Panoptic Segmentation On Coco Test Dev

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PQst

PQth

Results

Performance results of various models on this benchmark

				Paper Title	Repository
Mask DINO (single scale)	59.5	-	-	Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation
kMaX-DeepLab (single-scale)	58.5	49.0	64.8	kMaX-DeepLab: k-means Mask Transformer
Mask2Former (Swin-L)	58.3	48.1	65.1	Masked-attention Mask Transformer for Universal Image Segmentation
Panoptic SegFormer (Swin-L)	56.2	47.0	62.3	Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers
Panoptic SegFormer (PVTv2-B5)	55.8	46.5	61.9	Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers
CMT-DeepLab (single-scale)	55.7	46.8	61.6	CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation
K-Net (Swin-L)	55.2	46.2	61.2	K-Net: Towards Unified Image Segmentation
MaskConver (ResNet50, single-scale)	53.6	58.9	45.6	MaskConver: Revisiting Pure Convolution Model for Panoptic Segmentation
MaskFormer (Swin-L)	53.3	44.5	59.1	Per-Pixel Classification is Not All You Need for Semantic Segmentation
Panoptic FCN* (Swin-L)	52.7	-	59.4	Fully Convolutional Networks for Panoptic Segmentation
REFINE (ResNeXt-101-DCN)	51.5	39.2	59.6	REFINE: Prediction Fusion Network for Panoptic Segmentation	-
MaX-DeepLab-L (single-scale)	51.3	42.4	57.2	MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers
Panoptic SegFormer (ResNet-101)	50.9	43.0	56.2	Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers
Panoptic SegFormer (ResNet-50)	50.2	42.4	55.3	Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers
DetectoRS (ResNeXt-101-64x4d, multi-scale)	50	37.2	58.5	DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution
REFINE (ResNet-101-DCN)	49.6	37.7	57.5	REFINE: Prediction Fusion Network for Panoptic Segmentation	-
Ada-Segment (ResNet-101-DCN)	48.5	37.6	55.7	Ada-Segment: Automated Multi-loss Adaptation for Panoptic Segmentation	-
SpatialFlow(ResNet-101-FPN)	48.5	37.9	55.5	SpatialFlow: Bridging All Tasks for Panoptic Segmentation
K-Net (R101-FPN-DCN)	48.3	39.7	54	K-Net: Towards Unified Image Segmentation
SOGNet (ResNet-101-FPN)	47.8	-	-	SOGNet: Scene Overlap Graph Network for Panoptic Segmentation

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Panoptic Segmentation On Coco Test Dev

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