Panoptic Segmentation On Cityscapes Test

评测结果

各个模型在此基准测试上的表现结果

		Paper Title	Repository
OneFormer (ConvNeXt-L, single-scale, Mapillary Vistas-Pretrained)	68.0	OneFormer: One Transformer to Rule Universal Image Segmentation
Panoptic-DeepLab (SWideRNet [1, 1, 4.5], Mapillary, multi-scale)	67.8	Scaling Wide Residual Networks for Panoptic Segmentation	-
EfficientPS	67.1	EfficientPS: Efficient Panoptic Segmentation
Axial-DeepLab-XL (Mapillary Vistas, multi-scale)	66.6	Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation
kMaX-DeepLab (single-scale)	66.2	kMaX-DeepLab: k-means Mask Transformer
Panoptic-Deeplab	65.5	Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation
EfficientPS (Cityscapes-fine)	62.9	EfficientPS: Efficient Panoptic Segmentation
SOGNet (ResNet-50)	60	SOGNet: Scene Overlap Graph Network for Panoptic Segmentation
COPS (ResNet-50)	60	Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable Approach
Dynamically Instantiated Network	55.4	Pixelwise Instance Segmentation with a Dynamically Instantiated Network

0 of 10 row(s) selected.