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SOTA
Panoptische Segmentierung
Panoptic Segmentation On Coco Test Dev
Panoptic Segmentation On Coco Test Dev
Metriken
PQ
PQst
PQth
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
PQ
PQst
PQth
Paper Title
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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