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Instance Segmentation On Ade20K Val

Metrics

AP

APL

APM

APS

Results

Performance results of various models on this benchmark

					Paper Title	Repository
OneFormer (InternImage-H, emb_dim=1024, single-scale, 896x896, COCO-Pretrained)	44.2	64.3	49.9	23.7	OneFormer: One Transformer to Rule Universal Image Segmentation
OpenSeeD	42.6	-	-	-	A Simple Framework for Open-Vocabulary Segmentation and Detection
OneFormer (DiNAT-L, single-scale, 1280x1280, COCO-pretrain)	40.2	59.7	44.4	19.2	OneFormer: One Transformer to Rule Universal Image Segmentation
X-Decoder (Davit-d5, Deform, single-scale, 1280x1280)	38.7	59.6	43.3	18.9	Generalized Decoding for Pixel, Image, and Language
OneFormer (DiNAT-L, single-scale)	36.0	-	-	-	OneFormer: One Transformer to Rule Universal Image Segmentation
OneFormer (Swin-L, single-scale)	35.9	-	-	-	OneFormer: One Transformer to Rule Universal Image Segmentation
X-Decoder (L)	35.8	-	-	-	Generalized Decoding for Pixel, Image, and Language
DiNAT-L (Mask2Former, single-scale)	35.4	55.5	39.0	16.3	Dilated Neighborhood Attention Transformer
Mask2Former (Swin-L, single-scale)	34.9	54.7	40	16.3	Masked-attention Mask Transformer for Universal Image Segmentation
Mask2Former (Swin-L + FAPN)	33.4	54.6	37.6	14.6	Masked-attention Mask Transformer for Universal Image Segmentation
Mask2Former (ResNet50)	26.4	-	-	10.4	Masked-attention Mask Transformer for Universal Image Segmentation
Mask2Former (ResNet-50)	-	43.1	28.9	-	Masked-attention Mask Transformer for Universal Image Segmentation

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Instance Segmentation On Ade20K Val | SOTA | HyperAI