mmsegmentation/configs/mask2former

• Mask2Former
• • Introduction
  • Abstract
  • • Usage
  • Results and models
  • • Cityscapes
    • ADE20K
  • Citation

Mask2Former

Masked-attention Mask Transformer for Universal Image Segmentation

Introduction

Official Repo

Code Snippet

Abstract

Image segmentation is about grouping pixels with different semantics, e.g., category or instance membership, where each choice of semantics defines a task. While only the semantics of each task differ, current research focuses on designing specialized architectures for each task. We present Masked-attention Mask Transformer (Mask2Former), a new architecture capable of addressing any image segmentation task (panoptic, instance or semantic). Its key components include masked attention, which extracts localized features by constraining cross-attention within predicted mask regions. In addition to reducing the research effort by at least three times, it outperforms the best specialized architectures by a significant margin on four popular datasets. Most notably, Mask2Former sets a new state-of-the-art for panoptic segmentation (57.8 PQ on COCO), instance segmentation (50.1 AP on COCO) and semantic segmentation (57.7 mIoU on ADE20K).

Usage

• Mask2Former model needs to install MMDetection first.

pip install "mmdet>=3.0.0rc4"

Results and models

Cityscapes

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	Device	mIoU	mIoU(ms+flip)	config	download
Mask2Former	R-50-D32	512x1024	90000	5.67	9.17	A100	80.44	-	config	model | log
Mask2Former	R-101-D32	512x1024	90000	6.81	7.11	A100	80.80	-	config	model | log)
Mask2Former	Swin-T	512x1024	90000	6.36	7.18	A100	81.71	-	config	model | log)
Mask2Former	Swin-S	512x1024	90000	8.09	5.57	A100	82.57	-	config	model | log)
Mask2Former	Swin-B (in22k)	512x1024	90000	10.89	4.32	A100	83.52	-	config	model | log)
Mask2Former	Swin-L (in22k)	512x1024	90000	15.83	2.86	A100	83.65	-	config	model | log)

ADE20K

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	Device	mIoU	mIoU(ms+flip)	config	download
Mask2Former	R-50-D32	512x512	160000	3.31	26.59	A100	47.87	-	config	model | log)
Mask2Former	R-101-D32	512x512	160000	4.09	22.97	A100	48.60	-	config	model | log)
Mask2Former	Swin-T	512x512	160000	3826	23.82	A100	48.66	-	config	model | log)
Mask2Former	Swin-S	512x512	160000	3.74	19.69	A100	51.24	-	config	model | log)
Mask2Former	Swin-B	640x640	160000	5.66	12.48	A100	52.44	-	config	model | log)
Mask2Former	Swin-B (in22k)	640x640	160000	5.66	12.43	A100	53.90	-	config	model | log)
Mask2Former	Swin-L (in22k)	640x640	160000	8.86	8.81	A100	56.01	-	config	model | log)

Note:

• All experiments of Mask2Former are implemented with 8 A100 GPUs with 2 samplers per GPU.
• As mentioned at the official repo, the results of Mask2Former are relatively not stable, the result of Mask2Former(swin-s) on ADE20K dataset in the table is the medium result obtained by training 5 times following the suggestion of the author.
• The ResNet backbones utilized in MaskFormer models are standard ResNet rather than ResNetV1c.
• Test time augmentation is not supported in MMSegmentation 1.x version yet, we would add "ms+flip" results as soon as possible.

Citation

@inproceedings{cheng2021mask2former,
  title={Masked-attention Mask Transformer for Universal Image Segmentation},
  author={Bowen Cheng and Ishan Misra and Alexander G. Schwing and Alexander Kirillov and Rohit Girdhar},
  journal={CVPR},
  year={2022}
}
@inproceedings{cheng2021maskformer,
  title={Per-Pixel Classification is Not All You Need for Semantic Segmentation},
  author={Bowen Cheng and Alexander G. Schwing and Alexander Kirillov},
  journal={NeurIPS},
  year={2021}
}