| # Mask2Former | |
| > [Masked-attention Mask Transformer for Universal Image Segmentation](http://arxiv.org/abs/2112.01527) | |
| <!-- [ALGORITHM] --> | |
| ## 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). | |
| <div align=center> | |
| <img src="https://camo.githubusercontent.com/455d3116845b1d580b1f8a8542334b9752fdf39364deee2951cdd231524c7725/68747470733a2f2f626f77656e63303232312e6769746875622e696f2f696d616765732f6d61736b666f726d657276325f7465617365722e706e67" height="300"/> | |
| </div> | |
| ## Introduction | |
| Mask2Former requires COCO and [COCO-panoptic](http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip) dataset for training and evaluation. You need to download and extract it in the COCO dataset path. | |
| The directory should be like this. | |
| ```none | |
| mmdetection | |
| ├── mmdet | |
| ├── tools | |
| ├── configs | |
| ├── data | |
| │ ├── coco | |
| │ │ ├── annotations | |
| | | | ├── instances_train2017.json | |
| | | | ├── instances_val2017.json | |
| │ │ │ ├── panoptic_train2017.json | |
| │ │ │ ├── panoptic_train2017 | |
| │ │ │ ├── panoptic_val2017.json | |
| │ │ │ ├── panoptic_val2017 | |
| │ │ ├── train2017 | |
| │ │ ├── val2017 | |
| │ │ ├── test2017 | |
| ``` | |
| ## Results and Models | |
| ### Panoptic segmentation | |
| | Backbone | style | Pretrain | Lr schd | Mem (GB) | Inf time (fps) | PQ | box mAP | mask mAP | Config | Download | | |
| | :------: | :-----: | :----------: | :-----: | :------: | :------------: | :--: | :-----: | :------: | :----------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | |
| | R-50 | pytorch | ImageNet-1K | 50e | 13.9 | - | 51.9 | 44.8 | 41.9 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic/mask2former_r50_lsj_8x2_50e_coco-panoptic_20220326_224516-11a44721.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic/mask2former_r50_lsj_8x2_50e_coco-panoptic_20220326_224516.log.json) | | |
| | R-101 | pytorch | ImageNet-1K | 50e | 16.1 | - | 52.4 | 45.3 | 42.4 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_r101_lsj_8x2_50e_coco-panoptic.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r101_lsj_8x2_50e_coco-panoptic/mask2former_r101_lsj_8x2_50e_coco-panoptic_20220329_225104-c54e64c9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r101_lsj_8x2_50e_coco-panoptic/mask2former_r101_lsj_8x2_50e_coco-panoptic_20220329_225104.log.json) | | |
| | Swin-T | - | ImageNet-1K | 50e | 15.9 | - | 53.4 | 46.3 | 43.4 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic_20220326_224553-fc567107.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic_20220326_224553.log.json) | | |
| | Swin-S | - | ImageNet-1K | 50e | 19.1 | - | 54.5 | 47.8 | 44.5 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic_20220329_225200-c7b94355.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic_20220329_225200.log.json) | | |
| | Swin-B | - | ImageNet-1K | 50e | 26.0 | - | 55.1 | 48.2 | 44.9 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic_20220331_002244-c149a9e9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic_20220331_002244.log.json) | | |
| | Swin-B | - | ImageNet-21K | 50e | 25.8 | - | 56.3 | 50.0 | 46.3 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic_20220329_230021-3bb8b482.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic_20220329_230021.log.json) | | |
| | Swin-L | - | ImageNet-21K | 100e | 21.1 | - | 57.6 | 52.2 | 48.5 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic_20220407_104949-d4919c44.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic_20220407_104949.log.json) | | |
| ### Instance segmentation | |
| | Backbone | style | Pretrain | Lr schd | Mem (GB) | Inf time (fps) | box mAP | mask mAP | Config | Download | | |
| | -------- | ------- | ----------- | ------- | -------- | -------------- | ------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | |
| | R-50 | pytorch | ImageNet-1K | 50e | 13.7 | - | 45.7 | 42.9 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_r50_lsj_8x2_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r50_lsj_8x2_50e_coco/mask2former_r50_lsj_8x2_50e_coco_20220506_191028-8e96e88b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r50_lsj_8x2_50e_coco/mask2former_r50_lsj_8x2_50e_coco_20220506_191028.log.json) | | |
| | R-101 | pytorch | ImageNet-1K | 50e | 15.5 | - | 46.7 | 44.0 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_r101_lsj_8x2_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r101_lsj_8x2_50e_coco/mask2former_r101_lsj_8x2_50e_coco_20220426_100250-c50b6fa6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r101_lsj_8x2_50e_coco/mask2former_r101_lsj_8x2_50e_coco_20220426_100250.log.json) | | |
| | Swin-T | - | ImageNet-1K | 50e | 15.3 | - | 47.7 | 44.7 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco_20220508_091649-4a943037.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco_20220508_091649.log.json) | | |
| | Swin-S | - | ImageNet-1K | 50e | 18.8 | - | 49.3 | 46.1 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco_20220504_001756-743b7d99.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco_20220504_001756.log.json) | | |
| Note: We have trained the instance segmentation models many times (see more details in [PR 7571](https://github.com/open-mmlab/mmdetection/pull/7571)). The results of the trained models are relatively stable (+- 0.2), and have a certain gap (about 0.2 AP) in comparison with the results in the [paper](http://arxiv.org/abs/2112.01527). However, the performance of the model trained with the official code is unstable and may also be slightly lower than the reported results as mentioned in the [issue](https://github.com/facebookresearch/Mask2Former/issues/46). | |
| ## Citation | |
| ```latex | |
| @article{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={arXiv}, | |
| year={2021} | |
| } | |
| ``` | |