Panoptic Segmentation on ADE20K val

Metric: PQ (higher is better)

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#	Model↕	PQ▼	Extra Data	Paper	Date↕	Code
1	OneFormer (InternImage-H, emb_dim=256, single-scale, 896x896)	54.5	No	OneFormer: One Transformer to Rule Universal Ima...	2022-11-10	Code
2	ViT-P (OneFormer, DiNAT-L, single-scale, 1280x1280, COCO_pretrain)	54	Yes	The Missing Point in Vision Transformers for Uni...	2025-05-26	Code
3	OpenSeed(SwinL, single scale, 1280x1280)	53.7	Yes	A Simple Framework for Open-Vocabulary Segmentat...	2023-03-14	Code
4	OneFormer (DiNAT-L, single-scale, 1280x1280, COCO-Pretrain)	53.4	Yes	OneFormer: One Transformer to Rule Universal Ima...	2022-11-10	Code
5	EoMT (DINOv2-g, single-scale, 1280x1280, COCO pre-trained)	52.8	Yes	Your ViT is Secretly an Image Segmentation Model	2025-03-24	Code
6	X-Decoder (Davit-d5, Deform, single-scale, 1280x1280)	52.4	Yes	Generalized Decoding for Pixel, Image, and Langu...	2022-12-21	Code
7	ViT-P (OneFormer, DiNAT-L, single-scale, 1280x1280)	51.9	No	The Missing Point in Vision Transformers for Uni...	2025-05-26	Code
8	OneFormer (DiNAT-L, single-scale, 1280x1280)	51.5	No	OneFormer: One Transformer to Rule Universal Ima...	2022-11-10	Code
9	OneFormer (Swin-L, single-scale, 1280x1280)	51.4	No	OneFormer: One Transformer to Rule Universal Ima...	2022-11-10	Code
10	kMaX-DeepLab (ConvNeXt-L, single-scale, 1281x1281)	50.9	No	kMaX-DeepLab: k-means Mask Transformer	2022-07-08	Code
11	OneFormer (DiNAT-L, single-scale, 640x640)	50.5	No	OneFormer: One Transformer to Rule Universal Ima...	2022-11-10	Code
12	OneFormer (ConvNeXt-XL, single-scale, 640x640)	50.1	No	OneFormer: One Transformer to Rule Universal Ima...	2022-11-10	Code
13	OneFormer (ConvNeXt-L, single-scale, 640x640)	50	No	OneFormer: One Transformer to Rule Universal Ima...	2022-11-10	Code
14	OneFormer (Swin-L, single-scale, 640x640)	49.8	No	OneFormer: One Transformer to Rule Universal Ima...	2022-11-10	Code
15	X-Decoder (L)	49.6	Yes	Generalized Decoding for Pixel, Image, and Langu...	2022-12-21	Code
16	DiNAT-L (Mask2Former, 640x640)	49.4	No	Dilated Neighborhood Attention Transformer	2022-09-29	Code
17	kMaX-DeepLab (ConvNeXt-L, single-scale, 641x641)	48.7	No	kMaX-DeepLab: k-means Mask Transformer	2022-07-08	Code
18	Mask2Former (Swin-L)	48.1	No	Masked-attention Mask Transformer for Universal ...	2021-12-02	Code
19	Mask2Former (Swin-L + FAPN, 640x640)	46.2	No	Masked-attention Mask Transformer for Universal ...	2021-12-02	Code
20	kMaX-DeepLab (ResNet50, single-scale, 1281x1281)	42.3	No	kMaX-DeepLab: k-means Mask Transformer	2022-07-08	Code
21	kMaX-DeepLab (ResNet50, single-scale, 641x641)	41.5	No	kMaX-DeepLab: k-means Mask Transformer	2022-07-08	Code
22	Mask2Former (ResNet-50, 640x640)	39.7	No	Masked-attention Mask Transformer for Universal ...	2021-12-02	Code
23	Panoptic-DeepLab (SwideRNet)	37.9	No	Masked-attention Mask Transformer for Universal ...	2021-12-02	Code
24	MaskFormer (R101 + 6 Enc)	35.7	No	Per-Pixel Classification is Not All You Need for...	2021-07-13	Code

#1OneFormer (InternImage-H, emb_dim=256, single-scale, 896x896)SOTA
54.5
PQ· 2022-11-10
OneFormer: One Transformer to Rule Universal Image Segmentation Code
#2ViT-P (OneFormer, DiNAT-L, single-scale, 1280x1280, COCO_pretrain)
54
PQ· Extra Data· 2025-05-26
The Missing Point in Vision Transformers for Universal Image Segmentation Code
#3OpenSeed(SwinL, single scale, 1280x1280)
53.7
PQ· Extra Data· 2023-03-14
A Simple Framework for Open-Vocabulary Segmentation and Detection Code
#4OneFormer (DiNAT-L, single-scale, 1280x1280, COCO-Pretrain)
53.4
PQ· Extra Data· 2022-11-10
OneFormer: One Transformer to Rule Universal Image Segmentation Code
#5EoMT (DINOv2-g, single-scale, 1280x1280, COCO pre-trained)
52.8
PQ· Extra Data· 2025-03-24
Your ViT is Secretly an Image Segmentation Model Code
#6X-Decoder (Davit-d5, Deform, single-scale, 1280x1280)
52.4
PQ· Extra Data· 2022-12-21
Generalized Decoding for Pixel, Image, and Language Code
#7ViT-P (OneFormer, DiNAT-L, single-scale, 1280x1280)
51.9
PQ· 2025-05-26
The Missing Point in Vision Transformers for Universal Image Segmentation Code
#8OneFormer (DiNAT-L, single-scale, 1280x1280)
51.5
PQ· 2022-11-10
OneFormer: One Transformer to Rule Universal Image Segmentation Code
#9OneFormer (Swin-L, single-scale, 1280x1280)
51.4
PQ· 2022-11-10
OneFormer: One Transformer to Rule Universal Image Segmentation Code
#10kMaX-DeepLab (ConvNeXt-L, single-scale, 1281x1281)SOTA
50.9
PQ· 2022-07-08
kMaX-DeepLab: k-means Mask Transformer Code
#11OneFormer (DiNAT-L, single-scale, 640x640)
50.5
PQ· 2022-11-10
OneFormer: One Transformer to Rule Universal Image Segmentation Code
#12OneFormer (ConvNeXt-XL, single-scale, 640x640)
50.1
PQ· 2022-11-10
OneFormer: One Transformer to Rule Universal Image Segmentation Code
#13OneFormer (ConvNeXt-L, single-scale, 640x640)
50
PQ· 2022-11-10
OneFormer: One Transformer to Rule Universal Image Segmentation Code
#14OneFormer (Swin-L, single-scale, 640x640)
49.8
PQ· 2022-11-10
OneFormer: One Transformer to Rule Universal Image Segmentation Code
#15X-Decoder (L)
49.6
PQ· Extra Data· 2022-12-21
Generalized Decoding for Pixel, Image, and Language Code
#16DiNAT-L (Mask2Former, 640x640)
49.4
PQ· 2022-09-29
Dilated Neighborhood Attention Transformer Code
#17kMaX-DeepLab (ConvNeXt-L, single-scale, 641x641)
48.7
PQ· 2022-07-08
kMaX-DeepLab: k-means Mask Transformer Code
#18Mask2Former (Swin-L)SOTA
48.1
PQ· 2021-12-02
Masked-attention Mask Transformer for Universal Image Segmentation Code
#19Mask2Former (Swin-L + FAPN, 640x640)
46.2
PQ· 2021-12-02
Masked-attention Mask Transformer for Universal Image Segmentation Code
#20kMaX-DeepLab (ResNet50, single-scale, 1281x1281)
42.3
PQ· 2022-07-08
kMaX-DeepLab: k-means Mask Transformer Code
#21kMaX-DeepLab (ResNet50, single-scale, 641x641)
41.5
PQ· 2022-07-08
kMaX-DeepLab: k-means Mask Transformer Code
#22Mask2Former (ResNet-50, 640x640)
39.7
PQ· 2021-12-02
Masked-attention Mask Transformer for Universal Image Segmentation Code
#23Panoptic-DeepLab (SwideRNet)
37.9
PQ· 2021-12-02
Masked-attention Mask Transformer for Universal Image Segmentation Code
#24MaskFormer (R101 + 6 Enc)SOTA
35.7
PQ· 2021-07-13
Per-Pixel Classification is Not All You Need for Semantic Segmentation Code