Fully Convolutional Networks for Semantic Segmentation

Benchmark Model Rank Results
crack-segmentation-on-crackvision12kFCN#3mIoU: 0.59842
multi-tissue-nucleus-segmentation-on-kumarFCN8 (e)#8Dice: 0.797Hausdorff Distance (mm): 31.2
multispectral-object-detection-on-kaistFusionRPN+BF#11All Miss Rate: 51.70
semantic-segmentation-on-ade20kFCN#220Validation mIoU: 29.39
semantic-segmentation-on-cityscapesFCN#87Mean IoU (class): 65.3%
semantic-segmentation-on-coco-stuff-testFCN (VGG-16)#17mIoU: 22.7%
semantic-segmentation-on-event-basedFCN#6mIoU: 59.6
semantic-segmentation-on-fine-grained-grassFCN#9mIoU: 47.47
semantic-segmentation-on-nyu-depth-v2FCN-32s RGB-HHA#86Mean Accuracy: 44
semantic-segmentation-on-pascal-contextFCN-8s#52mIoU: 37.8
semantic-segmentation-on-pascal-voc-2012FCN (VGG-16)#45Mean IoU: 62.2%
semantic-segmentation-on-selmaFCN#6mIoU: 68.2
semantic-segmentation-on-skyscapes-dense-1FCN8s (ResNet-50)#2Mean IoU: 33.06
semantic-segmentation-on-trans10kFCN#11mIoU: 62.75%GFLOPs: 42.23
video-semantic-segmentation-on-cityscapes-valFCN-50 [14]#6mIoU: 70.1