Image Segmentation
Transformers
PyTorch
upernet
Inference Endpoints
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PSANet: Point-wise Spatial Attention Network for Scene Parsing

Introduction

[ALGORITHM]

@inproceedings{zhao2018psanet,
  title={Psanet: Point-wise spatial attention network for scene parsing},
  author={Zhao, Hengshuang and Zhang, Yi and Liu, Shu and Shi, Jianping and Change Loy, Chen and Lin, Dahua and Jia, Jiaya},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  pages={267--283},
  year={2018}
}

Results and models

Cityscapes

Method Backbone Crop Size Lr schd Mem (GB) Inf time (fps) mIoU mIoU(ms+flip) download
PSANet R-50-D8 512x1024 40000 7 3.17 77.63 79.04 model | log
PSANet R-101-D8 512x1024 40000 10.5 2.20 79.14 80.19 model | log
PSANet R-50-D8 769x769 40000 7.9 1.40 77.99 79.64 model | log
PSANet R-101-D8 769x769 40000 11.9 0.98 78.43 80.26 model | log
PSANet R-50-D8 512x1024 80000 - - 77.24 78.69 model | log
PSANet R-101-D8 512x1024 80000 - - 79.31 80.53 model | log
PSANet R-50-D8 769x769 80000 - - 79.31 80.91 model | log
PSANet R-101-D8 769x769 80000 - - 79.69 80.89 model | log

ADE20K

Method Backbone Crop Size Lr schd Mem (GB) Inf time (fps) mIoU mIoU(ms+flip) download
PSANet R-50-D8 512x512 80000 9 18.91 41.14 41.91 model | log
PSANet R-101-D8 512x512 80000 12.5 13.13 43.80 44.75 model | log
PSANet R-50-D8 512x512 160000 - - 41.67 42.95 model | log
PSANet R-101-D8 512x512 160000 - - 43.74 45.38 model | log

Pascal VOC 2012 + Aug

Method Backbone Crop Size Lr schd Mem (GB) Inf time (fps) mIoU mIoU(ms+flip) download
PSANet R-50-D8 512x512 20000 6.9 18.24 76.39 77.34 model | log
PSANet R-101-D8 512x512 20000 10.4 12.63 77.91 79.30 model | log
PSANet R-50-D8 512x512 40000 - - 76.30 77.35 model | log
PSANet R-101-D8 512x512 40000 - - 77.73 79.05 model | log