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 | Waymo 2D detection all_ns f0val | ATSS (ConvNeXt-T) | https://arxiv.org/abs/2103.14027v3 | COCO-style AP | 38.3 |
16k > Object Detection | Waymo 2D detection all_ns f0val | ATSS (Swin-T) | https://arxiv.org/abs/2103.14027v3 | COCO-style AP | 37.2 |
16k > Object Detection | Waymo 2D detection all_ns f0val | ATSS+DyHead | https://arxiv.org/abs/2103.14027v3 | COCO-style AP | 37.1 |
16k > Object Detection | Waymo 2D detection all_ns f0val | Cascade R-CNN | https://arxiv.org/abs/2103.14027v3 | COCO-style AP | 36.4 |
16k > Object Detection | Waymo 2D detection all_ns f0val | GFL | https://arxiv.org/abs/2103.14027v3 | COCO-style AP | 35.7 |
16k > Object Detection | Waymo 2D detection all_ns f0val | ATSS | https://arxiv.org/abs/2103.14027v3 | COCO-style AP | 35.4 |
16k > Object Detection | Waymo 2D detection all_ns f0val | ATSS+SEPC | https://arxiv.org/abs/2103.14027v3 | COCO-style AP | 35.0 |
16k > Object Detection | Waymo 2D detection all_ns f0val | Faster R-CNN | https://arxiv.org/abs/2103.14027v3 | COCO-style AP | 34.5 |
16k > Object Detection | Waymo 2D detection all_ns f0val | Sparse R-CNN | https://arxiv.org/abs/2103.14027v3 | COCO-style AP | 32.8 |
16k > Object Detection | Waymo 2D detection all_ns f0val | Deformable DETR | https://arxiv.org/abs/2103.14027v3 | COCO-style AP | 32.7 |
16k > Object Detection | Waymo 2D detection all_ns f0val | RetinaNet | https://arxiv.org/abs/2103.14027v3 | COCO-style AP | 32.5 |
16k > Object Detection | Waymo 2D detection all_ns f0val | DETR | https://arxiv.org/abs/2103.14027v3 | COCO-style AP | 17.8 |
16k > Object Detection | Waymo Open Dataset | LeapMotor_Det | https://arxiv.org/abs/2106.08713v1 | AP/L2 | 70.41 |
16k > Object Detection | Waymo Open Dataset | LeapMotor_Det | https://arxiv.org/abs/2106.08713v1 | Latency, ms | 6.16 |
16k > Object Detection | Waymo Open Dataset | YOLOR_TensorRT (Ours) | https://arxiv.org/abs/2106.08713v1 | AP/L2 | 69.72 |
16k > Object Detection | Waymo Open Dataset | YOLOR_TensorRT (Ours) | https://arxiv.org/abs/2106.08713v1 | Latency, ms | 4.58 |
16k > Object Detection | Waymo Open Dataset | YOLOR_P6_TRT | https://arxiv.org/abs/2106.08713v1 | AP/L2 | 69.56 |
16k > Object Detection | Waymo Open Dataset | YOLOR_P6_TRT | https://arxiv.org/abs/2106.08713v1 | Latency, ms | 3.74 |
16k > Object Detection | Waymo Open Dataset | dereyly_self_ensemble | https://arxiv.org/abs/2106.08713v1 | AP/L2 | 65.65 |
16k > Object Detection | Waymo Open Dataset | dereyly_self_ensemble | https://arxiv.org/abs/2106.08713v1 | Latency, ms | 6.87 |
16k > Object Detection | Waymo Open Dataset | YOLO_v5 | https://arxiv.org/abs/2106.08713v1 | AP/L2 | 64.14 |
16k > Object Detection | Waymo Open Dataset | YOLO_v5 | https://arxiv.org/abs/2106.08713v1 | Latency, ms | 3.81 |
16k > Object Detection | MSCOCO | PP-PicoDet-L | https://arxiv.org/abs/2111.00902v1 | mAP @0.5:0.95 | 40.9 |
16k > Object Detection | MSCOCO | YOLOv5s | null | mAP @0.5:0.95 | 37.2 |
16k > Object Detection | MSCOCO | PP-PicoDet-M | null | mAP @0.5:0.95 | 36.6 |
16k > Object Detection | MSCOCO | YOLOX-tiny | null | mAP @0.5:0.95 | 32.8 |
16k > Object Detection | MSCOCO | YOLOX-Nano | null | mAP @0.5:0.95 | 25.8 |
16k > Object Detection | MSCOCO | NanoDet-M | null | mAP @0.5:0.95 | 25.3 |
16k > Object Detection | MSCOCO | ScaleDet | https://arxiv.org/abs/2306.04849v1 | AP | 58.8 |
16k > Object Detection | Objects365 | ScaleDet | https://arxiv.org/abs/2306.04849v1 | AP | 46.8 |
16k > Object Detection | VisDrone - 10% labeled data | SSOD + Crop (L + U) | https://arxiv.org/abs/2308.05032v1 | COCO-style AP | 27.46 |
16k > Object Detection | TexBiG 2022 test | VSR (Vison, Semantics and Relation Model) | https://link.springer.com/chapter/10.1007/978-3-031-16788-1_22 | mAP@0.5:0.95:0.05 | 75.9 |
16k > Object Detection | SHEL5K | YOLO | https://www.mdpi.com/1424-8220/22/6/2315 | Average mAP | 0.8828 |
16k > Object Detection | SeaDronesSee | Synth Pretrained Faster R-CNN ResNeXt-101-FPN | https://arxiv.org/abs/2112.12252v1 | mAP@0.5 | 59.20 |
16k > Object Detection | SeaDronesSee | Synth Pretrained Yolo5 | https://arxiv.org/abs/2112.12252v1 | mAP@0.5 | 59.08 |
16k > Object Detection | SeaDronesSee | Faster R-CNN ResNeXt-101-FPN | https://arxiv.org/abs/2105.01922v2 | mAP@0.5 | 54.66 |
16k > Object Detection | SeaDronesSee | CenterNet Hourglass104 | https://arxiv.org/abs/2105.01922v2 | mAP@0.5 | 50.32 |
16k > Object Detection | SeaDronesSee | Synth Pretrained EffDetD0 | https://arxiv.org/abs/2112.12252v1 | mAP@0.5 | 38.74 |
16k > Object Detection | SeaDronesSee | EfficientDet D0 | https://arxiv.org/abs/2105.01922v2 | mAP@0.5 | 37.11 |
16k > Object Detection | SeaDronesSee | CenterNet ResNet101 | https://arxiv.org/abs/2105.01922v2 | mAP@0.5 | 36.42 |
16k > Object Detection | SeaDronesSee | Faster RCNN ResNet50FPN | https://arxiv.org/abs/2105.01922v2 | mAP@0.5 | 30.09 |
16k > Object Detection | SeaDronesSee | CenterNet ResNet18 | https://arxiv.org/abs/2105.01922v2 | mAP@0.5 | 21.84 |
16k > Object Detection | SeaDronesSee | Yolo 5 | https://arxiv.org/abs/2112.12252v1 | mAP@0.50 | 54.74 |
16k > Object Detection | TBBR | Swin-T (ImageNet-1k pretrain) | https://doi.org/10.1016/j.autcon.2022.104690 | Average Recall@IoU:0.5-0.95 | 45.4 |
16k > Object Detection | TBBR | FSAF (ResNeXt-101, ImageNet-1k pretrain) | https://doi.org/10.1016/j.autcon.2022.104690 | Average Recall@IoU:0.5-0.95 | 38.0 |
16k > Object Detection | TBBR | Mask R-CNN (ResNet-50-FPN, ImageNet-1k pretrain) | https://doi.org/10.1016/j.autcon.2022.104690 | Average Recall@IoU:0.5-0.95 | 37.0 |
16k > Object Detection | TBBR | Mask R-CNN (ResNet-50-FPN) | https://doi.org/10.1016/j.autcon.2022.104690 | Average Recall@IoU:0.5-0.95 | 30.8 |
16k > Object Detection | TBBR | TridentNet (ResNet-50, ImageNet-1k pretrain) | https://doi.org/10.1016/j.autcon.2022.104690 | Average Recall@IoU:0.5-0.95 | 30.0 |
16k > Object Detection | TBBR | FSAF (ResNeXt-101) | https://doi.org/10.1016/j.autcon.2022.104690 | Average Recall@IoU:0.5-0.95 | 24.8 |
16k > Object Detection | TBBR | TridentNet (ResNet-50) | https://doi.org/10.1016/j.autcon.2022.104690 | Average Recall@IoU:0.5-0.95 | 21.5 |
16k > Object Detection | Manga109 | DASS-Detector (YOLOX XL) | https://arxiv.org/abs/2211.10641v2 | Average Precision | 87.93 |
16k > Object Detection | Manga109 | DASS-Detector (YOLOX Tiny) | https://arxiv.org/abs/2211.10641v2 | Average Precision | 87.46 |
16k > Object Detection | Extended TACO-1 | EfficientDet-D2 | https://arxiv.org/abs/2105.06808v1 | AP50 | 56.8 |
16k > Object Detection | iSAID | PANet++ | https://arxiv.org/abs/1905.12886v2 | Average Precision | 47.0 |
16k > Object Detection | iSAID | PANet+ | https://arxiv.org/abs/1905.12886v2 | Average Precision | 46.31 |
16k > Object Detection | iSAID | PANet | http://arxiv.org/abs/1803.01534v4 | Average Precision | 41.66 |
16k > Object Detection | iSAID | Mask-RCNN+ | http://arxiv.org/abs/1703.06870v3 | Average Precision | 37.18 |
16k > Object Detection | iSAID | Mask-RCNN | http://arxiv.org/abs/1703.06870v3 | Average Precision | 36.50 |
16k > Object Detection | Pascal VOC to Clipart1K | DDT | https://arxiv.org/abs/2506.04211v1 | mAP | 55.6 |
16k > Object Detection | Pascal VOC to Clipart1K | MILA | https://arxiv.org/abs/2309.01086v1 | mAP | 49.9 |
16k > Object Detection | Pascal VOC to Clipart1K | CDDMSL | https://arxiv.org/abs/2309.13525v1 | mAP | 40.4 |
16k > Object Detection | KITTI Cars Hard | Patches | https://arxiv.org/abs/1910.04093v1 | AP | 68.91 |
16k > Object Detection | KITTI Cars Hard | PointRCNN Shi et al. (2019) | https://arxiv.org/abs/1812.04244v2 | AP | 68.32 |
16k > Object Detection | KITTI Cars Hard | Vote3Deep | http://arxiv.org/abs/1609.06666v2 | AP | 63.23 |
16k > Object Detection | KITTI Cars Hard | F-PointNet | http://arxiv.org/abs/1711.08488v2 | AP | 62.19 |
16k > Object Detection | KITTI Cars Hard | VeloFCN | http://arxiv.org/abs/1608.07916v1 | AP | 42.74 |
16k > Object Detection | IndustReal | YoloV8 | https://arxiv.org/abs/2310.17323v1 | mAP | 64.1 |
16k > Object Detection | IndustReal | YoloV8 (synthetic data only) | https://arxiv.org/abs/2310.17323v1 | mAP | 57.5 |
16k > Object Detection | GRAZPEDWRI-DX | YOLOv8x | https://arxiv.org/abs/2407.12597v2 | mAP | 77.00 |
16k > Object Detection | GRAZPEDWRI-DX | YOLOv10-X | https://arxiv.org/abs/2407.15689v2 | mAP | 76.2 |
16k > Object Detection | GRAZPEDWRI-DX | YOLOv5x | https://arxiv.org/abs/2407.12597v2 | mAP | 69.00 |
16k > Object Detection | GRAZPEDWRI-DX | DeepLOC | https://arxiv.org/abs/2308.12727v1 | mAP | 65.4 |
16k > Object Detection | GRAZPEDWRI-DX | YOLOv6m | https://arxiv.org/abs/2407.12597v2 | mAP | 64.00 |
16k > Object Detection | GRAZPEDWRI-DX | YOLOv7 | https://arxiv.org/abs/2407.12597v2 | mAP | 61.00 |
16k > Object Detection | A2D | RL [10] Lpixel | https://arxiv.org/abs/2108.03798v2 | Mean IoU | 5.8 |
16k > Object Detection | ELEVATER | GLIP-T | https://arxiv.org/abs/2204.08790v6 | AP | 62.6 |
16k > Object Detection | WaterScenes | YOLOv8-M | https://arxiv.org/abs/2307.06505v3 | mAP@50-95 | 59.2 |
16k > Object Detection | WaterScenes | YOLOX-M | https://arxiv.org/abs/2107.08430v2 | mAP@50-95 | 57.8 |
16k > Object Detection | WaterScenes | Achelous-MV-GDF-S2 | https://arxiv.org/abs/2307.07102v1 | mAP@50-95 | 56.0 |
16k > Object Detection | WaterScenes | Faster R-CNN | https://arxiv.org/abs/2307.06505v3 | mAP@50-95 | 47.8 |
16k > Object Detection | SFCHD | YOLOv8+SCALE | https://arxiv.org/abs/2306.02098v2 | mAP@0.50 | 78.6 |
16k > Object Detection | SFCHD | YOLOv8+SCALE | https://arxiv.org/abs/2306.02098v2 | mAP@0.5:0.95 | 53.3 |
16k > Object Detection | SFCHD | TOOD+SCALE | https://arxiv.org/abs/2306.02098v2 | mAP@0.50 | 79.3 |
16k > Object Detection | SFCHD | TOOD+SCALE | https://arxiv.org/abs/2306.02098v2 | mAP@0.5:0.95 | 52.4 |
16k > Object Detection | SFCHD | TOOD | https://arxiv.org/abs/2306.02098v2 | mAP@0.50 | 78.9 |
16k > Object Detection | SFCHD | TOOD | https://arxiv.org/abs/2306.02098v2 | mAP@0.5:0.95 | 52.3 |
16k > Object Detection | SFCHD | YOLOv8 | https://arxiv.org/abs/2306.02098v2 | mAP@0.50 | 77.9 |
16k > Object Detection | SFCHD | YOLOv8 | https://arxiv.org/abs/2306.02098v2 | mAP@0.5:0.95 | 52.2 |
16k > Object Detection | SFCHD | VFNet+SCALE | https://arxiv.org/abs/2306.02098v2 | mAP@0.50 | 76.6 |
16k > Object Detection | SFCHD | VFNet+SCALE | https://arxiv.org/abs/2306.02098v2 | mAP@0.5:0.95 | 51.4 |
16k > Object Detection | SFCHD | VFNet | https://arxiv.org/abs/2306.02098v2 | mAP@0.50 | 76.4 |
16k > Object Detection | SFCHD | VFNet | https://arxiv.org/abs/2306.02098v2 | mAP@0.5:0.95 | 51.0 |
16k > Object Detection | SFCHD | Faster RCNN | https://arxiv.org/abs/2306.02098v2 | mAP@0.50 | 76.4 |
16k > Object Detection | SFCHD | Faster RCNN | https://arxiv.org/abs/2306.02098v2 | mAP@0.5:0.95 | 50.3 |
16k > Object Detection | SFCHD | FCOS | https://arxiv.org/abs/2306.02098v2 | mAP@0.50 | 76.4 |
16k > Object Detection | SFCHD | FCOS | https://arxiv.org/abs/2306.02098v2 | mAP@0.5:0.95 | 49.6 |
16k > Object Detection | SFCHD | YOLOv5 | https://arxiv.org/abs/2306.02098v2 | mAP@0.50 | 74.1 |
16k > Object Detection | SFCHD | YOLOv5 | https://arxiv.org/abs/2306.02098v2 | mAP@0.5:0.95 | 49.6 |
16k > Object Detection | SFCHD | FCOS+SCALE | https://arxiv.org/abs/2306.02098v2 | mAP@0.50 | 76.3 |
16k > Object Detection | SFCHD | FCOS+SCALE | https://arxiv.org/abs/2306.02098v2 | mAP@0.5:0.95 | 49.5 |
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