task_path stringlengths 3 199 ⌀ | dataset stringlengths 1 128 ⌀ | model_name stringlengths 1 223 ⌀ | paper_url stringlengths 21 601 ⌀ | metric_name stringlengths 1 50 ⌀ | metric_value stringlengths 1 9.22k ⌀ |
|---|---|---|---|---|---|
16k > Object Detection > Open World Object Detection | COCO-OOD | unsniffer | https://arxiv.org/abs/2303.13769v3 | unknown F1 score | 0.479 |
16k > Object Detection > Open World Object Detection | COCO VOC to non-VOC | GOOD | https://arxiv.org/abs/2212.11720v3 | AR100 | 39.7 |
16k > Object Detection > Open World Object Detection | COCO VOC to non-VOC | OLN-Box | https://arxiv.org/abs/2108.06753v1 | AR100 | 33.4 |
16k > Object Detection > Open World Object Detection | PASCAL VOC 2007 | ORE (MDef-DETR) | https://arxiv.org/abs/2111.11430v6 | WI | 0.0474 |
16k > Object Detection > Open World Object Detection | PASCAL VOC 2007 | ORE (MDef-DETR) | https://arxiv.org/abs/2111.11430v6 | A-OSE | 7322 |
16k > Object Detection > Open World Object Detection | PASCAL VOC 2007 | ORE (MDef-DETR) | https://arxiv.org/abs/2111.11430v6 | MAP | 64.03 |
16k > Object Detection > Open World Object Detection | PASCAL VOC 2007 | ORE (MDef-DETR) | https://arxiv.org/abs/2111.11430v6 | Unknown Recall | 50.13 |
16k > Object Detection > Open World Object Detection | PASCAL VOC 2007 | ORE | https://arxiv.org/abs/2103.02603v2 | WI | 0.02193 |
16k > Object Detection > Open World Object Detection | PASCAL VOC 2007 | ORE | https://arxiv.org/abs/2103.02603v2 | A-OSE | 8234 |
16k > Object Detection > Open World Object Detection | PASCAL VOC 2007 | ORE | https://arxiv.org/abs/2103.02603v2 | MAP | 56.34 |
16k > Object Detection > Open World Object Detection | PASCAL VOC 2007 | ORE | https://arxiv.org/abs/2103.02603v2 | Unknown Recall | 14.40 |
16k > Object Detection > Medical Object Detection | DeepLesion | P3D | https://arxiv.org/abs/2201.01426v1 | Sensitivity | 88.55 |
16k > Object Detection > Medical Object Detection | DeepLesion | DKMA-ULD | https://arxiv.org/abs/2203.06886v1 | Sensitivity | 87.16 |
16k > Object Detection > Medical Object Detection | DeepLesion | AlignShift | https://arxiv.org/abs/2103.12277v1 | Sensitivity | 86.83 |
16k > Object Detection > Medical Object Detection | DeepLesion | MP3D | https://arxiv.org/abs/2012.08770v1 | Sensitivity | 86.74 |
16k > Object Detection > Medical Object Detection | DeepLesion | MELD | https://arxiv.org/abs/2005.13753v1 | Sensitivity | 86.6 |
16k > Object Detection > Medical Object Detection | DeepLesion | FCOS | https://arxiv.org/abs/2203.16074v1 | Sensitivity | 86.05 |
16k > Object Detection > Medical Object Detection | DeepLesion | MULAN | https://arxiv.org/abs/1908.04373v1 | Sensitivity | 85.22 |
16k > Object Detection > Medical Object Detection | DeepLesion | MVP Net | https://arxiv.org/abs/1909.04247v3 | Sensitivity | 83.64 |
16k > Object Detection > Medical Object Detection | DeepLesion | Improved RetinaNet | https://arxiv.org/abs/1906.02283v1 | Sensitivity | 82.36 |
16k > Object Detection > Medical Object Detection | DeepLesion | 3DCE | http://arxiv.org/abs/1806.09648v2 | Sensitivity | 75.55 |
16k > Object Detection > Medical Object Detection | GRAZPEDWRI-DX | YOLOv8x | https://arxiv.org/abs/2407.12597v2 | mAP | 77.00 |
16k > Object Detection > Medical Object Detection | MoNuSeg 2018 | CircleNet | https://arxiv.org/abs/2110.12093v1 | Average-mAP | 0.487 |
16k > Object Detection > Medical Object Detection | Barrett’s Esophagus | Attention-based model | https://arxiv.org/abs/1811.08513v2 | Mean Accuracy | 81% |
16k > Object Detection > Medical Object Detection | Barrett’s Esophagus | Sliding Window | http://arxiv.org/abs/1703.02442v2 | Mean Accuracy | 74% |
16k > Object Detection > Dense Object Detection | SKU-110K | RetailDet | https://arxiv.org/abs/2204.00298v4 | AP | 59.0 |
16k > Object Detection > Dense Object Detection | SKU-110K | Cascade-RCNN | https://arxiv.org/abs/2007.11946v3 | AP | 58.7 |
16k > Object Detection > Dense Object Detection | SKU-110K | SAPD | https://arxiv.org/abs/1911.12448v2 | AP | 55.7 |
16k > Object Detection > Dense Object Detection | SKU-110K | Soft-IoU + EM-Merger unit | http://arxiv.org/abs/1904.00853v3 | AP | 49.2 |
16k > Object Detection > Dense Object Detection | SKU-110K | RetinaNet | http://arxiv.org/abs/1708.02002v2 | AP | 45.5 |
16k > Object Detection > Dense Object Detection | SKU-110K | RetinaNet | http://arxiv.org/abs/1708.02002v2 | AP75 | .389 |
16k > Object Detection > Object Proposal Generation | PASCAL VOC 2012, 60 proposals per image | MDef-DETR | https://arxiv.org/abs/2111.11430v6 | Average Recall | 0.9126 |
16k > Object Detection > Object Proposal Generation | PASCAL VOC 2012, 60 proposals per image | Recurrent Pixel Embedding | http://arxiv.org/abs/1712.08273v1 | Average Recall | 0.814 |
16k > Object Detection > Object Proposal Generation | PASCAL VOC 2012, 60 proposals per image | inst-DML | http://arxiv.org/abs/1703.10277v1 | Average Recall | 0.667 |
16k > Object Detection > Object Proposal Generation | COCO (Common Objects in Context) | MDef-DETR (Off-the-shelf evaluation) | https://arxiv.org/abs/2111.11430v6 | Average Recall | 0.6503 |
16k > Object Detection > Head Detection | Rebar Head | WSMA-Seg (stack=2 ,base=40, depth=5) | https://arxiv.org/abs/1904.13300v3 | F1 | 98.83% |
16k > Object Detection > License Plate Detection | Common Voice Estonian | porshe | https://arxiv.org/abs/2206.09379v2 | 0S | big car |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv8+SE | https://arxiv.org/abs/2410.01031v2 | F1-score | 0.66 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv8+SE | https://arxiv.org/abs/2410.01031v2 | AP50 | 67.07 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv8+GC | https://arxiv.org/abs/2407.03163v1 | F1-score | 0.66 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv8+GC | https://arxiv.org/abs/2407.03163v1 | AP50 | 66.32 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv8+GC | https://arxiv.org/abs/2410.01031v2 | F1-score | 0.66 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv8+GC | https://arxiv.org/abs/2410.01031v2 | AP50 | 66.32 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv8+GE | https://arxiv.org/abs/2410.01031v2 | F1-score | 0.64 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv8+GE | https://arxiv.org/abs/2410.01031v2 | AP50 | 65.99 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv8+ResCBAM | https://arxiv.org/abs/2409.18826v1 | F1-score | 0.64 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv8+ResCBAM | https://arxiv.org/abs/2409.18826v1 | AP50 | 65.8 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv8+ResCBAM | https://arxiv.org/abs/2402.09329v5 | F1-score | 0.64 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv8+ResCBAM | https://arxiv.org/abs/2402.09329v5 | AP50 | 65.8 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv8+GCT | https://arxiv.org/abs/2410.01031v2 | F1-score | 0.64 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv8+GCT | https://arxiv.org/abs/2410.01031v2 | AP50 | 65.67 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv9-E | https://arxiv.org/abs/2403.11249v2 | F1-score | 0.64 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv9-E | https://arxiv.org/abs/2403.11249v2 | AP50 | 65.46 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv9-C | https://arxiv.org/abs/2403.11249v2 | F1-score | 0.64 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv9-C | https://arxiv.org/abs/2403.11249v2 | AP50 | 65.31 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv8+ResGAM | https://arxiv.org/abs/2402.09329v5 | F1-score | 0.64 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv8+ResGAM | https://arxiv.org/abs/2402.09329v5 | AP50 | 65.0 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv8+SA | https://arxiv.org/abs/2402.09329v5 | F1-score | 0.63 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv8+SA | https://arxiv.org/abs/2402.09329v5 | AP50 | 64.3 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv8+ECA | https://arxiv.org/abs/2402.09329v5 | F1-score | 0.65 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv8+ECA | https://arxiv.org/abs/2402.09329v5 | AP50 | 64.2 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv8+GAM | https://arxiv.org/abs/2402.09329v5 | F1-score | 0.65 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv8+GAM | https://arxiv.org/abs/2402.09329v5 | AP50 | 64.2 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv8 | https://arxiv.org/abs/2304.05071v5 | F1-score | 0.62 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv8 | https://arxiv.org/abs/2304.05071v5 | AP50 | 63.6 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv10-M | https://arxiv.org/abs/2407.15689v2 | Fracture Sensitivity | 92.5 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv8m | https://arxiv.org/abs/2407.12597v2 | Fracture Sensitivity | 92.00 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv7 | https://arxiv.org/abs/2407.12597v2 | Fracture Sensitivity | 91.00 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv5s | https://arxiv.org/abs/2206.09379v2 | Fracture Sensitivity | 91.00 |
16k > Object Detection > Fracture detection | GRAZPEDWRI-DX | YOLOv6s | https://arxiv.org/abs/2206.09379v2 | Fracture Sensitivity | 89.00 |
16k > Object Detection > Moving Object Detection | DVSMOTION20 | GSCEventMOD | https://arxiv.org/abs/2109.14979v3 | F-Measure | 66.93 |
16k > Object Detection > 3D Object Detection From Monocular Images | nuScenes Cars | MonoDIS | https://arxiv.org/abs/1905.12365v1 | AP 2.0m | 69.0 |
16k > Object Detection > 3D Object Detection From Monocular Images | nuScenes Cars | MonoDIS | https://arxiv.org/abs/1905.12365v1 | AP 0.5m | 10.7 |
16k > Object Detection > 3D Object Detection From Monocular Images | nuScenes Cars | MonoDIS | https://arxiv.org/abs/1905.12365v1 | AP 1.0m | 37.5 |
16k > Object Detection > 3D Object Detection From Monocular Images | nuScenes Cars | MonoDIS | https://arxiv.org/abs/1905.12365v1 | AP 4.0m | 85.7 |
16k > Object Detection > 3D Object Detection From Monocular Images | nuScenes Cars | MonoDIS | https://arxiv.org/abs/1905.12365v1 | ATE | 0.61 |
16k > Object Detection > 3D Object Detection From Monocular Images | nuScenes Cars | MonoDIS | https://arxiv.org/abs/1905.12365v1 | ASE | 0.15 |
16k > Object Detection > 3D Object Detection From Monocular Images | nuScenes Cars | MonoDIS | https://arxiv.org/abs/1905.12365v1 | AOE | 0.08 |
16k > Object Detection > 3D Object Detection From Monocular Images | Waymo Open Dataset | DEVIANT | https://arxiv.org/abs/2207.10758v1 | 3D mAPH Vehicle (Front Camera Only) | 2.52 |
16k > Object Detection > 3D Object Detection From Monocular Images | Waymo Open Dataset | GUP Net | https://arxiv.org/abs/2107.13774v2 | 3D mAPH Vehicle (Front Camera Only) | 2.14 |
16k > Object Detection > 3D Object Detection From Monocular Images | Waymo Open Dataset | M3D-RPN | https://arxiv.org/abs/1907.06038v2 | 3D mAPH Vehicle (Front Camera Only) | 0.65 |
16k > Object Detection > 3D Object Detection From Monocular Images | KITTI-360 | SeaBird + PanopticBEV | https://arxiv.org/abs/2403.20318v1 | AP50 | 4.64 |
16k > Object Detection > 3D Object Detection From Monocular Images | KITTI-360 | SeaBird + PanopticBEV | https://arxiv.org/abs/2403.20318v1 | AP25 | 37.12 |
16k > Object Detection > 3D Object Detection From Monocular Images | KITTI-360 | BoxNet | https://arxiv.org/abs/1904.09664v2 | AP50 | 4.08 |
16k > Object Detection > 3D Object Detection From Monocular Images | KITTI-360 | BoxNet | https://arxiv.org/abs/1904.09664v2 | AP25 | 23.59 |
16k > Object Detection > 3D Object Detection From Monocular Images | KITTI-360 | VoteNet | https://arxiv.org/abs/1904.09664v2 | AP50 | 3.40 |
16k > Object Detection > 3D Object Detection From Monocular Images | KITTI-360 | VoteNet | https://arxiv.org/abs/1904.09664v2 | AP25 | 30.61 |
16k > Object Detection > 3D Object Detection From Monocular Images | KITTI-360 | SeaBird + Image2Maps | https://arxiv.org/abs/2403.20318v1 | AP50 | 3.14 |
16k > Object Detection > 3D Object Detection From Monocular Images | KITTI-360 | SeaBird + Image2Maps | https://arxiv.org/abs/2403.20318v1 | AP25 | 35.04 |
16k > Object Detection > 3D Object Detection From Monocular Images | KITTI-360 | MonoDTR | https://arxiv.org/abs/2203.10981v2 | AP50 | 3.02 |
16k > Object Detection > 3D Object Detection From Monocular Images | KITTI-360 | MonoDTR | https://arxiv.org/abs/2203.10981v2 | AP25 | 39.76 |
16k > Object Detection > 3D Object Detection From Monocular Images | KITTI-360 | DEVIANT | https://arxiv.org/abs/2207.10758v1 | AP50 | 0.88 |
16k > Object Detection > 3D Object Detection From Monocular Images | KITTI-360 | DEVIANT | https://arxiv.org/abs/2207.10758v1 | AP25 | 26.96 |
16k > Object Detection > 3D Object Detection From Monocular Images | KITTI-360 | GUPNet | https://arxiv.org/abs/2107.13774v2 | AP50 | 0.87 |
16k > Object Detection > 3D Object Detection From Monocular Images | KITTI-360 | GUPNet | https://arxiv.org/abs/2107.13774v2 | AP25 | 27.25 |
16k > Object Detection > 3D Object Detection From Monocular Images | KITTI-360 | MonoDLE | https://arxiv.org/abs/2103.16237v1 | AP50 | 0.85 |
16k > Object Detection > 3D Object Detection From Monocular Images | KITTI-360 | MonoDLE | https://arxiv.org/abs/2103.16237v1 | AP25 | 28.99 |
16k > Object Detection > 3D Object Detection From Monocular Images | KITTI-360 | Cube R-CNN | https://arxiv.org/abs/2207.10660v2 | AP50 | 0.80 |
16k > Object Detection > 3D Object Detection From Monocular Images | KITTI-360 | Cube R-CNN | https://arxiv.org/abs/2207.10660v2 | AP25 | 15.57 |
16k > Object Detection > 3D Object Detection From Monocular Images | KITTI-360 | MonoDETR | https://arxiv.org/abs/2203.13310v5 | AP50 | 0.79 |
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