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 | SFCHD | RetinaNet | https://arxiv.org/abs/2306.02098v2 | mAP@0.50 | 75.9 |
16k > Object Detection | SFCHD | RetinaNet | https://arxiv.org/abs/2306.02098v2 | mAP@0.5:0.95 | 48.9 |
16k > Object Detection | SFCHD | SSD | https://arxiv.org/abs/2306.02098v2 | mAP@0.50 | 72.8 |
16k > Object Detection | SFCHD | SSD | https://arxiv.org/abs/2306.02098v2 | mAP@0.5:0.95 | 41.5 |
16k > Object Detection | PASCAL VOC 2012 test | SynCo (ResNet-50) 200ep | https://arxiv.org/abs/2410.02401v5 | Bounding Box AP | 57.2 |
16k > Object Detection | Cityscapes to Foggy Cityscapes | CDDMSL | https://arxiv.org/abs/2309.13525v1 | mAP | 54.3 |
16k > Object Detection | Clipart1k | CDDMSL | https://arxiv.org/abs/2309.13525v1 | MAP | 39.8 |
16k > Object Detection | GMOT-40 | iGDINO MAC-SORT | https://arxiv.org/abs/2409.02490v1 | mAP@0.5 | 72.7 |
16k > Object Detection | KITTI Cyclists Easy | Vote3Deep | http://arxiv.org/abs/1609.06666v2 | AP | 79.92 |
16k > Object Detection | KITTI Pedestrians Easy | Vote3Deep | http://arxiv.org/abs/1609.06666v2 | AP | 68.39 |
16k > Object Detection | NAO | Mask RCNN R50 | https://arxiv.org/abs/2111.04204v1 | mAP | 15.2 |
16k > Object Detection | NAO | Mask RCNN R50 | https://arxiv.org/abs/2111.04204v1 | mAP w/o OOD | 24.6 |
16k > Object Detection | NAO | Mask RCNN R50 | https://arxiv.org/abs/2111.04204v1 | mAR | 43.8 |
16k > Object Detection | NAO | EfficientDet-D4 | https://arxiv.org/abs/2111.04204v1 | mAP | 15.0 |
16k > Object Detection | NAO | EfficientDet-D4 | https://arxiv.org/abs/2111.04204v1 | mAP w/o OOD | 29.6 |
16k > Object Detection | NAO | EfficientDet-D4 | https://arxiv.org/abs/2111.04204v1 | mAR | 42.7 |
16k > Object Detection | NAO | EfficientDet-D7 | https://arxiv.org/abs/2111.04204v1 | mAP | 13.6 |
16k > Object Detection | NAO | EfficientDet-D7 | https://arxiv.org/abs/2111.04204v1 | mAP w/o OOD | 26.6 |
16k > Object Detection | NAO | EfficientDet-D7 | https://arxiv.org/abs/2111.04204v1 | mAR | 40.8 |
16k > Object Detection | NAO | Faster RCNN | https://arxiv.org/abs/2111.04204v1 | mAP | 13.5 |
16k > Object Detection | NAO | Faster RCNN | https://arxiv.org/abs/2111.04204v1 | mAP w/o OOD | 22.8 |
16k > Object Detection | NAO | Faster RCNN | https://arxiv.org/abs/2111.04204v1 | mAR | 41.4 |
16k > Object Detection | NAO | EfficientDet-D2 | https://arxiv.org/abs/2111.04204v1 | mAP | 12.8 |
16k > Object Detection | NAO | EfficientDet-D2 | https://arxiv.org/abs/2111.04204v1 | mAP w/o OOD | 25.4 |
16k > Object Detection | NAO | EfficientDet-D2 | https://arxiv.org/abs/2111.04204v1 | mAR | 40.2 |
16k > Object Detection | NAO | RetinaNet-R50 | https://arxiv.org/abs/2111.04204v1 | mAP | 11.1 |
16k > Object Detection | NAO | RetinaNet-R50 | https://arxiv.org/abs/2111.04204v1 | mAP w/o OOD | 19.5 |
16k > Object Detection | NAO | RetinaNet-R50 | https://arxiv.org/abs/2111.04204v1 | mAR | 37.2 |
16k > Object Detection | NAO | YOLOv3 | https://arxiv.org/abs/2111.04204v1 | mAP | 10.0 |
16k > Object Detection | NAO | YOLOv3 | https://arxiv.org/abs/2111.04204v1 | mAP w/o OOD | 17.5 |
16k > Object Detection | NAO | YOLOv3 | https://arxiv.org/abs/2111.04204v1 | mAR | 28.4 |
16k > Object Detection | COCO 2017 | MaxViT-B | https://arxiv.org/abs/2204.01697v4 | AP | 53.4 |
16k > Object Detection | COCO 2017 | MaxViT-B | https://arxiv.org/abs/2204.01697v4 | AP50 | 72.9 |
16k > Object Detection | COCO 2017 | MaxViT-B | https://arxiv.org/abs/2204.01697v4 | AP75 | 58.1 |
16k > Object Detection | COCO 2017 | MaxViT-B | https://arxiv.org/abs/2204.01697v4 | APM | 45.7 |
16k > Object Detection | COCO 2017 | MaxViT-B | https://arxiv.org/abs/2204.01697v4 | APM50 | 70.3 |
16k > Object Detection | COCO 2017 | MaxViT-B | https://arxiv.org/abs/2204.01697v4 | APM75 | 50 |
16k > Object Detection | COCO 2017 | MaxViT-S | https://arxiv.org/abs/2204.01697v4 | AP | 53.1 |
16k > Object Detection | COCO 2017 | MaxViT-S | https://arxiv.org/abs/2204.01697v4 | AP50 | 72.5 |
16k > Object Detection | COCO 2017 | MaxViT-S | https://arxiv.org/abs/2204.01697v4 | AP75 | 58.1 |
16k > Object Detection | COCO 2017 | MaxViT-S | https://arxiv.org/abs/2204.01697v4 | APM | 45.4 |
16k > Object Detection | COCO 2017 | MaxViT-S | https://arxiv.org/abs/2204.01697v4 | APM50 | 69.8 |
16k > Object Detection | COCO 2017 | MaxViT-S | https://arxiv.org/abs/2204.01697v4 | APM75 | 49.5 |
16k > Object Detection | COCO 2017 | MaxViT-T | https://arxiv.org/abs/2204.01697v4 | AP | 52.1 |
16k > Object Detection | COCO 2017 | MaxViT-T | https://arxiv.org/abs/2204.01697v4 | AP50 | 71.9 |
16k > Object Detection | COCO 2017 | MaxViT-T | https://arxiv.org/abs/2204.01697v4 | AP75 | 56.8 |
16k > Object Detection | COCO 2017 | MaxViT-T | https://arxiv.org/abs/2204.01697v4 | APM | 44.6 |
16k > Object Detection | COCO 2017 | MaxViT-T | https://arxiv.org/abs/2204.01697v4 | APM50 | 69.1 |
16k > Object Detection | COCO 2017 | MaxViT-T | https://arxiv.org/abs/2204.01697v4 | APM75 | 48.4 |
16k > Object Detection | COCO 2017 | DAT-S++ | https://arxiv.org/abs/2309.01430v1 | AP | 50.2 |
16k > Object Detection | COCO 2017 | DAT-T++ | https://arxiv.org/abs/2309.01430v1 | AP | 49.2 |
16k > Object Detection | COCO 2017 | DyHead (SAP) | https://arxiv.org/abs/2409.16630v1 | AP | 42.1 |
16k > Object Detection | COCO 2017 | DyHead (SAP) | https://arxiv.org/abs/2409.16630v1 | AP50 | 59.4 |
16k > Object Detection | COCO 2017 | DyHead (SAP) | https://arxiv.org/abs/2409.16630v1 | AP75 | 45.9 |
16k > Object Detection | COCO 2017 | Faster R-CNN (ideal number of groups) | https://arxiv.org/abs/2302.03193v1 | AP | 40.7 |
16k > Object Detection | COCO 2017 | Faster R-CNN (ideal number of groups) | https://arxiv.org/abs/2302.03193v1 | AP50 | 61.2 |
16k > Object Detection | COCO 2017 | Faster R-CNN (ideal number of groups) | https://arxiv.org/abs/2302.03193v1 | AP75 | 44.6 |
16k > Object Detection | COCO 2017 | UniRepLKNet-XL++ | https://arxiv.org/abs/2311.15599v2 | mAP | 56.4 |
16k > Object Detection | COCO 2017 | UniRepLKNet-L++ | https://arxiv.org/abs/2311.15599v2 | mAP | 55.8 |
16k > Object Detection | COCO 2017 | UniRepLKNet-B++ | https://arxiv.org/abs/2311.15599v2 | mAP | 54.8 |
16k > Object Detection | COCO 2017 | UniRepLKNet-S++ | https://arxiv.org/abs/2311.15599v2 | mAP | 54.3 |
16k > Object Detection | COCO 2017 | MixMIM-L | https://arxiv.org/abs/2205.13137v4 | mAP | 54.1 |
16k > Object Detection | COCO 2017 | UniRepLKNet-S | https://arxiv.org/abs/2311.15599v2 | mAP | 53 |
16k > Object Detection | COCO 2017 | MixMIM-B | https://arxiv.org/abs/2205.13137v4 | mAP | 52.2 |
16k > Object Detection | COCO 2017 | UniRepLKNet-T | https://arxiv.org/abs/2311.15599v2 | mAP | 51.7 |
16k > Object Detection | COCO 2017 | BiFormer-B (IN1k pretrain, MaskRCNN 12ep) | https://arxiv.org/abs/2303.08810v1 | mAP | 48.6 |
16k > Object Detection | COCO 2017 | DeBiFormer-B (IN1k pretrain, MaskRCNN 12ep) | https://arxiv.org/abs/2410.08582v1 | mAP | 48.5 |
16k > Object Detection | COCO 2017 | BiFormer-S (IN1k pretrain, MaskRCNN 12ep) | https://arxiv.org/abs/2303.08810v1 | mAP | 47.8 |
16k > Object Detection | COCO 2017 | DeBiFormer-S (IN1k pretrain, MaskRCNN 12ep) | https://arxiv.org/abs/2410.08582v1 | mAP | 47.5 |
16k > Object Detection | COCO 2017 | DeBiFormer-B (IN1k pretrain, Retina) | https://arxiv.org/abs/2410.08582v1 | mAP | 47.1 |
16k > Object Detection | COCO 2017 | DeBiFormer-S (IN1k pretrain, Retina) | https://arxiv.org/abs/2410.08582v1 | mAP | 45.6 |
16k > Object Detection | COCO 2017 | YOLO-Drone | https://arxiv.org/abs/2304.06925v2 | mAP | 35.45 |
16k > Object Detection | COCO 2017 | retinanet | https://arxiv.org/abs/1912.09476v2 | Mean mAP | 3153 |
16k > Object Detection | COCO 2017 | Lpixel | https://arxiv.org/abs/2108.03798v2 | Mean mAP | 4.2 |
16k > Object Detection | COCO val2017 | SynCo (ResNet-50) 200ep | https://arxiv.org/abs/2410.02401v5 | Bounding Box AP | 40.4 |
16k > Object Detection | 01/01/19679682867 | Six Ways to Call Delta Airlines customer service via Phone, Email, or Chat Option | https://arxiv.org/abs/2206.09379v2 | 0S | helping |
16k > Object Detection | KITTI Cars Easy | Patches | https://arxiv.org/abs/1910.04093v1 | AP | 87.87 |
16k > Object Detection | KITTI Cars Easy | PointRCNN Shi et al. (2019) | https://arxiv.org/abs/1812.04244v2 | AP | 85.94 |
16k > Object Detection | KITTI Cars Easy | Roarnet | http://arxiv.org/abs/1811.03818v1 | AP | 83.71 |
16k > Object Detection | KITTI Cars Easy | Vote3Deep | http://arxiv.org/abs/1609.06666v2 | AP | 76.79 |
16k > Object Detection | KITTI Cars Easy | VeloFCN | http://arxiv.org/abs/1608.07916v1 | AP | 60.34 |
16k > Object Detection | 100 sleep nights of 8 caregivers | Six Ways to Call Delta Airlines customer service via Phone, Email, or Chat Option | https://arxiv.org/abs/2402.04499v2 | 10°10 cm | Delta Airlines™ main customer service number is 1-8O0-Delta Airlines™ or +1-(832) - (553) - (18O0) [US-Delta Airlines™] or +1-(832) - (553) - (18O0) [UK-Delta Airlines™] OTA (Live Person), available 24/7. This guide explains how to contact Delta Airlines™ customer service effectively through phone, chat, and email opti... |
16k > Object Detection | PASCAL VOC 2007 | Cascade Eff-B7 NAS-FPN (Copy Paste pre-training, single-scale) | https://arxiv.org/abs/2012.07177v2 | MAP | 89.3% |
16k > Object Detection | PASCAL VOC 2007 | YOLO-Former | https://arxiv.org/abs/2401.06244v1 | MAP | 86.01% |
16k > Object Detection | PASCAL VOC 2007 | DETReg (MDef-DETR) | https://arxiv.org/abs/2111.11430v6 | MAP | 84.16% |
16k > Object Detection | PASCAL VOC 2007 | DETReg (MDef-DETR) | https://arxiv.org/abs/2111.11430v6 | AP50 | 84.16 |
16k > Object Detection | PASCAL VOC 2007 | HSD (VGG16, 512x512, single-scale test) | http://openaccess.thecvf.com/content_ICCV_2019/html/Cao_Hierarchical_Shot_Detector_ICCV_2019_paper.html | MAP | 83.0% |
16k > Object Detection | PASCAL VOC 2007 | CoupleNet | http://arxiv.org/abs/1708.02863v1 | MAP | 82.7% |
16k > Object Detection | PASCAL VOC 2007 | EEEA-Net-C2 (YOLOv4) | https://arxiv.org/abs/2108.06156v1 | MAP | 81.8% |
16k > Object Detection | PASCAL VOC 2007 | HSD (VGG16, 320x320, single-scale test) | http://openaccess.thecvf.com/content_ICCV_2019/html/Cao_Hierarchical_Shot_Detector_ICCV_2019_paper.html | MAP | 81.7% |
16k > Object Detection | PASCAL VOC 2007 | SSD512 (07+12+COCO) | http://arxiv.org/abs/1512.02325v5 | MAP | 81.6% |
16k > Object Detection | PASCAL VOC 2007 | BlitzNet512 + seg (s8) | http://arxiv.org/abs/1708.02813v1 | MAP | 81.5% |
16k > Object Detection | PASCAL VOC 2007 | Localize | https://arxiv.org/abs/2009.14085v1 | MAP | 81.5% |
16k > Object Detection | PASCAL VOC 2007 | CenterNet(DLA34, Flip, 512x512) | http://arxiv.org/abs/1904.07850v2 | MAP | 80.7% |
16k > Object Detection | PASCAL VOC 2007 | PS-KD (ResNet-152, CutMix) | https://arxiv.org/abs/2006.12000v3 | MAP | 79.7% |
16k > Object Detection | PASCAL VOC 2007 | DPNet | https://arxiv.org/abs/2209.13933v1 | MAP | 79.2% |
16k > Object Detection | PASCAL VOC 2007 | OHEM | http://arxiv.org/abs/1604.03540v1 | MAP | 78.9% |
16k > Object Detection | PASCAL VOC 2007 | YOLO v2 | http://arxiv.org/abs/1612.08242v1 | MAP | 78.6% |
16k > Object Detection | PASCAL VOC 2007 | ThunderNet SNet535 Backbone | https://arxiv.org/abs/1903.11752v3 | MAP | 78.6% |
16k > Object Detection | PASCAL VOC 2007 | DeNet-101 (skip) | http://arxiv.org/abs/1703.10295v3 | MAP | 77.1% |
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