2019-08-20) Cascade R-CNN (HRNetV2p-W32) — 43.7 (2019-08-20) HTC (HRNetV2p-W18) — 43.1 (2019-08-20) Mask R-CNN (HRNetV2p-W32) — 42.3 (2019-08-20) Faster R-CNN (HRNetV2p-W48) — 41.8 (2019-08-20) Cascade R-CNN (HRNetV2p-W18) — 41.3 (2019-08-20) Faster R-CNN (HRNetV2p-W32) — 40.9 (2019-08-20) Mask R-CNN (HRNetV2p-W18) — 39.2 (2019-08-20) Faster R-CNN (HRNetV2p-W18) — 38.0 (2019-08-20) HTC (HRNetV2p-W48) — 47.0 (2019-08-20) Mask R-CNN (HRNetV2p-W48, cascade) — 46.0 (2019-08-20) HTC (HRNetV2p-W32) — 45.3 (2019-08-20) Cascade R-CNN (HRNetV2p-W48) — 44.6 (2019-08-20) Cascade R-CNN (HRNetV2p-W32) — 43.7 (2019-08-20) HTC (HRNetV2p-W18) — 43.1 (2019-08-20) Mask R-CNN (HRNetV2p-W32) — 42.3 (2019-08-20) Faster R-CNN (HRNetV2p-W48) — 41.8 (2019-08-20) Cascade R-CNN (HRNetV2p-W18) — 41.3 (2019-08-20) Faster R-CNN (HRNetV2p-W32) — 40.9 (2019-08-20) Mask R-CNN (HRNetV2p-W18) — 39.2 (2019-08-20) Faster R-CNN (HRNetV2p-W18) — 38.0 (2019-08-20) HTC (HRNetV2p-W48) — 47.0 (2019-08-20) Mask R-CNN (HRNetV2p-W48, cascade) — 46.0 (2019-08-20) HTC (HRNetV2p-W32) — 45.3 (2019-08-20) Cascade R-CNN (HRNetV2p-W48) — 44.6 (2019-08-20) Cascade R-CNN (HRNetV2p-W32) — 43.7 (2019-08-20) HTC (HRNetV2p-W18) — 43.1 (2019-08-20) Mask R-CNN (HRNetV2p-W32) — 42.3 (2019-08-20) Faster R-CNN (HRNetV2p-W48) — 41.8 (2019-08-20) Cascade R-CNN (HRNetV2p-W18) — 41.3 (2019-08-20) Faster R-CNN (HRNetV2p-W32) — 40.9 (2019-08-20) Mask R-CNN (HRNetV2p-W18) — 39.2 (2019-08-20) Faster R-CNN (HRNetV2p-W18) — 38.0 (2019-08-20) HTC (HRNetV2p-W48) — 47.0 (2019-08-20) Mask R-CNN (HRNetV2p-W48, cascade) — 46.0 (2019-08-20) HTC (HRNetV2p-W32) — 45.3 (2019-08-20) Cascade R-CNN (HRNetV2p-W48) — 44.6 (2019-08-20) Cascade R-CNN (HRNetV2p-W32) — 43.7 (2019-08-20) HTC (HRNetV2p-W18) — 43.1 (2019-08-20) Mask R-CNN (HRNetV2p-W32) — 42.3 (2019-08-20) Faster R-CNN (HRNetV2p-W48) — 41.8 (2019-08-20) Cascade R-CNN (HRNetV2p-W18) — 41.3 (2019-08-20) Faster R-CNN (HRNetV2p-W32) — 40.9 (2019-08-20) Mask R-CNN (HRNetV2p-W18) — 39.2 (2019-08-20) Faster R-CNN (HRNetV2p-W18) — 38.0 (2019-08-20) Online Fg Bal. Sampling+Hard Negative Mining (ResNet-50) — 35.6 (2019-09-21) Online Fg Bal. Sampling+Hard Negative Mining (ResNet-50) — 35.6 (2019-09-21) Online Fg Bal. Sampling+Hard Negative Mining (ResNet-50) — 35.6 (2019-09-21) Online Fg Bal. Sampling+Hard Negative Mining (ResNet-50) — 35.6 (2019-09-21) Online Fg Bal. Sampling+Hard Negative Mining (ResNet-50) — 35.6 (2019-09-21) CenterMask+VoVNet99 (multi-scale) — 48.6 (2019-11-15) CenterMask+VoVNetV2-99 (single-scale) — 45.6 (2019-11-15) Mask R-CNN (VoVNetV2-99, single-scale) — 44.9 (2019-11-15) CenterMask+VoVNetV2-57 (single-scale) — 44.6 (2019-11-15) CenterMask+X101-32x8d (single-scale) — 44.4 (2019-11-15) CenterMask+VoVNet99 (multi-scale) — 48.6 (2019-11-15) CenterMask+VoVNetV2-99 (single-scale) — 45.6 (2019-11-15) Mask R-CNN (VoVNetV2-99, single-scale) — 44.9 (2019-11-15) CenterMask+VoVNetV2-57 (single-scale) — 44.6 (2019-11-15) CenterMask+X101-32x8d (single-scale) — 44.4 (2019-11-15) CenterMask+VoVNet99 (multi-scale) — 48.6 (2019-11-15) CenterMask+VoVNetV2-99 (single-scale) — 45.6 (2019-11-15) Mask R-CNN (VoVNetV2-99, single-scale) — 44.9 (2019-11-15) CenterMask+VoVNetV2-57 (single-scale) — 44.6 (2019-11-15) CenterMask+X101-32x8d (single-scale) — 44.4 (2019-11-15) CenterMask+VoVNet99 (multi-scale) — 48.6 (2019-11-15) CenterMask+VoVNetV2-99 (single-scale) — 45.6 (2019-11-15) Mask R-CNN (VoVNetV2-99, single-scale) — 44.9 (2019-11-15) CenterMask+VoVNetV2-57 (single-scale) — 44.6 (2019-11-15) CenterMask+X101-32x8d (single-scale) — 44.4 (2019-11-15) CenterMask+VoVNet99 (multi-scale) — 48.6 (2019-11-15) CenterMask+VoVNetV2-99 (single-scale) — 45.6 (2019-11-15) Mask R-CNN (VoVNetV2-99, single-scale) — 44.9 (2019-11-15) CenterMask+VoVNetV2-57 (single-scale) — 44.6 (2019-11-15) CenterMask+X101-32x8d (single-scale) — 44.4 (2019-11-15) EfficientDet-D7 (1536) — 52.1 (2019-11-20) EfficientDet-D7 (1536) — 52.1 (2019-11-20) EfficientDet-D7 (1536) — 52.1 (2019-11-20) EfficientDet-D7 (1536) — 52.1 (2019-11-20) EfficientDet-D7 (1536) — 52.1 (2019-11-20) RetinaNet (SpineNet-190, 1536x1536) — 52.2 (2019-12-10) RetinaNet (SpineNet-190, 1536x1536) — 52.2 (2019-12-10) RetinaNet (SpineNet-190, 1536x1536) — 52.2 (2019-12-10) RetinaNet (SpineNet-190, 1536x1536) — 52.2 (2019-12-10) RetinaNet (SpineNet-190, 1536x1536) — 52.2 (2019-12-10) ResNeSt-200 (multi-scale) — 52.47 (2020-04-19) ResNeSt-200-DCN (single-scale) — 50.91 (2020-04-19) ResNeSt-200 (single-scale) — 50.54 (2020-04-19) ResNeSt-200 (multi-scale) — 52.47 (2020-04-19) ResNeSt-200-DCN (single-scale) — 50.91 (2020-04-19) ResNeSt-200 (single-scale) — 50.54 (2020-04-19) ResNeSt-200 (multi-scale) — 52.47 (2020-04-19) ResNeSt-200-DCN (single-scale) — 50.91 (2020-04-19) ResNeSt-200 (single-scale) — 50.54 (2020-04-19) ResNeSt-200 (multi-scale) — 52.47 (2020-04-19) ResNeSt-200-DCN (single-scale) — 50.91 (2020-04-19) ResNeSt-200 (single-scale) — 50.54 (2020-04-19) ResNeSt-200 (multi-scale) — 52.47 (2020-04-19) ResNeSt-200-DCN (single-scale) — 50.91 (2020-04-19) ResNeSt-200 (single-scale) — 50.54 (2020-04-19) Mask R-CNN (ResNet-50-FPN, GRoIE) — 38.4 (2020-04-28) Faster R-CNN (ResNet-50-FPN, GRoIE) — 37.5 (2020-04-28) Mask R-CNN (ResNet-50-FPN, GRoIE) — 38.4 (2020-04-28) Faster R-CNN (ResNet-50-FPN, GRoIE) — 37.5 (2020-04-28) Mask R-CNN (ResNet-50-FPN, GRoIE) — 38.4 (2020-04-28) Faster R-CNN (ResNet-50-FPN, GRoIE) — 37.5 (2020-04-28) Mask R-CNN (ResNet-50-FPN, GRoIE) — 38.4 (2020-04-28) Faster R-CNN (ResNet-50-FPN, GRoIE) — 37.5 (2020-04-28) Mask R-CNN (ResNet-50-FPN, GRoIE) — 38.4 (2020-04-28) Faster R-CNN (ResNet-50-FPN, GRoIE) — 37.5 (2020-04-28) DETR-DC5 (ResNet-101) — 44.9 (2020-05-26) Faster RCNN-R101-FPN+ — 44.0 (2020-05-26) DETR-DC5 (ResNet-101) — 44.9 (2020-05-26) Faster RCNN-R101-FPN+ — 44.0 (2020-05-26) DETR-DC5 (ResNet-101) — 44.9 (2020-05-26) Faster RCNN-R101-FPN+ — 44.0 (2020-05-26) DETR-DC5 (ResNet-101) — 44.9 (2020-05-26) Faster RCNN-R101-FPN+ — 44.0 (2020-05-26) DETR-DC5 (ResNet-101) — 44.9 (2020-05-26) Faster RCNN-R101-FPN+ — 44.0 (2020-05-26) SpineNet-190 (1280, with Self-training on OpenImages, single-scale) — 54.2 (2020-06-11) VirTex Mask R-CNN (ResNet-50-FPN) — 40.9 (2020-06-11) SpineNet-190 (1280, with Self-training on OpenImages, single-scale) — 54.2 (2020-06-11) VirTex Mask R-CNN (ResNet-50-FPN) — 40.9 (2020-06-11) SpineNet-190 (1280, with Self-training on OpenImages, single-scale) — 54.2 (2020-06-11) VirTex Mask R-CNN (ResNet-50-FPN) — 40.9 (2020-06-11) SpineNet-190 (1280, with Self-training on OpenImages, single-scale) — 54.2 (2020-06-11) VirTex Mask R-CNN (ResNet-50-FPN) — 40.9 (2020-06-11) SpineNet-190 (1280, with Self-training on OpenImages, single-scale) — 54.2 (2020-06-11) VirTex Mask R-CNN (ResNet-50-FPN) — 40.9 (2020-06-11) HoughNet (HG-104, MS) — 46.1 (2020-07-05) HoughNet (HG-104) — 43.0 (2020-07-05) HoughNet (HG-104, MS) — 46.1 (2020-07-05) HoughNet (HG-104) — 43.0 (2020-07-05) HoughNet (HG-104, MS) — 46.1 (2020-07-05) HoughNet (HG-104) — 43.0 (2020-07-05) HoughNet (HG-104, MS) — 46.1 (2020-07-05) HoughNet (HG-104) — 43.0 (2020-07-05) HoughNet (HG-104, MS) — 46.1 (2020-07-05) HoughNet (HG-104) — 43.0 (2020-07-05) PPDet (ResNet-101-FPN) — 40.5 (2020-08-03) PPDet (ResNet-101-FPN) — 40.5 (2020-08-03) PPDet (ResNet-101-FPN) — 40.5 (2020-08-03) PPDet (ResNet-101-FPN) — 40.5 (2020-08-03) PPDet (ResNet-101-FPN) — 40.5 (2020-08-03) Faster R-CNN+aLRP Loss (ResNet-50, 500 scale) — 40.7 (2020-09-28) RetinaNet+aLRP Loss (ResNet-50, 500 scale) — 40.2 (2020-09-28) FoveaBox+aLRP Loss (ResNet-50, 500 scale) — 39.7 (2020-09-28) Faster R-CNN+aLRP Loss (ResNet-50, 500 scale) — 40.7 (2020-09-28) RetinaNet+aLRP Loss (ResNet-50, 500 scale) — 40.2 (2020-09-28) FoveaBox+aLRP Loss (ResNet-50, 500 scale) — 39.7 (2020-09-28) Faster R-CNN+aLRP Loss (ResNet-50, 500 scale) — 40.7 (2020-09-28) RetinaNet+aLRP Loss (ResNet-50, 500 scale) — 40.2 (2020-09-28) FoveaBox+aLRP Loss (ResNet-50, 500 scale) — 39.7 (2020-09-28) Faster R-CNN+aLRP Loss (ResNet-50, 500 scale) — 40.7 (2020-09-28) RetinaNet+aLRP Loss (ResNet-50, 500 scale) — 40.2 (2020-09-28) FoveaBox+aLRP Loss (ResNet-50, 500 scale) — 39.7 (2020-09-28) Faster R-CNN+aLRP Loss (ResNet-50, 500 scale) — 40.7 (2020-09-28) RetinaNet+aLRP Loss (ResNet-50, 500 scale) — 40.2 (2020-09-28) FoveaBox+aLRP Loss (ResNet-50, 500 scale) — 39.7 (2020-09-28) YOLOv4-P7 CSP-P7 (single-scale, 16 fps) — 55.4 (2020-11-16) YOLOv4-P7 CSP-P7 (single-scale, 16 fps) — 55.4 (2020-11-16) YOLOv4-P7 CSP-P7 (single-scale, 16 fps) — 55.4 (2020-11-16) YOLOv4-P7 CSP-P7 (single-scale, 16 fps) — 55.4 (2020-11-16) YOLOv4-P7 CSP-P7 (single-scale, 16 fps) — 55.4 (2020-11-16) Sparse R-CNN (ResNet-101, learnable proposals, random crop aug, FPN) — 45.6 (2020-11-25) Sparse R-CNN (ResNet-50, learnable proposals, random crop aug, FPN) — 44.5 (2020-11-25) Sparse R-CNN (ResNet-101, FPN) — 43.5 (2020-11-25) Sparse R-CNN (ResNet-50, FPN) — 42.3 (2020-11-25) Sparse R-CNN (ResNet-101, learnable proposals, random crop aug, FPN) — 45.6 (2020-11-25) Sparse R-CNN (ResNet-50, learnable proposals, random crop aug, FPN) — 44.5 (2020-11-25) Sparse R-CNN (ResNet-101, FPN) — 43.5 (2020-11-25) Sparse R-CNN (ResNet-50, FPN) — 42.3 (2020-11-25) Sparse R-CNN (ResNet-101, learnable proposals, random crop aug, FPN) — 45.6 (2020-11-25) Sparse R-CNN (ResNet-50, learnable proposals, random crop aug, FPN) — 44.5 (2020-11-25) Sparse R-CNN (ResNet-101, FPN) — 43.5 (2020-11-25) Sparse R-CNN (ResNet-50, FPN) — 42.3 (2020-11-25) Sparse R-CNN (ResNet-101, learnable proposals, random crop aug, FPN) — 45.6 (2020-11-25) Sparse R-CNN (ResNet-50, learnable proposals, random crop aug, FPN) — 44.5 (2020-11-25) Sparse R-CNN (ResNet-101, FPN) — 43.5 (2020-11-25) Sparse R-CNN (ResNet-50, FPN) — 42.3 (2020-11-25) Sparse R-CNN (ResNet-101, learnable proposals, random crop aug, FPN) — 45.6 (2020-11-25) Sparse R-CNN (ResNet-50, learnable proposals, random crop aug, FPN) — 44.5 (2020-11-25) Sparse R-CNN (ResNet-101, FPN) — 43.5 (2020-11-25) Sparse R-CNN (ResNet-50, FPN) — 42.3 (2020-11-25) Cascade Eff-B7 NAS-FPN (1280, self-training Copy Paste, single-scale) — 57.0 (2020-12-13) Cascade Eff-B7 NAS-FPN (1280) — 54.5 (2020-12-13) Cascade Eff-B7 NAS-FPN (1280, self-training Copy Paste, single-scale) — 57.0 (2020-12-13) Cascade Eff-B7 NAS-FPN (1280) — 54.5 (2020-12-13) Cascade Eff-B7 NAS-FPN (1280, self-training Copy Paste, single-scale) — 57.0 (2020-12-13) Cascade Eff-B7 NAS-FPN (1280) — 54.5 (2020-12-13) Cascade Eff-B7 NAS-FPN (1280, self-training Copy Paste, single-scale) — 57.0 (2020-12-13) Cascade Eff-B7 NAS-FPN (1280) — 54.5 (2020-12-13) Cascade Eff-B7 NAS-FPN (1280, self-training Copy Paste, single-scale) — 57.0 (2020-12-13) Cascade Eff-B7 NAS-FPN (1280) — 54.5 (2020-12-13) GCNet (ResNeXt-101 + DCN + cascade + GC r4) — 51.8 (2020-12-24) GCNet (ResNeXt-101 + DCN + cascade + GC r4) — 51.8 (2020-12-24) GCNet (ResNeXt-101 + DCN + cascade + GC r4) — 51.8 (2020-12-24) GCNet (ResNeXt-101 + DCN + cascade + GC r4) — 51.8 (2020-12-24) GCNet (ResNeXt-101 + DCN + cascade + GC r4) — 51.8 (2020-12-24) BoTNet 200 (Mask R-CNN, single scale, 72 epochs) — 49.7 (2021-01-27) BoTNet 152 (Mask R-CNN, single scale, 72 epochs) — 49.5 (2021-01-27) BoTNet 50 (72 epochs) — 45.9 (2021-01-27) BoTNet 200 (Mask R-CNN, single scale, 72 epochs) — 49.7 (2021-01-27) BoTNet 152 (Mask R-CNN, single scale, 72 epochs) — 49.5 (2021-01-27) BoTNet 50 (72 epochs) — 45.9 (2021-01-27) BoTNet 200 (Mask R-CNN, single scale, 72 epochs) — 49.7 (2021-01-27) BoTNet 152 (Mask R-CNN, single scale, 72 epochs) — 49.5 (2021-01-27) BoTNet 50 (72 epochs) — 45.9 (2021-01-27) BoTNet 200 (Mask R-CNN, single scale, 72 epochs) — 49.7 (2021-01-27) BoTNet 152 (Mask R-CNN, single scale, 72 epochs) — 49.5 (2021-01-27) BoTNet 50 (72 epochs) — 45.9 (2021-01-27) BoTNet 200 (Mask R-CNN, single scale, 72 epochs) — 49.7 (2021-01-27) BoTNet 152 (Mask R-CNN, single scale, 72 epochs) — 49.5 (2021-01-27) BoTNet 50 (72 epochs) — 45.9 (2021-01-27) PVT-Large (RetinaNet 3x,MS) — 43.4 (2021-02-24) PVT-Large (RetinaNet 1x) — 42.6 (2021-02-24) PVT-Large (RetinaNet 3x,MS) — 43.4 (2021-02-24) PVT-Large (RetinaNet 1x) — 42.6 (2021-02-24) PVT-Large (RetinaNet 3x,MS) — 43.4 (2021-02-24) PVT-Large (RetinaNet 1x) — 42.6 (2021-02-24) PVT-Large (RetinaNet 3x,MS) — 43.4 (2021-02-24) PVT-Large (RetinaNet 1x) — 42.6 (2021-02-24) PVT-Large (RetinaNet 3x,MS) — 43.4 (2021-02-24) PVT-Large (RetinaNet 1x) — 42.6 (2021-02-24) Swin-L (HTC++, multi scale) — 58.0 (2021-03-25) Swin-L (HTC++, single scale) — 57.1 (2021-03-25) UniverseNet-20.08d (Res2Net-101, DCN, multi-scale) — 53.5 (2021-03-25) UniverseNet-20.08d (Res2Net-101, DCN, single-scale) — 50.9 (2021-03-25) UniverseNet-20.08 (Res2Net-50, DCN, single-scale) — 48.5 (2021-03-25) Swin-L (HTC++, multi scale) — 58.0 (2021-03-25) Swin-L (HTC++, single scale) — 57.1 (2021-03-25) UniverseNet-20.08d (Res2Net-101, DCN, multi-scale) — 53.5 (2021-03-25) UniverseNet-20.08d (Res2Net-101, DCN, single-scale) — 50.9 (2021-03-25) UniverseNet-20.08 (Res2Net-50, DCN, single-scale) — 48.5 (2021-03-25) Swin-L (HTC++, multi scale) — 58.0 (2021-03-25) Swin-L (HTC++, single scale) — 57.1 (2021-03-25) UniverseNet-20.08d (Res2Net-101, DCN, multi-scale) — 53.5 (2021-03-25) UniverseNet-20.08d (Res2Net-101, DCN, single-scale) — 50.9 (2021-03-25) UniverseNet-20.08 (Res2Net-50, DCN, single-scale) — 48.5 (2021-03-25) Swin-L (HTC++, multi scale) — 58.0 (2021-03-25) Swin-L (HTC++, single scale) — 57.1 (2021-03-25) UniverseNet-20.08d (Res2Net-101, DCN, multi-scale) — 53.5 (2021-03-25) UniverseNet-20.08d (Res2Net-101, DCN, single-scale) — 50.9 (2021-03-25) UniverseNet-20.08 (Res2Net-50, DCN, single-scale) — 48.5 (2021-03-25) Swin-L (HTC++, multi scale) — 58.0 (2021-03-25) Swin-L (HTC++, single scale) — 57.1 (2021-03-25) UniverseNet-20.08d (Res2Net-101, DCN, multi-scale) — 53.5 (2021-03-25) UniverseNet-20.08d (Res2Net-101, DCN, single-scale) — 50.9 (2021-03-25) UniverseNet-20.08 (Res2Net-50, DCN, single-scale) — 48.5 (2021-03-25) RetinaNet (ViL-Base, multi-scale, 3x) — 44.7 (2021-03-29) RetinaNet (ViL-Base) — 44.3 (2021-03-29) RetinaNet (ViL-Base, multi-scale, 3x) — 44.7 (2021-03-29) RetinaNet (ViL-Base) — 44.3 (2021-03-29) RetinaNet (ViL-Base, multi-scale, 3x) — 44.7 (2021-03-29) RetinaNet (ViL-Base) — 44.3 (2021-03-29) RetinaNet (ViL-Base, multi-scale, 3x) — 44.7 (2021-03-29) RetinaNet (ViL-Base) — 44.3 (2021-03-29) RetinaNet (ViL-Base, multi-scale, 3x) — 44.7 (2021-03-29) RetinaNet (ViL-Base) — 44.3 (2021-03-29) R3-CNN (ResNet-50-FPN, DCN) — 44.8 (2021-04-03) R3-CNN (ResNet-50-FPN, GC-Net) — 44.3 (2021-04-03) R3-CNN (ResNet-50-FPN) — 42.0 (2021-04-03) R3-CNN (ResNet-50-FPN, DCN) — 44.8 (2021-04-03) R3-CNN (ResNet-50-FPN, GC-Net) — 44.3 (2021-04-03) R3-CNN (ResNet-50-FPN) — 42.0 (2021-04-03) R3-CNN (ResNet-50-FPN, DCN) — 44.8 (2021-04-03) R3-CNN (ResNet-50-FPN, GC-Net) — 44.3 (2021-04-03) R3-CNN (ResNet-50-FPN) — 42.0 (2021-04-03) R3-CNN (ResNet-50-FPN, DCN) — 44.8 (2021-04-03) R3-CNN (ResNet-50-FPN, GC-Net) — 44.3 (2021-04-03) R3-CNN (ResNet-50-FPN) — 42.0 (2021-04-03) R3-CNN (ResNet-50-FPN, DCN) — 44.8 (2021-04-03) R3-CNN (ResNet-50-FPN, GC-Net) — 44.3 (2021-04-03) R3-CNN (ResNet-50-FPN) — 42.0 (2021-04-03) QueryInst (single scale) — 56.1 (2021-05-05) QueryInst (single scale) — 56.1 (2021-05-05) QueryInst (single scale) — 56.1 (2021-05-05) QueryInst (single scale) — 56.1 (2021-05-05) QueryInst (single scale) — 56.1 (2021-05-05) Faster R-CNN (FPN, X-volution) — 42.8 (2021-06-04) Faster R-CNN (FPN, X-volution) — 42.8 (2021-06-04) Faster R-CNN (FPN, X-volution) — 42.8 (2021-06-04) Faster R-CNN (FPN, X-volution) — 42.8 (2021-06-04) Faster R-CNN (FPN, X-volution) — 42.8 (2021-06-04) DyHead (Swin-L, multi scale, self-training) — 60.3 (2021-06-15) DyHead (Swin-L, multi scale) — 58.4 (2021-06-15) DyHead (ResNet-101) — 46.5 (2021-06-15) DyHead (Swin-L, multi scale, self-training) — 60.3 (2021-06-15) DyHead (Swin-L, multi scale) — 58.4 (2021-06-15) DyHead (ResNet-101) — 46.5 (2021-06-15) DyHead (Swin-L, multi scale, self-training) — 60.3 (2021-06-15) DyHead (Swin-L, multi scale) — 58.4 (2021-06-15) DyHead (ResNet-101) — 46.5 (2021-06-15) DyHead (Swin-L, multi scale, self-training) — 60.3 (2021-06-15) DyHead (Swin-L, multi scale) — 58.4 (2021-06-15) DyHead (ResNet-101) — 46.5 (2021-06-15) DyHead (Swin-L, multi scale, self-training) — 60.3 (2021-06-15) DyHead (Swin-L, multi scale) — 58.4 (2021-06-15) DyHead (ResNet-101) — 46.5 (2021-06-15) Soft Teacher + Swin-L (HTC++, multi-scale) — 60.7 (2021-06-16) Soft Teacher+Swin-L(HTC++, single scale) — 60.1 (2021-06-16) Soft Teacher + Swin-L (HTC++, multi-scale) — 60.7 (2021-06-16) Soft Teacher+Swin-L(HTC++, single scale) — 60.1 (2021-06-16) Soft Teacher + Swin-L (HTC++, multi-scale) — 60.7 (2021-06-16) Soft Teacher+Swin-L(HTC++, single scale) — 60.1 (2021-06-16) Soft Teacher + Swin-L (HTC++, multi-scale) — 60.7 (2021-06-16) Soft Teacher+Swin-L(HTC++, single scale) — 60.1 (2021-06-16) Soft Teacher + Swin-L (HTC++, multi-scale) — 60.7 (2021-06-16) Soft Teacher+Swin-L(HTC++, single scale) — 60.1 (2021-06-16) XCiT-M24/8 — 48.5 (2021-06-17) XCiT-S24/8 — 48.1 (2021-06-17) XCiT-M24/8 — 48.5 (2021-06-17) XCiT-S24/8 — 48.1 (2021-06-17) XCiT-M24/8 — 48.5 (2021-06-17) XCiT-S24/8 — 48.1 (2021-06-17) XCiT-M24/8 — 48.5 (2021-06-17) XCiT-S24/8 — 48.1 (2021-06-17) XCiT-M24/8 — 48.5 (2021-06-17) XCiT-S24/8 — 48.1 (2021-06-17) Sparse R-CNN (PVTv2-B2) — 50.1 (2021-06-25) Sparse R-CNN (PVTv2-B2) — 50.1 (2021-06-25) Sparse R-CNN (PVTv2-B2) — 50.1 (2021-06-25) Sparse R-CNN (PVTv2-B2) — 50.1 (2021-06-25) Sparse R-CNN (PVTv2-B2) — 50.1 (2021-06-25) Cascade RCNN-RS (SpineNet-143L, single scale) — 53.6 (2021-06-30) Cascade RCNN-RS (ResNet-200, single scale) — 53.1 (2021-06-30) Cascade RCNN-RS (SpineNet-143L, single scale) — 53.6 (2021-06-30) Cascade RCNN-RS (ResNet-200, single scale) — 53.1 (2021-06-30) Cascade RCNN-RS (SpineNet-143L, single scale) — 53.6 (2021-06-30) Cascade RCNN-RS (ResNet-200, single scale) — 53.1 (2021-06-30) Cascade RCNN-RS (SpineNet-143L, single scale) — 53.6 (2021-06-30) Cascade RCNN-RS (ResNet-200, single scale) — 53.1 (2021-06-30) Cascade RCNN-RS (SpineNet-143L, single scale) — 53.6 (2021-06-30) Cascade RCNN-RS (ResNet-200, single scale) — 53.1 (2021-06-30) CBNetV2 (Dual-Swin-L HTC, multi-scale) — 59.6 (2021-07-01) CBNetV2 (Dual-Swin-L HTC, multi-scale) — 59.1 (2021-07-01) Focal-L (DyHead, multi-scale) — 58.7 (2021-07-01) CBNetV2 (Dual-Swin-L HTC, multi-scale) — 59.6 (2021-07-01) CBNetV2 (Dual-Swin-L HTC, multi-scale) — 59.1 (2021-07-01) Focal-L (DyHead, multi-scale) — 58.7 (2021-07-01) CBNetV2 (Dual-Swin-L HTC, multi-scale) — 59.6 (2021-07-01) CBNetV2 (Dual-Swin-L HTC, multi-scale) — 59.1 (2021-07-01) Focal-L (DyHead, multi-scale) — 58.7 (2021-07-01) CBNetV2 (Dual-Swin-L HTC, multi-scale) — 59.6 (2021-07-01) CBNetV2 (Dual-Swin-L HTC, multi-scale) — 59.1 (2021-07-01) Focal-L (DyHead, multi-scale) — 58.7 (2021-07-01) CBNetV2 (Dual-Swin-L HTC, multi-scale) — 59.6 (2021-07-01) CBNetV2 (Dual-Swin-L HTC, multi-scale) — 59.1 (2021-07-01) Focal-L (DyHead, multi-scale) — 58.7 (2021-07-01) DETR-ResNet50 with iRPE-K (300 epochs) — 42.3 (2021-07-29) DETR-ResNet50 with iRPE-K (150 epochs) — 40.8 (2021-07-29) DETR-ResNet50 with iRPE-K (300 epochs) — 42.3 (2021-07-29) DETR-ResNet50 with iRPE-K (150 epochs) — 40.8 (2021-07-29) DETR-ResNet50 with iRPE-K (300 epochs) — 42.3 (2021-07-29) DETR-ResNet50 with iRPE-K (150 epochs) — 40.8 (2021-07-29) DETR-ResNet50 with iRPE-K (300 epochs) — 42.3 (2021-07-29) DETR-ResNet50 with iRPE-K (150 epochs) — 40.8 (2021-07-29) DETR-ResNet50 with iRPE-K (300 epochs) — 42.3 (2021-07-29) DETR-ResNet50 with iRPE-K (150 epochs) — 40.8 (2021-07-29) Conditional DETR-DC5-R101 — 45.9 (2021-08-13) Conditional DETR-DC5-R50 — 45.1 (2021-08-13) Conditional DETR-R101 — 44.5 (2021-08-13) Conditional DETR-R50 — 43.0 (2021-08-13) Conditional DETR-DC5-R101 — 45.9 (2021-08-13) Conditional DETR-DC5-R50 — 45.1 (2021-08-13) Conditional DETR-R101 — 44.5 (2021-08-13) Conditional DETR-R50 — 43.0 (2021-08-13) Conditional DETR-DC5-R101 — 45.9 (2021-08-13) Conditional DETR-DC5-R50 — 45.1 (2021-08-13) Conditional DETR-R101 — 44.5 (2021-08-13) Conditional DETR-R50 — 43.0 (2021-08-13) Conditional DETR-DC5-R101 — 45.9 (2021-08-13) Conditional DETR-DC5-R50 — 45.1 (2021-08-13) Conditional DETR-R101 — 44.5 (2021-08-13) Conditional DETR-R50 — 43.0 (2021-08-13) Conditional DETR-DC5-R101 — 45.9 (2021-08-13) Conditional DETR-DC5-R50 — 45.1 (2021-08-13) Conditional DETR-R101 — 44.5 (2021-08-13) Conditional DETR-R50 — 43.0 (2021-08-13) Anchor DETR-DC5-R101 — 45.1 (2021-09-15) Anchor DETR-DC5-R50 — 44.2 (2021-09-15) Anchor DETR-DC5-R101 — 45.1 (2021-09-15) Anchor DETR-DC5-R50 — 44.2 (2021-09-15) Anchor DETR-DC5-R101 — 45.1 (2021-09-15) Anchor DETR-DC5-R50 — 44.2 (2021-09-15) Anchor DETR-DC5-R101 — 45.1 (2021-09-15) Anchor DETR-DC5-R50 — 44.2 (2021-09-15) Anchor DETR-DC5-R101 — 45.1 (2021-09-15) Anchor DETR-DC5-R50 — 44.2 (2021-09-15) Pix2seq (ViT-L) — 50.0 (2021-09-22) Pix2seq (R50-C4) — 47.3 (2021-09-22) Pix2seq (ViT-B) — 47.1 (2021-09-22) Pix2seq (R101-DC5) — 45.0 (2021-09-22) Pix2seq (R50-DC5 ) — 43.2 (2021-09-22) Pix2seq (R50) — 42.6 (2021-09-22) Pix2seq (ViT-L) — 50.0 (2021-09-22) Pix2seq (R50-C4) — 47.3 (2021-09-22) Pix2seq (ViT-B) — 47.1 (2021-09-22) Pix2seq (R101-DC5) — 45.0 (2021-09-22) Pix2seq (R50-DC5 ) — 43.2 (2021-09-22) Pix2seq (R50) — 42.6 (2021-09-22) Pix2seq (ViT-L) — 50.0 (2021-09-22) Pix2seq (R50-C4) — 47.3 (2021-09-22) Pix2seq (ViT-B) — 47.1 (2021-09-22) Pix2seq (R101-DC5) — 45.0 (2021-09-22) Pix2seq (R50-DC5 ) — 43.2 (2021-09-22) Pix2seq (R50) — 42.6 (2021-09-22) Pix2seq (ViT-L) — 50.0 (2021-09-22) Pix2seq (R50-C4) — 47.3 (2021-09-22) Pix2seq (ViT-B) — 47.1 (2021-09-22) Pix2seq (R101-DC5) — 45.0 (2021-09-22) Pix2seq (R50-DC5 ) — 43.2 (2021-09-22) Pix2seq (R50) — 42.6 (2021-09-22) Pix2seq (ViT-L) — 50.0 (2021-09-22) Pix2seq (R50-C4) — 47.3 (2021-09-22) Pix2seq (ViT-B) — 47.1 (2021-09-22) Pix2seq (R101-DC5) — 45.0 (2021-09-22) Pix2seq (R50-DC5 ) — 43.2 (2021-09-22) Pix2seq (R50) — 42.6 (2021-09-22) MAE (ViT-L, Mask R-CNN) — 53.3 (2021-11-11) MAE (ViT-B, Mask R-CNN) — 50.3 (2021-11-11) MAE (ViT-L, Mask R-CNN) — 53.3 (2021-11-11) MAE (ViT-B, Mask R-CNN) — 50.3 (2021-11-11) MAE (ViT-L, Mask R-CNN) — 53.3 (2021-11-11) MAE (ViT-B, Mask R-CNN) — 50.3 (2021-11-11) MAE (ViT-L, Mask R-CNN) — 53.3 (2021-11-11) MAE (ViT-B, Mask R-CNN) — 50.3 (2021-11-11) MAE (ViT-L, Mask R-CNN) — 53.3 (2021-11-11) MAE (ViT-B, Mask R-CNN) — 50.3 (2021-11-11) SwinV2-G (HTC++) — 62.5 (2021-11-18) SwinV2-G (HTC++) — 62.5 (2021-11-18) SwinV2-G (HTC++) — 62.5 (2021-11-18) SwinV2-G (HTC++) — 62.5 (2021-11-18) SwinV2-G (HTC++) — 62.5 (2021-11-18) Florence-CoSwin-H — 62.0 (2021-11-22) PoolFormer-S36 (Mask R-CNN) — 41.0 (2021-11-22) Florence-CoSwin-H — 62.0 (2021-11-22) PoolFormer-S36 (Mask R-CNN) — 41.0 (2021-11-22) Florence-CoSwin-H — 62.0 (2021-11-22) PoolFormer-S36 (Mask R-CNN) — 41.0 (2021-11-22) Florence-CoSwin-H — 62.0 (2021-11-22) PoolFormer-S36 (Mask R-CNN) — 41.0 (2021-11-22) Florence-CoSwin-H — 62.0 (2021-11-22) PoolFormer-S36 (Mask R-CNN) — 41.0 (2021-11-22) MAE-Det(MAE-Det-L+GFLV2) — 47.8 (2021-11-26) MAE-Det(MAE-Det-L+GFLV2) — 47.8 (2021-11-26) MAE-Det(MAE-Det-L+GFLV2) — 47.8 (2021-11-26) MAE-Det(MAE-Det-L+GFLV2) — 47.8 (2021-11-26) MAE-Det(MAE-Det-L+GFLV2) — 47.8 (2021-11-26) MViTv2-L (Cascade Mask R-CNN, multi-scale, IN21k pre-train) — 58.7 (2021-12-02) MViTv2-H (Cascade Mask R-CNN, single-scale, IN21k pre-train) — 56.1 (2021-12-02) MViTv2-L (Cascade Mask R-CNN, single-scale) — 54.3 (2021-12-02) MViT-L (Mask R-CNN, single-scale, IN21k pre-train) — 52.7 (2021-12-02) MViTv2-L (Cascade Mask R-CNN, multi-scale, IN21k pre-train) — 58.7 (2021-12-02) MViTv2-H (Cascade Mask R-CNN, single-scale, IN21k pre-train) — 56.1 (2021-12-02) MViTv2-L (Cascade Mask R-CNN, single-scale) — 54.3 (2021-12-02) MViT-L (Mask R-CNN, single-scale, IN21k pre-train) — 52.7 (2021-12-02) MViTv2-L (Cascade Mask R-CNN, multi-scale, IN21k pre-train) — 58.7 (2021-12-02) MViTv2-H (Cascade Mask R-CNN, single-scale, IN21k pre-train) — 56.1 (2021-12-02) MViTv2-L (Cascade Mask R-CNN, single-scale) — 54.3 (2021-12-02) MViT-L (Mask R-CNN, single-scale, IN21k pre-train) — 52.7 (2021-12-02) MViTv2-L (Cascade Mask R-CNN, multi-scale, IN21k pre-train) — 58.7 (2021-12-02) MViTv2-H (Cascade Mask R-CNN, single-scale, IN21k pre-train) — 56.1 (2021-12-02) MViTv2-L (Cascade Mask R-CNN, single-scale) — 54.3 (2021-12-02) MViT-L (Mask R-CNN, single-scale, IN21k pre-train) — 52.7 (2021-12-02) MViTv2-L (Cascade Mask R-CNN, multi-scale, IN21k pre-train) — 58.7 (2021-12-02) MViTv2-H (Cascade Mask R-CNN, single-scale, IN21k pre-train) — 56.1 (2021-12-02) MViTv2-L (Cascade Mask R-CNN, single-scale) — 54.3 (2021-12-02) MViT-L (Mask R-CNN, single-scale, IN21k pre-train) — 52.7 (2021-12-02) GLIP (Swin-L, multi-scale) — 60.8 (2021-12-07) GLIP (Swin-L, multi-scale) — 60.8 (2021-12-07) GLIP (Swin-L, multi-scale) — 60.8 (2021-12-07) GLIP (Swin-L, multi-scale) — 60.8 (2021-12-07) GLIP (Swin-L, multi-scale) — 60.8 (2021-12-07) REGO-Deformable DETR-X101 — 49.1 (2021-12-09) REGO-Deformable DETR-X101 — 49.1 (2021-12-09) REGO-Deformable DETR-X101 — 49.1 (2021-12-09) REGO-Deformable DETR-X101 — 49.1 (2021-12-09) REGO-Deformable DETR-X101 — 49.1 (2021-12-09) ELSA-S (Cascade Mask RCNN) — 51.6 (2021-12-23) ELSA-S (Mask RCNN) — 48.3 (2021-12-23) ELSA-S (Cascade Mask RCNN) — 51.6 (2021-12-23) ELSA-S (Mask RCNN) — 48.3 (2021-12-23) ELSA-S (Cascade Mask RCNN) — 51.6 (2021-12-23) ELSA-S (Mask RCNN) — 48.3 (2021-12-23) ELSA-S (Cascade Mask RCNN) — 51.6 (2021-12-23) ELSA-S (Mask RCNN) — 48.3 (2021-12-23) ELSA-S (Cascade Mask RCNN) — 51.6 (2021-12-23) ELSA-S (Mask RCNN) — 48.3 (2021-12-23) PatchConvNet-S120 (Mask R-CNN) — 47.0 (2021-12-27) PatchConvNet-S60 (Mask R-CNN) — 46.4 (2021-12-27) PatchConvNet-S120 (Mask R-CNN) — 47.0 (2021-12-27) PatchConvNet-S60 (Mask R-CNN) — 46.4 (2021-12-27) PatchConvNet-S120 (Mask R-CNN) — 47.0 (2021-12-27) PatchConvNet-S60 (Mask R-CNN) — 46.4 (2021-12-27) PatchConvNet-S120 (Mask R-CNN) — 47.0 (2021-12-27) PatchConvNet-S60 (Mask R-CNN) — 46.4 (2021-12-27) PatchConvNet-S120 (Mask R-CNN) — 47.0 (2021-12-27) PatchConvNet-S60 (Mask R-CNN) — 46.4 (2021-12-27) DAB-DETR-DC5-R101 — 46.6 (2022-01-28) DAB-DETR-R101 — 44.1 (2022-01-28) DAB-DETR-DC5-R101 — 46.6 (2022-01-28) DAB-DETR-R101 — 44.1 (2022-01-28) DAB-DETR-DC5-R101 — 46.6 (2022-01-28) DAB-DETR-R101 — 44.1 (2022-01-28) DAB-DETR-DC5-R101 — 46.6 (2022-01-28) DAB-DETR-R101 — 44.1 (2022-01-28) DAB-DETR-DC5-R101 — 46.6 (2022-01-28) DAB-DETR-R101 — 44.1 (2022-01-28) CAE (ViT-L, Mask R-CNN, 1x schedule) — 54.5 (2022-02-07) CAE (ViT-L, Mask R-CNN, 1x schedule) — 54.5 (2022-02-07) CAE (ViT-L, Mask R-CNN, 1x schedule) — 54.5 (2022-02-07) CAE (ViT-L, Mask R-CNN, 1x schedule) — 54.5 (2022-02-07) CAE (ViT-L, Mask R-CNN, 1x schedule) — 54.5 (2022-02-07) DN-Deformable-DETR-R50++ — 49.5 (2022-03-02) DN-Deformable-DETR-R50++ — 49.5 (2022-03-02) DN-Deformable-DETR-R50++ — 49.5 (2022-03-02) DN-Deformable-DETR-R50++ — 49.5 (2022-03-02) DN-Deformable-DETR-R50++ — 49.5 (2022-03-02) DINO (Swin-L) — 63.2 (2022-03-07) DINO-5scale (24 epoch) — 51.3 (2022-03-07) DINO-5scale (36 epoch) — 51.2 (2022-03-07) DINO (Swin-L) — 63.2 (2022-03-07) DINO-5scale (24 epoch) — 51.3 (2022-03-07) DINO-5scale (36 epoch) — 51.2 (2022-03-07) DINO (Swin-L) — 63.2 (2022-03-07) DINO-5scale (24 epoch) — 51.3 (2022-03-07) DINO-5scale (36 epoch) — 51.2 (2022-03-07) DINO (Swin-L) — 63.2 (2022-03-07) DINO-5scale (24 epoch) — 51.3 (2022-03-07) DINO-5scale (36 epoch) — 51.2 (2022-03-07) DINO (Swin-L) — 63.2 (2022-03-07) DINO-5scale (24 epoch) — 51.3 (2022-03-07) DINO-5scale (36 epoch) — 51.2 (2022-03-07) ActiveMLP-B (Cascade Mask R-CNN) — 52.3 (2022-03-11) ActiveMLP-B (Cascade Mask R-CNN) — 52.3 (2022-03-11) ActiveMLP-B (Cascade Mask R-CNN) — 52.3 (2022-03-11) ActiveMLP-B (Cascade Mask R-CNN) — 52.3 (2022-03-11) ActiveMLP-B (Cascade Mask R-CNN) — 52.3 (2022-03-11) FocalNet-H (DINO) — 64.2 (2022-03-22) FocalNet-T (LRF, Cascade Mask R-CNN) — 51.5 (2022-03-22) FocalNet-H (DINO) — 64.2 (2022-03-22) FocalNet-T (LRF, Cascade Mask R-CNN) — 51.5 (2022-03-22) FocalNet-H (DINO) — 64.2 (2022-03-22) FocalNet-T (LRF, Cascade Mask R-CNN) — 51.5 (2022-03-22) FocalNet-H (DINO) — 64.2 (2022-03-22) FocalNet-T (LRF, Cascade Mask R-CNN) — 51.5 (2022-03-22) FocalNet-H (DINO) — 64.2 (2022-03-22) FocalNet-T (LRF, Cascade Mask R-CNN) — 51.5 (2022-03-22) ViTDet, ViT-H Cascade (multiscale) — 61.3 (2022-03-30) ViTDet, ViT-H Cascade — 60.4 (2022-03-30) ViTDet, ViT-H Cascade (multiscale) — 61.3 (2022-03-30) ViTDet, ViT-H Cascade — 60.4 (2022-03-30) ViTDet, ViT-H Cascade (multiscale) — 61.3 (2022-03-30) ViTDet, ViT-H Cascade — 60.4 (2022-03-30) ViTDet, ViT-H Cascade (multiscale) — 61.3 (2022-03-30) ViTDet, ViT-H Cascade — 60.4 (2022-03-30) ViTDet, ViT-H Cascade (multiscale) — 61.3 (2022-03-30) ViTDet, ViT-H Cascade — 60.4 (2022-03-30) DaViT-T (Mask R-CNN, 36 epochs) — 49.9 (2022-04-07) DaViT-T (Mask R-CNN, 36 epochs) — 49.9 (2022-04-07) DaViT-T (Mask R-CNN, 36 epochs) — 49.9 (2022-04-07) DaViT-T (Mask R-CNN, 36 epochs) — 49.9 (2022-04-07) DaViT-T (Mask R-CNN, 36 epochs) — 49.9 (2022-04-07) FAN-L-Hybrid — 55.1 (2022-04-26) FAN-L-Hybrid — 55.1 (2022-04-26) FAN-L-Hybrid — 55.1 (2022-04-26) FAN-L-Hybrid — 55.1 (2022-04-26) FAN-L-Hybrid — 55.1 (2022-04-26) ViT-Adapter-L (HTC++, BEiTv2 pretrain, multi-scale) — 60.5 (2022-05-17) ViT-Adapter-L (HTC++, BEiT pretrain, multi-scale) — 60.2 (2022-05-17) ViT-Adapter-L (HTC++, BEiTv2 pretrain, multi-scale) — 60.5 (2022-05-17) ViT-Adapter-L (HTC++, BEiT pretrain, multi-scale) — 60.2 (2022-05-17) ViT-Adapter-L (HTC++, BEiTv2 pretrain, multi-scale) — 60.5 (2022-05-17) ViT-Adapter-L (HTC++, BEiT pretrain, multi-scale) — 60.2 (2022-05-17) ViT-Adapter-L (HTC++, BEiTv2 pretrain, multi-scale) — 60.5 (2022-05-17) ViT-Adapter-L (HTC++, BEiT pretrain, multi-scale) — 60.2 (2022-05-17) ViT-Adapter-L (HTC++, BEiTv2 pretrain, multi-scale) — 60.5 (2022-05-17) ViT-Adapter-L (HTC++, BEiT pretrain, multi-scale) — 60.2 (2022-05-17) UM-MAE(HTC++, Swin-L, IN1K) — 57.4 (2022-05-20) UM-MAE(HTC++, Swin-L, IN1K) — 57.4 (2022-05-20) UM-MAE(HTC++, Swin-L, IN1K) — 57.4 (2022-05-20) UM-MAE(HTC++, Swin-L, IN1K) — 57.4 (2022-05-20) UM-MAE(HTC++, Swin-L, IN1K) — 57.4 (2022-05-20) HorNet-L — 59.2 (2022-07-28) HorNet-L — 59.2 (2022-07-28) HorNet-L — 59.2 (2022-07-28) HorNet-L — 59.2 (2022-07-28) HorNet-L — 59.2 (2022-07-28) MOAT-3 (IN-22K pretraining, single-scale) — 59.2 (2022-10-04) MOAT-2 (IN-22K pretraining, single-scale) — 58.5 (2022-10-04) MOAT-1 (IN-1K pretraining, single-scale) — 57.7 (2022-10-04) MOAT-0 (IN-1K pretraining, single-scale) — 55.9 (2022-10-04) tiny-MOAT-3 (IN-1K pretraining, single-scale) — 55.2 (2022-10-04) tiny-MOAT-2 (IN-1K pretraining, single-scale) — 53.0 (2022-10-04) tiny-MOAT-1 (IN-1K pretraining, single-scale) — 51.9 (2022-10-04) tiny-MOAT-0 (IN-1K pretraining, single-scale) — 50.5 (2022-10-04) MOAT-3 (IN-22K pretraining, single-scale) — 59.2 (2022-10-04) MOAT-2 (IN-22K pretraining, single-scale) — 58.5 (2022-10-04) MOAT-1 (IN-1K pretraining, single-scale) — 57.7 (2022-10-04) MOAT-0 (IN-1K pretraining, single-scale) — 55.9 (2022-10-04) tiny-MOAT-3 (IN-1K pretraining, single-scale) — 55.2 (2022-10-04) tiny-MOAT-2 (IN-1K pretraining, single-scale) — 53.0 (2022-10-04) tiny-MOAT-1 (IN-1K pretraining, single-scale) — 51.9 (2022-10-04) tiny-MOAT-0 (IN-1K pretraining, single-scale) — 50.5 (2022-10-04) MOAT-3 (IN-22K pretraining, single-scale) — 59.2 (2022-10-04) MOAT-2 (IN-22K pretraining, single-scale) — 58.5 (2022-10-04) MOAT-1 (IN-1K pretraining, single-scale) — 57.7 (2022-10-04) MOAT-0 (IN-1K pretraining, single-scale) — 55.9 (2022-10-04) tiny-MOAT-3 (IN-1K pretraining, single-scale) — 55.2 (2022-10-04) tiny-MOAT-2 (IN-1K pretraining, single-scale) — 53.0 (2022-10-04) tiny-MOAT-1 (IN-1K pretraining, single-scale) — 51.9 (2022-10-04) tiny-MOAT-0 (IN-1K pretraining, single-scale) — 50.5 (2022-10-04) MOAT-3 (IN-22K pretraining, single-scale) — 59.2 (2022-10-04) MOAT-2 (IN-22K pretraining, single-scale) — 58.5 (2022-10-04) MOAT-1 (IN-1K pretraining, single-scale) — 57.7 (2022-10-04) MOAT-0 (IN-1K pretraining, single-scale) — 55.9 (2022-10-04) tiny-MOAT-3 (IN-1K pretraining, single-scale) — 55.2 (2022-10-04) tiny-MOAT-2 (IN-1K pretraining, single-scale) — 53.0 (2022-10-04) tiny-MOAT-1 (IN-1K pretraining, single-scale) — 51.9 (2022-10-04) tiny-MOAT-0 (IN-1K pretraining, single-scale) — 50.5 (2022-10-04) MOAT-3 (IN-22K pretraining, single-scale) — 59.2 (2022-10-04) MOAT-2 (IN-22K pretraining, single-scale) — 58.5 (2022-10-04) MOAT-1 (IN-1K pretraining, single-scale) — 57.7 (2022-10-04) MOAT-0 (IN-1K pretraining, single-scale) — 55.9 (2022-10-04) tiny-MOAT-3 (IN-1K pretraining, single-scale) — 55.2 (2022-10-04) tiny-MOAT-2 (IN-1K pretraining, single-scale) — 53.0 (2022-10-04) tiny-MOAT-1 (IN-1K pretraining, single-scale) — 51.9 (2022-10-04) tiny-MOAT-0 (IN-1K pretraining, single-scale) — 50.5 (2022-10-04) TEC(VIT-B, Mask-RCNN) — 54.6 (2022-10-20) TEC(VIT-B, Mask-RCNN) — 54.6 (2022-10-20) TEC(VIT-B, Mask-RCNN) — 54.6 (2022-10-20) TEC(VIT-B, Mask-RCNN) — 54.6 (2022-10-20) TEC(VIT-B, Mask-RCNN) — 54.6 (2022-10-20) Frozen Backbone, SwinV2-G-ext22K (HTC) — 59.3 (2022-11-03) Frozen Backbone, SwinV2-G-ext22K (HTC) — 59.3 (2022-11-03) Frozen Backbone, SwinV2-G-ext22K (HTC) — 59.3 (2022-11-03) Frozen Backbone, SwinV2-G-ext22K (HTC) — 59.3 (2022-11-03) Frozen Backbone, SwinV2-G-ext22K (HTC) — 59.3 (2022-11-03) InternImage-H — 65.0 (2022-11-10) InternImage-XL — 64.2 (2022-11-10) InternImage-H — 65.0 (2022-11-10) InternImage-XL — 64.2 (2022-11-10) InternImage-H — 65.0 (2022-11-10) InternImage-XL — 64.2 (2022-11-10) InternImage-H — 65.0 (2022-11-10) InternImage-XL — 64.2 (2022-11-10) InternImage-H — 65.0 (2022-11-10) InternImage-XL — 64.2 (2022-11-10) EVA — 64.5 (2022-11-14) EVA — 64.5 (2022-11-14) EVA — 64.5 (2022-11-14) EVA — 64.5 (2022-11-14) EVA — 64.5 (2022-11-14) M3I Pre-training (InternImage-H) — 65.0 (2022-11-17) M3I Pre-training (InternImage-H) — 65.0 (2022-11-17) M3I Pre-training (InternImage-H) — 65.0 (2022-11-17) M3I Pre-training (InternImage-H) — 65.0 (2022-11-17) M3I Pre-training (InternImage-H) — 65.0 (2022-11-17) Co-DETR — 65.9 (2022-11-22) Co-DETR (Swin-L) — 64.7 (2022-11-22) Co-DETR — 65.9 (2022-11-22) Co-DETR (Swin-L) — 64.7 (2022-11-22) Co-DETR — 65.9 (2022-11-22) Co-DETR (Swin-L) — 64.7 (2022-11-22) Co-DETR — 65.9 (2022-11-22) Co-DETR (Swin-L) — 64.7 (2022-11-22) Co-DETR — 65.9 (2022-11-22) Co-DETR (Swin-L) — 64.7 (2022-11-22) RevCol-H(DINO) — 63.8 (2022-12-22) RevCol-H(DINO) — 63.8 (2022-12-22) RevCol-H(DINO) — 63.8 (2022-12-22) RevCol-H(DINO) — 63.8 (2022-12-22) RevCol-H(DINO) — 63.8 (2022-12-22) YOLOv6-L6(46 fps, 1280, V100) — 57.2 (2023-01-13) YOLOv6-L6(46 fps, 1280, V100) — 57.2 (2023-01-13) YOLOv6-L6(46 fps, 1280, V100) — 57.2 (2023-01-13) YOLOv6-L6(46 fps, 1280, V100) — 57.2 (2023-01-13) YOLOv6-L6(46 fps, 1280, V100) — 57.2 (2023-01-13) Grounding DINO — 63.0 (2023-03-09) Grounding DINO — 63.0 (2023-03-09) Grounding DINO — 63.0 (2023-03-09) Grounding DINO — 63.0 (2023-03-09) Grounding DINO — 63.0 (2023-03-09) UNINEXT-H — 60.6 (2023-03-12) UNINEXT-H — 60.6 (2023-03-12) UNINEXT-H — 60.6 (2023-03-12) UNINEXT-H — 60.6 (2023-03-12) UNINEXT-H — 60.6 (2023-03-12) Focal-Stable-DINO (Focal-Huge, no TTA) — 64.6 (2023-04-25) Focal-Stable-DINO (Focal-Huge, no TTA) — 64.6 (2023-04-25) Focal-Stable-DINO (Focal-Huge, no TTA) — 64.6 (2023-04-25) Focal-Stable-DINO (Focal-Huge, no TTA) — 64.6 (2023-04-25) Focal-Stable-DINO (Focal-Huge, no TTA) — 64.6 (2023-04-25) Hiera-L — 55.0 (2023-06-01) Hiera-L — 55.0 (2023-06-01) Hiera-L — 55.0 (2023-06-01) Hiera-L — 55.0 (2023-06-01) Hiera-L — 55.0 (2023-06-01) TransNeXt-Base (IN-1K pretrain, DINO 1x) — 57.1 (2023-11-28) TransNeXt-Small (IN-1K pretrain, DINO 1x) — 56.6 (2023-11-28) TransNeXt-Tiny (IN-1K pretrain, DINO 1x) — 55.7 (2023-11-28) TransNeXt-Base (IN-1K pretrain, DINO 1x) — 57.1 (2023-11-28) TransNeXt-Small (IN-1K pretrain, DINO 1x) — 56.6 (2023-11-28) TransNeXt-Tiny (IN-1K pretrain, DINO 1x) — 55.7 (2023-11-28) TransNeXt-Base (IN-1K pretrain, DINO 1x) — 57.1 (2023-11-28) TransNeXt-Small (IN-1K pretrain, DINO 1x) — 56.6 (2023-11-28) TransNeXt-Tiny (IN-1K pretrain, DINO 1x) — 55.7 (2023-11-28) TransNeXt-Base (IN-1K pretrain, DINO 1x) — 57.1 (2023-11-28) TransNeXt-Small (IN-1K pretrain, DINO 1x) — 56.6 (2023-11-28) TransNeXt-Tiny (IN-1K pretrain, DINO 1x) — 55.7 (2023-11-28) TransNeXt-Base (IN-1K pretrain, DINO 1x) — 57.1 (2023-11-28) TransNeXt-Small (IN-1K pretrain, DINO 1x) — 56.6 (2023-11-28) TransNeXt-Tiny (IN-1K pretrain, DINO 1x) — 55.7 (2023-11-28) GLEE-Pro — 62.0 (2023-12-14) GLEE-Plus — 60.4 (2023-12-14) GLEE-Lite — 55.0 (2023-12-14) GLEE-Pro — 62.0 (2023-12-14) GLEE-Plus — 60.4 (2023-12-14) GLEE-Lite — 55.0 (2023-12-14) GLEE-Pro — 62.0 (2023-12-14) GLEE-Plus — 60.4 (2023-12-14) GLEE-Lite — 55.0 (2023-12-14) GLEE-Pro — 62.0 (2023-12-14) GLEE-Plus — 60.4 (2023-12-14) GLEE-Lite — 55.0 (2023-12-14) GLEE-Pro — 62.0 (2023-12-14) GLEE-Plus — 60.4 (2023-12-14) GLEE-Lite — 55.0 (2023-12-14) ViT-CoMer — 64.3 (2024-03-13) ViT-CoMer — 64.3 (2024-03-13) ViT-CoMer — 64.3 (2024-03-13) ViT-CoMer — 64.3 (2024-03-13) ViT-CoMer — 64.3 (2024-03-13) CP-DETR-L Swin-L(Fine tuning separately in COCO) — 64.1 (2024-12-13) CP-DETR-L Swin-L(Fine tuning separately in COCO) — 64.1 (2024-12-13) CP-DETR-L Swin-L(Fine tuning separately in COCO) — 64.1 (2024-12-13) CP-DETR-L Swin-L(Fine tuning separately in COCO) — 64.1 (2024-12-13) CP-DETR-L Swin-L(Fine tuning separately in COCO) — 64.1 (2024-12-13) PE_spatial (DETA) — 66.0 (2025-04-17) PE_spatial (DETA) — 66.0 (2025-04-17) PE_spatial (DETA) — 66.0 (2025-04-17) PE_spatial (DETA) — 66.0 (2025-04-17) PE_spatial (DETA) — 66.0 (2025-04-17) Cascade Mask R-CNN (ResNet-50) — 46.3 (2015-12-10) Mask R-CNN (ResNeXt-152-FPN, cascade) — 48.6 (2018-11-21) EfficientDet-D7 (1536) — 52.1 (2019-11-20) RetinaNet (SpineNet-190, 1536x1536) — 52.2 (2019-12-10) ResNeSt-200 (multi-scale) — 52.47 (2020-04-19) SpineNet-190 (1280, with Self-training on OpenImages, single-scale) — 54.2 (2020-06-11) YOLOv4-P7 CSP-P7 (single-scale, 16 fps) — 55.4 (2020-11-16) Cascade Eff-B7 NAS-FPN (1280, self-training Copy Paste, single-scale) — 57.0 (2020-12-13) Swin-L (HTC++, multi scale) — 58.0 (2021-03-25) DyHead (Swin-L, multi scale, self-training) — 60.3 (2021-06-15) Soft Teacher + Swin-L (HTC++, multi-scale) — 60.7 (2021-06-16) SwinV2-G (HTC++) — 62.5 (2021-11-18) DINO (Swin-L) — 63.2 (2022-03-07) FocalNet-H (DINO) — 64.2 (2022-03-22) InternImage-H — 65.0 (2022-11-10) Co-DETR — 65.9 (2022-11-22) PE_spatial (DETA) — 66.0 (2025-04-17)
RankModel box APAP50AP75APSAPMAPLParams (M) PaperCodeYear
101 RPDet (ResNeXt-101-DCN, multi-scale) 46.8–––––– RepPoints: Point Set Representation for Object Detection open-mmlab/mmdetection · microsoft/RepPoints · Scalsol/RepPointsV2 · +3 2019
102 DAB-DETR-DC5-R101 46.66750.228.150.564.163 DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR IDEA-Research/detrex · alibaba/EasyCV · idea-research/dn-detr · +5 2022
103 DyHead (ResNet-101) 46.5–––––– Dynamic Head: Unifying Object Detection Heads with Attentions open-mmlab/mmdetection · microsoft/DynamicHead · Coldestadam/DynamicHead 2021
104 Mask R-CNN (ResNeXt-152-FPN) 46.467.151.1–––– Rethinking ImageNet Pre-training tensorpack/tensorpack 2018
104 RPDet (ResNet-101-DCN, multi-scale) 46.4–––––– RepPoints: Point Set Representation for Object Detection open-mmlab/mmdetection · microsoft/RepPoints · Scalsol/RepPointsV2 · +3 2019
104 PatchConvNet-S60 (Mask R-CNN) 46.4–––––– Augmenting Convolutional networks with attention-based aggregation facebookresearch/deit · keras-team/keras-io · DarshanDeshpande/jax-models · +2 2021
107 Cascade Mask R-CNN (ResNet-50) 46.364.350.5–––– Deep Residual Learning for Image Recognition tensorflow/models · tensorflow/models · tensorflow/models · +481 2015
108 HoughNet (HG-104, MS) 46.164.650.330.048.859.7– HoughNet: Integrating near and long-range evidence for bottom-up object detection giddyyupp/coco-minitrain · nerminsamet/houghnet 2020
109 Mask R-CNN (HRNetV2p-W48, cascade) 46.0––27.5–60.1– Deep High-Resolution Representation Learning for Visual Recognition open-mmlab/mmdetection · PaddlePaddle/PaddleDetection · open-mmlab/mmsegmentation · +39 2019
110 Conditional DETR-DC5-R101 45.966.849.527.250.363.363 Conditional DETR for Fast Training Convergence huggingface/transformers · IDEA-Research/detrex · atten4vis/conditionaldetr · +1 2021
110 BoTNet 50 (72 epochs) 45.9–––––– Bottleneck Transformers for Visual Recognition rwightman/pytorch-image-models · BR-IDL/PaddleViT · The-AI-Summer/self_attention · +10 2021
112 Sparse R-CNN (ResNet-101, learnable proposals, random crop aug, FPN) 45.664.649.528.348.361.6– Sparse R-CNN: End-to-End Object Detection with Learnable Proposals open-mmlab/mmdetection · PaddlePaddle/PaddleDetection · PeizeSun/SparseR-CNN · +3 2020
112 CenterMask+VoVNetV2-99 (single-scale) 45.6––29.2–58.8– CenterMask : Real-Time Anchor-Free Instance Segmentation youngwanLEE/centermask2 · youngwanLEE/CenterMask · youngwanLEE/vovnet-detectron2 · +5 2019
114 HTC (HRNetV2p-W32) 45.3––27.048.459.5– Deep High-Resolution Representation Learning for Visual Recognition open-mmlab/mmdetection · PaddlePaddle/PaddleDetection · open-mmlab/mmsegmentation · +39 2019
115 Anchor DETR-DC5-R101 45.165.748.825.849.461.6– Anchor DETR: Query Design for Transformer-Based Object Detection megvii-research/AnchorDETR · megvii-model/anchordetr 2021
115 Conditional DETR-DC5-R50 45.165.448.525.34962.244 Conditional DETR for Fast Training Convergence huggingface/transformers · IDEA-Research/detrex · atten4vis/conditionaldetr · +1 2021
117 Mask R-CNN (ResNeXt-152 + 1 NL) 45.067.848.9–––– Non-local Neural Networks facebookresearch/detectron · facebookresearch/SlowFast · open-mmlab/mmaction2 · +29 2017
117 Pix2seq (R101-DC5) 45.063.248.628.248.960.4– Pix2seq: A Language Modeling Framework for Object Detection google-research/pix2seq · gaopengcuhk/Stable-Pix2Seq · gaopengcuhk/Unofficial-Pix2Seq · +3 2021
119 Mask R-CNN-FPN (AOGNet-40M) 44.966.249.1–––– Attentive Normalization iVMCL/AOGNet-v2 · ivMCL/AttentiveNorm_Detection 2019
119 DETR-DC5 (ResNet-101) 44.964.747.723.749.562.3– End-to-End Object Detection with Transformers huggingface/transformers · tensorflow/models · open-mmlab/mmdetection · +34 2020
119 Mask R-CNN (VoVNetV2-99, single-scale) 44.9––28.5–57.7– CenterMask : Real-Time Anchor-Free Instance Segmentation youngwanLEE/centermask2 · youngwanLEE/CenterMask · youngwanLEE/vovnet-detectron2 · +5 2019
122 R3-CNN (ResNet-50-FPN, DCN) 44.864.348.926.648.359.6– Recursively Refined R-CNN: Instance Segmentation with Self-RoI Rebalancing IMPLabUniPr/mmdetection 2021
122 RPDet (ResNet-101-DCN, multi-scale train) 44.8–––––– RepPoints: Point Set Representation for Object Detection open-mmlab/mmdetection · microsoft/RepPoints · Scalsol/RepPointsV2 · +3 2019
124 RetinaNet (ViL-Base, multi-scale, 3x) 44.7–47.629.94858.1– Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding microsoft/esvit · microsoft/vision-longformer · microsoft/VisionLongformerForObjectDetection 2021
125 Cascade R-CNN (HRNetV2p-W48) 44.662.748.726.348.158.5– Deep High-Resolution Representation Learning for Visual Recognition open-mmlab/mmdetection · PaddlePaddle/PaddleDetection · open-mmlab/mmsegmentation · +39 2019
125 CenterMask+VoVNetV2-57 (single-scale) 44.6––27.748.3–– CenterMask : Real-Time Anchor-Free Instance Segmentation youngwanLEE/centermask2 · youngwanLEE/CenterMask · youngwanLEE/vovnet-detectron2 · +5 2019
127 Conditional DETR-R101 44.565.647.523.648.463.663 Conditional DETR for Fast Training Convergence huggingface/transformers · IDEA-Research/detrex · atten4vis/conditionaldetr · +1 2021
127 Sparse R-CNN (ResNet-50, learnable proposals, random crop aug, FPN) 44.563.448.226.947.259.5– Sparse R-CNN: End-to-End Object Detection with Learnable Proposals open-mmlab/mmdetection · PaddlePaddle/PaddleDetection · PeizeSun/SparseR-CNN · +3 2020
127 GFL (ResNet-50) 44.563.048.3–––– Deep Residual Learning for Image Recognition tensorflow/models · tensorflow/models · tensorflow/models · +481 2015
127 RPDet (ResNeXt-101-DCN) 44.5–––––– RepPoints: Point Set Representation for Object Detection open-mmlab/mmdetection · microsoft/RepPoints · Scalsol/RepPointsV2 · +3 2019
131 CenterMask+X101-32x8d (single-scale) 44.4––26.7–57.1– CenterMask : Real-Time Anchor-Free Instance Segmentation youngwanLEE/centermask2 · youngwanLEE/CenterMask · youngwanLEE/vovnet-detectron2 · +5 2019
132 RetinaNet (ViL-Base) 44.365.547.128.947.958.3– Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding microsoft/esvit · microsoft/vision-longformer · microsoft/VisionLongformerForObjectDetection 2021
132 R3-CNN (ResNet-50-FPN, GC-Net) 44.364.148.42747.158.9– Recursively Refined R-CNN: Instance Segmentation with Self-RoI Rebalancing IMPLabUniPr/mmdetection 2021
134 Anchor DETR-DC5-R50 44.264.747.524.748.260.6– Anchor DETR: Query Design for Transformer-Based Object Detection megvii-research/AnchorDETR · megvii-model/anchordetr 2021
135 DAB-DETR-R101 44.164.747.224.148.262.963 DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR IDEA-Research/detrex · alibaba/EasyCV · idea-research/dn-detr · +5 2022
136 Faster RCNN-R101-FPN+ 4463.947.827.248.156– End-to-End Object Detection with Transformers huggingface/transformers · tensorflow/models · open-mmlab/mmdetection · +34 2020
137 Cascade R-CNN (HRNetV2p-W32) 43.761.747.725.646.557.4– Deep High-Resolution Representation Learning for Visual Recognition open-mmlab/mmdetection · PaddlePaddle/PaddleDetection · open-mmlab/mmsegmentation · +39 2019
138 Sparse R-CNN (ResNet-101, FPN) 43.562.147.226.146.359.7– Sparse R-CNN: End-to-End Object Detection with Learnable Proposals open-mmlab/mmdetection · PaddlePaddle/PaddleDetection · PeizeSun/SparseR-CNN · +3 2020
138 ATSS (ResNet-50) 43.561.947.0–––– Deep Residual Learning for Image Recognition tensorflow/models · tensorflow/models · tensorflow/models · +481 2015
140 PVT-Large (RetinaNet 3x,MS) 43.463.646.126.146.059.5– Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions open-mmlab/mmdetection · open-mmlab/mmpose · whai362/PVT · +8 2021
141 ExtremeNet (Hourglass-104, multi-scale) 43.359.646.825.746.659.4– Bottom-up Object Detection by Grouping Extreme and Center Points xingyizhou/ExtremeNet · DataXujing/ExtremeNet-Pytorch 2019
142 Pix2seq (R50-DC5 ) 43.261.046.126.64758.6– Pix2seq: A Language Modeling Framework for Object Detection google-research/pix2seq · gaopengcuhk/Stable-Pix2Seq · gaopengcuhk/Unofficial-Pix2Seq · +3 2021
142 HTC (cascade) 43.259.440.720.340.952.3– Hybrid Task Cascade for Instance Segmentation open-mmlab/mmdetection · PaddlePaddle/PaddleDetection · amirassov/kaggle-imaterialist · +2 2019
144 Mask R-CNN-FPN (ResNeXt-101, GN+WS) 43.1264.1547.1125.4947.1956.39– Micro-Batch Training with Batch-Channel Normalization and Weight Standardization labmlai/annotated_deep_learning_paper_implementations · joe-siyuan-qiao/WeightStandardization · jinfagang/nb · +4 2019
145 HTC (HRNetV2p-W18) 43.1––26.646.0–– Deep High-Resolution Representation Learning for Visual Recognition open-mmlab/mmdetection · PaddlePaddle/PaddleDetection · open-mmlab/mmsegmentation · +39 2019
145 Mask R-CNN (ResNet-101, DCNv2) 43.1–––––– Deformable ConvNets v2: More Deformable, Better Results open-mmlab/mmdetection · PaddlePaddle/PaddleDetection · msracver/Deformable-ConvNets · +23 2018
147 Conditional DETR-R50 436445.722.746.761.544 Conditional DETR for Fast Training Convergence huggingface/transformers · IDEA-Research/detrex · atten4vis/conditionaldetr · +1 2021
147 HoughNet (HG-104) 43.062.246.925.547.655.8– HoughNet: Integrating near and long-range evidence for bottom-up object detection giddyyupp/coco-minitrain · nerminsamet/houghnet 2020
149 Faster R-CNN (FPN, X-volution) 42.86446.426.94655– X-volution: On the unification of convolution and self-attention 2021
150 Cascade R-CNN (ResNet-101-FPN+, cascade) 42.761.646.623.846.257.4– Cascade R-CNN: Delving into High Quality Object Detection open-mmlab/mmdetection · PaddlePaddle/PaddleDetection · tensorpack/tensorpack · +5 2017
151 PVT-Large (RetinaNet 1x) 42.663.745.425.846.058.4– Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions open-mmlab/mmdetection · open-mmlab/mmpose · whai362/PVT · +8 2021
151 CornerNet-Saccade (Hourglass-54) 42.6––25.544.358.4– CornerNet-Lite: Efficient Keypoint Based Object Detection PaddlePaddle/PaddleDetection · princeton-vl/CornerNet-Lite · takooctopus/CornerNet-Lite-Tako · +3 2019
151 Pix2seq (R50) 42.6–––––– Pix2seq: A Language Modeling Framework for Object Detection google-research/pix2seq · gaopengcuhk/Stable-Pix2Seq · gaopengcuhk/Unofficial-Pix2Seq · +3 2021
154 Mask R-CNN (ResNet-101-FPN, GroupNorm, long) 42.362.846.2–––– Group Normalization labmlai/annotated_deep_learning_paper_implementations · facebookresearch/detectron · PaddlePaddle/PaddleDetection · +19 2018
154 Sparse R-CNN (ResNet-50, FPN) 42.361.245.726.744.657.6– Sparse R-CNN: End-to-End Object Detection with Learnable Proposals open-mmlab/mmdetection · PaddlePaddle/PaddleDetection · PeizeSun/SparseR-CNN · +3 2020
154 Mask R-CNN (HRNetV2p-W32) 42.3––25.045.4–– Deep High-Resolution Representation Learning for Visual Recognition open-mmlab/mmdetection · PaddlePaddle/PaddleDetection · open-mmlab/mmsegmentation · +39 2019
154 DETR-ResNet50 with iRPE-K (300 epochs) 42.3–––––– Rethinking and Improving Relative Position Encoding for Vision Transformer microsoft/cream 2021
158 TridentNet (ResNet-101) 4263.545.524.94756.9– Scale-Aware Trident Networks for Object Detection facebookresearch/detectron2 · open-mmlab/mmdetection · tusimple/simpledet · +1 2019
158 R3-CNN (ResNet-50-FPN) 426146.324.545.255.7– Recursively Refined R-CNN: Instance Segmentation with Self-RoI Rebalancing IMPLabUniPr/mmdetection 2021
160 Faster R-CNN (HRNetV2p-W48) 41.862.845.9–44.754.6– Deep High-Resolution Representation Learning for Visual Recognition open-mmlab/mmdetection · PaddlePaddle/PaddleDetection · open-mmlab/mmsegmentation · +39 2019
161 Faster R-CNN (LIP-ResNet-101) 41.763.645.625.245.8–– LIP: Local Importance-based Pooling sebgao/LIP 2019
161 Faster R-CNN (ResNet-101, DCNv2) 41.7––22.245.858.7– Deformable ConvNets v2: More Deformable, Better Results open-mmlab/mmdetection · PaddlePaddle/PaddleDetection · msracver/Deformable-ConvNets · +23 2018
163 FSAF (ResNeXt-101, anchor-based branches) 41.662.4––––– Feature Selective Anchor-Free Module for Single-Shot Object Detection open-mmlab/mmdetection · hdjang/Feature-Selective-Anchor-Free-Module-for-Single-Shot-Object-Detection · xuannianz/FSAF · +1 2019
164 CornerNet-Saccade (Hourglass-104) 41.4––23.843.557.1– CornerNet-Lite: Efficient Keypoint Based Object Detection PaddlePaddle/PaddleDetection · princeton-vl/CornerNet-Lite · takooctopus/CornerNet-Lite-Tako · +3 2019
165 Grid R-CNN (ResNet-101-FPN) 41.360.344.423.445.854.1– Grid R-CNN open-mmlab/mmdetection · STVIR/Grid-R-CNN 2018
165 Cascade R-CNN (HRNetV2p-W18) 41.359.244.923.744.254.1– Deep High-Resolution Representation Learning for Visual Recognition open-mmlab/mmdetection · PaddlePaddle/PaddleDetection · open-mmlab/mmsegmentation · +39 2019
165 CenterNet511 (Hourglass-52) 41.359.243.923.643.855.8– CenterNet: Keypoint Triplets for Object Detection Duankaiwen/CenterNet · ximilar-com/xcenternet · kuku-sichuan/CenterNet · +17 2019
168 RetinaMask (ResNet-101-FPN) 41.160.244.1–––– RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free chengyangfu/retinamask · lzrobots/dgmn · oulutan/Drone_FasterRCNN · +50 2019
169 PoolFormer-S36 (Mask R-CNN) 41.063.144.8–––– MetaFormer Is Actually What You Need for Vision huggingface/transformers · rwightman/pytorch-image-models · facebookresearch/xformers · +15 2021
170 Faster R-CNN (HRNetV2p-W32) 40.961.844.824.443.753.3– Deep High-Resolution Representation Learning for Visual Recognition open-mmlab/mmdetection · PaddlePaddle/PaddleDetection · open-mmlab/mmsegmentation · +39 2019
170 VirTex Mask R-CNN (ResNet-50-FPN) 40.9–––––– VirTex: Learning Visual Representations from Textual Annotations kdexd/virtex · mattdeitke/cvpr-buzz · rahulvigneswaran/longtail-buzz 2020
172 Mask R-CNN (ResNet-101 + 1 NL) 40.863.144.5–––– Non-local Neural Networks facebookresearch/detectron · facebookresearch/SlowFast · open-mmlab/mmaction2 · +29 2017
172 Mask R-CNN (ResNet-50-FPN, GroupNorm, long) 40.861.644.4–––– Group Normalization labmlai/annotated_deep_learning_paper_implementations · facebookresearch/detectron · PaddlePaddle/PaddleDetection · +19 2018
172 RPDet (ResNet-50, multi-scale train) 40.8–––––– RepPoints: Point Set Representation for Object Detection open-mmlab/mmdetection · microsoft/RepPoints · Scalsol/RepPointsV2 · +3 2019
172 DETR-ResNet50 with iRPE-K (150 epochs) 40.8–––––– Rethinking and Improving Relative Position Encoding for Vision Transformer microsoft/cream 2021
176 Faster R-CNN+aLRP Loss (ResNet-50, 500 scale) 40.760.743.3–––– A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection kemaloksuz/aLRPLoss · xudangliatiger/ape-loss · kemaloksuz/aLRPLoss-AblationExperiments 2020
177 PPDet (ResNet-101-FPN) 40.559.544.225.444.752.3– Reducing Label Noise in Anchor-Free Object Detection nerminsamet/ppdet 2020
178 GCnet (ResNet-50-FPN, GRoIE) 40.362.44424.244.452.5– GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond open-mmlab/mmdetection · open-mmlab/mmsegmentation · PaddlePaddle/PaddleSeg · +6 2019
178 Mask R-CNN (ResNet-50-FPN, GroupNorm) 40.36144–––– Group Normalization labmlai/annotated_deep_learning_paper_implementations · facebookresearch/detectron · PaddlePaddle/PaddleDetection · +19 2018
178 Cascade R-CNN (ResNet-50-FPN+) 40.3 59.443.722.943.754.1– Cascade R-CNN: Delving into High Quality Object Detection open-mmlab/mmdetection · PaddlePaddle/PaddleDetection · tensorpack/tensorpack · +5 2017
178 ExtremeNet (Hourglass-104, single-scale) 40.355.143.721.644.056.1– Bottom-up Object Detection by Grouping Extreme and Center Points xingyizhou/ExtremeNet · DataXujing/ExtremeNet-Pytorch 2019
178 RPDet (ResNet-101) 40.3–––––– RepPoints: Point Set Representation for Object Detection open-mmlab/mmdetection · microsoft/RepPoints · Scalsol/RepPointsV2 · +3 2019
183 RetinaNet+aLRP Loss (ResNet-50, 500 scale) 40.260.342.3–––– A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection kemaloksuz/aLRPLoss · xudangliatiger/ape-loss · kemaloksuz/aLRPLoss-AblationExperiments 2020
184 Mask R-CNN (ResNet-101-FPN) 40.0–––––– Mask R-CNN tensorflow/models · facebookresearch/detectron2 · facebookresearch/detectron · +176 2017
185 FPN+ 39.861.343.322.943.352.6– Feature Pyramid Networks for Object Detection PaddlePaddle/PaddleOCR · open-mmlab/mmdetection · facebookresearch/detectron · +82 2016
186 FoveaBox+aLRP Loss (ResNet-50, 500 scale) 39.758.841.5–––– A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection kemaloksuz/aLRPLoss · xudangliatiger/ape-loss · kemaloksuz/aLRPLoss-AblationExperiments 2020
187 Grid R-CNN (ResNet-50-FPN) 39.658.342.422.643.851.5– Grid R-CNN open-mmlab/mmdetection · STVIR/Grid-R-CNN 2018
188 Mask R-CNN (ResNet-50, ACNet) 39.5–––––– Adaptively Connected Neural Networks wanggrun/Adaptively-Connected-Neural-Networks 2019
189 FSAF (ResNet-101, anchor-based branches) 39.359.2––––– Feature Selective Anchor-Free Module for Single-Shot Object Detection open-mmlab/mmdetection · hdjang/Feature-Selective-Anchor-Free-Module-for-Single-Shot-Object-Detection · xuannianz/FSAF · +1 2019
190 Mask R-CNN (HRNetV2p-W18) 39.2–––41.751.0– Deep High-Resolution Representation Learning for Visual Recognition open-mmlab/mmdetection · PaddlePaddle/PaddleDetection · open-mmlab/mmsegmentation · +39 2019
191 Mask R-CNN (ResNet-50 + 1 NL) 39.061.141.9–––– Non-local Neural Networks facebookresearch/detectron · facebookresearch/SlowFast · open-mmlab/mmaction2 · +29 2017
192 FoveaBox (ResNet-101-FPN, 800x800) 38.958.441.522.343.551.7– FoveaBox: Beyond Anchor-based Object Detector open-mmlab/mmdetection · taokong/FoveaBox · anonymous2020new/iffDetector · +4 2019
193 FCOS (ResNet-50-FPN + improvements) 38.6 57.441.422.342.549.8– FCOS: Fully Convolutional One-Stage Object Detection open-mmlab/mmdetection · pytorch/vision · PaddlePaddle/PaddleDetection · +84 2019
193 RPDet (ResNet-50) 38.6–––––– RepPoints: Point Set Representation for Object Detection open-mmlab/mmdetection · microsoft/RepPoints · Scalsol/RepPointsV2 · +3 2019
195 Libra R-CNN (ResNet-50 FPN) 38.559.342.022.942.150.5– Libra R-CNN: Towards Balanced Learning for Object Detection open-mmlab/mmdetection · PaddlePaddle/PaddleDetection · OceanPang/Libra_R-CNN · +3 2019
196 Mask R-CNN (ResNet-50-FPN, GRoIE) 38.459.941.722.942.149.7– A novel Region of Interest Extraction Layer for Instance Segmentation open-mmlab/mmdetection · open-mmlab/mmdetection · IMPLabUniPr/mmdetection-groie · +2 2020
196 CornerNet511 (Hourglass-104) 38.453.840.918.640.551.8– CornerNet: Detecting Objects as Paired Keypoints open-mmlab/mmdetection · PaddlePaddle/PaddleDetection · princeton-vl/CornerNet · +2 2018
198 FoveaBox+Retina (ResNet-50) 38.157.840.5–––– FoveaBox: Beyond Anchor-based Object Detector open-mmlab/mmdetection · taokong/FoveaBox · anonymous2020new/iffDetector · +4 2019
199 Faster R-CNN (HRNetV2p-W18) 38.058.941.522.640.849.6– Deep High-Resolution Representation Learning for Visual Recognition open-mmlab/mmdetection · PaddlePaddle/PaddleDetection · open-mmlab/mmsegmentation · +39 2019
199 FoveaBox (ResNet-101-FPN, 600x600) 3857.840.219.542.252.7– FoveaBox: Beyond Anchor-based Object Detector open-mmlab/mmdetection · taokong/FoveaBox · anonymous2020new/iffDetector · +4 2019
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