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README.md | 2 years ago | |
maskformer_r50_mstrain_16x1_75e_coco.py | 2 years ago | |
maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco.py | 2 years ago | |
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Per-Pixel Classification is Not All You Need for Semantic Segmentation
Modern approaches typically formulate semantic segmentation as a per-pixel classification task, while instance-level segmentation is handled with an alternative mask classification. Our key insight: mask classification is sufficiently general to solve both semantic- and instance-level segmentation tasks in a unified manner using the exact same model, loss, and training procedure. Following this observation, we propose MaskFormer, a simple mask classification model which predicts a set of binary masks, each associated with a single global class label prediction. Overall, the proposed mask classification-based method simplifies the landscape of effective approaches to semantic and panoptic segmentation tasks and shows excellent empirical results. In particular, we observe that MaskFormer outperforms per-pixel classification baselines when the number of classes is large. Our mask classification-based method outperforms both current state-of-the-art semantic (55.6 mIoU on ADE20K) and panoptic segmentation (52.7 PQ on COCO) models.
MaskFormer requires COCO and COCO-panoptic dataset for training and evaluation. You need to download and extract it in the COCO dataset path.
The directory should be like this.
mmdetection
├── mmdet
├── tools
├── configs
├── data
│ ├── coco
│ │ ├── annotations
│ │ │ ├── panoptic_train2017.json
│ │ │ ├── panoptic_train2017
│ │ │ ├── panoptic_val2017.json
│ │ │ ├── panoptic_val2017
│ │ ├── train2017
│ │ ├── val2017
│ │ ├── test2017
Backbone | style | Lr schd | Mem (GB) | Inf time (fps) | PQ | SQ | RQ | PQ_th | SQ_th | RQ_th | PQ_st | SQ_st | RQ_st | Config | Download | detail |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
R-50 | pytorch | 75e | 16.2 | - | 46.854 | 80.617 | 57.085 | 51.089 | 81.511 | 61.853 | 40.463 | 79.269 | 49.888 | config | model | log | This version was mentioned in Table XI, in paper Masked-attention Mask Transformer for Universal Image Segmentation |
Swin-L | pytorch | 300e | 27.2 | - | 53.249 | 81.704 | 64.231 | 58.798 | 82.923 | 70.282 | 44.874 | 79.863 | 55.097 | config | model | log | - |
@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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