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| # Mask2Former | ||
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| [Masked-attention Mask Transformer for Universal Image Segmentation](https://arxiv.org/abs/2112.01527) | ||
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| ## Introduction | ||
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| <!-- [ALGORITHM] --> | ||
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| <a href="https://github.com/facebookresearch/Mask2Former">Official Repo</a> | ||
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| <a href="https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py">Code Snippet</a> | ||
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| ## Abstract | ||
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| <!-- [ABSTRACT] --> | ||
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| 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). | ||
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| ```bibtex | ||
| @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} | ||
| } | ||
| ``` | ||
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| ## Results and models | ||
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| ### Cityscapes | ||
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| | Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | | ||
| | ----------- | -------------- | --------- | ------- | -------: | -------------- | ----- | ------------: | -----------------------------------------------------------------------------------------------------------------------------------------------------------: | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | ||
| | Mask2Former | R-50-D32 | 512x1024 | 90000 | 5806 | 9.17 | 80.16 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_r50_8xb2-90k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-90k_cityscapes-512x1024/mask2former_r50_8xb2-90k_cityscapes-512x1024_20221113_021441-c9da5c95.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-90k_cityscapes-512x1024/mask2former_r50_8xb2-90k_cityscapes-512x1024_20221113_021441.json) | | ||
| | Mask2Former | R-101-D32 | 512x1024 | 90000 | 6971 | 7.11 | 80.81 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_r101_8xb2-90k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r101_8xb2-90k_cityscapes-512x1024/mask2former_r101_8xb2-90k_cityscapes-512x1024_20221130_031628-8ad528ea.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r101_8xb2-90k_cityscapes-512x1024/mask2former_r101_8xb2-90k_cityscapes-512x1024_20221130_031628.json)) | | ||
| | Mask2Former | Swin-T | 512x1024 | 90000 | 6511 | 7.18 | 81.71 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-t_8xb2-90k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-t_8xb2-90k_cityscapes-512x1024/mask2former_swin-t_8xb2-90k_cityscapes-512x1024_20221127_144501-290b34af.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-t_8xb2-90k_cityscapes-512x1024/mask2former_swin-t_8xb2-90k_cityscapes-512x1024_20221127_144501.json)) | | ||
| | Mask2Former | Swin-S | 512x1024 | 90000 | 8282 | 5.57 | 82.57 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-s_8xb2-90k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-s_8xb2-90k_cityscapes-512x1024/mask2former_swin-s_8xb2-90k_cityscapes-512x1024_20221127_143802-7c98854a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-s_8xb2-90k_cityscapes-512x1024/mask2former_swin-s_8xb2-90k_cityscapes-512x1024_20221127_143802.json)) | | ||
| | Mask2Former | Swin-B (in22k) | 512x1024 | 90000 | 11152 | 4.32 | 83.52 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024_20221127_150026-efd13f24.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024_20221127_150026.json)) | | ||
| | Mask2Former | Swin-L (in22k) | 512x1024 | 90000 | 16207 | 2.86 | 83.38 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024_20221101_105159-f285471d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024_20221101_105159.json)) | | ||
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| ### ADE20K | ||
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| | Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | | ||
| | ----------- | -------------- | --------- | ------- | -------: | -------------- | ----- | ------------: | -------------------------------------------------------------------------------------------------------------------------------------------------------: | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | ||
| | Mask2Former | R-50-D32 | 512x512 | 160000 | 3385 | 26.59 | 47.22 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221116_192723-7b365f38.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-90k_cityscapes-512x1024_20221113_021441.json)) | | ||
| | Mask2Former | R-101-D32 | 512x512 | 160000 | 4190 | 22.97 | 47.8 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_r101_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r101_8xb2-160k_ade20k-512x512/mask2former_r101_8xb2-160k_ade20k-512x512_20221030_022305-9be49fd1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r101_8xb2-160k_ade20k-512x512/mask2former_r101_8xb2-160k_ade20k-512x512_20221030_022305.json)) | | ||
| | Mask2Former | Swin-T | 512x512 | 160000 | 3826 | 23.82 | 48.15 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-t_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-t_8xb2-160k_ade20k-512x512/mask2former_swin-t_8xb2-160k_ade20k-512x512_20221113_151352-12213349.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-t_8xb2-160k_ade20k-512x512/mask2former_swin-t_8xb2-160k_ade20k-512x512_20221113_151352.json)) | | ||
| | Mask2Former | Swin-S | 512x512 | 160000 | 5034 | 19.69 | 51.09 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-s_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-s_8xb2-160k_ade20k-512x512/mask2former_swin-s_8xb2-160k_ade20k-512x512_20221117_104921-e4bab9b8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-s_8xb2-160k_ade20k-512x512/mask2former_swin-s_8xb2-160k_ade20k-512x512_20221117_104921.json)) | | ||
| | Mask2Former | Swin-B | 640x640 | 160000 | 5795 | 12.48 | 52.44 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640_20221129_125118-35e3a2c7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640_20221129_125118.json)) | | ||
| | Mask2Former | Swin-B (in22k) | 640x640 | 160000 | 5795 | 12.43 | 53.95 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640_20221031_002811-028d15bd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640_20221031_002811.json)) | | ||
| | Mask2Former | Swin-L (in22k) | 640x640 | 160000 | 9077 | 8.81 | 56.11 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640_20221030_022757-92b3a2f0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640_20221030_022757.json)) | |
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