Mask R-CNN

Benchmark Model Rank Results
instance-segmentation-on-bdd100k-valMask R-CNN#3AP: 20.5
instance-segmentation-on-cocoMask R-CNN (ResNeXt-101-FPN)#88mask AP: 37.1AP50: 60.0AP75: 39.4APS: 16.9APM: 39.9APL: 53.5
keypoint-detection-on-coco-test-challengeMask R-CNN*#5AR: 75.4ARM: 70.2AP: 68.9AP50: 89.2AP75: 75.2APL: 82.6
keypoint-detection-on-coco-test-devMask R-CNN#13APM: 57.8APL: 71.4AP50: 87.3AP75: 68.7
multi-human-parsing-on-mhp-v20Mask R-CNN#5AP 0.5: 14.9
multi-person-pose-estimation-on-crowdposeMask R-CNN#23mAP @0.5:0.95: 57.2AP Easy: 69.4AP Medium: 57.9AP Hard: 45.8
multi-person-pose-estimation-on-ochumanMask R-CNN#8AP50: 33.2AP75: 24.5Validation AP: 20.2
multi-tissue-nucleus-segmentation-on-kumarMask R-CNN (e)#10Dice: 0.760Hausdorff Distance (mm): 50.9
object-detection-on-cocoMask R-CNN (ResNeXt-101-FPN)#195box mAP: 39.8AP50: 62.3AP75: 43.4APS: 22.1APM: 43.2APL: 51.2
object-detection-on-cocoMask R-CNN (ResNet-101-FPN)#208box mAP: 38.2AP50: 60.3AP75: 41.7APS: 20.1APM: 41.1APL: 50.2
object-detection-on-coco-minivalMask R-CNN (ResNet-101-FPN)#183box AP: 40.0
object-detection-on-coco-minivalMask R-CNN (ResNet-50-FPN)#201box AP: 37.7
object-detection-on-coco-minivalMask R-CNN (ResNeXt-101-FPN)#203box AP: 36.7AP50: 59.5AP75: 38.9
object-detection-on-coco-oMask R-CNN (ResNet-50)#35Average mAP: 17.1
object-detection-on-coco-oMask R-CNN (ResNet-50)#45Effective Robustness: -0.11
object-localization-on-gritMask R-CNN#1Localization (ablation): 44.7Localization (test): 45.1
panoptic-segmentation-on-cityscapes-valMask R-CNN+COCO#28PQth: 54.0
pose-estimation-on-coco-test-devMask-RCNN#38AP: 63.1AP50: 87.3AP75: 68.7APL: 71.4