HyperAI

Instance Segmentation On Ade20K Val

Métriques

AP
APL
APM
APS

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
AP
APL
APM
APS
Paper TitleRepository
Mask2Former (Swin-L, single-scale)34.954.74016.3Masked-attention Mask Transformer for Universal Image Segmentation
Mask2Former (ResNet-50)-43.128.9-Masked-attention Mask Transformer for Universal Image Segmentation
DiNAT-L (Mask2Former, single-scale)35.455.539.016.3Dilated Neighborhood Attention Transformer
Mask2Former (ResNet50)26.4--10.4Masked-attention Mask Transformer for Universal Image Segmentation
X-Decoder (L)35.8---Generalized Decoding for Pixel, Image, and Language-
X-Decoder (Davit-d5, Deform, single-scale, 1280x1280)38.759.643.318.9Generalized 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
OneFormer (DiNAT-L, single-scale, 1280x1280, COCO-pretrain)40.259.744.419.2OneFormer: One Transformer to Rule Universal Image Segmentation
OneFormer (InternImage-H, emb_dim=1024, single-scale, 896x896, COCO-Pretrained)44.264.349.923.7OneFormer: One Transformer to Rule Universal Image Segmentation
OpenSeeD42.6---A Simple Framework for Open-Vocabulary Segmentation and Detection
Mask2Former (Swin-L + FAPN)33.454.637.614.6Masked-attention Mask Transformer for Universal Image Segmentation
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