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Panoptic Feature Pyramid Networks

Introduction

[ALGORITHM]

@article{Kirillov_2019,
   title={Panoptic Feature Pyramid Networks},
   ISBN={9781728132938},
   url={http://dx.doi.org/10.1109/CVPR.2019.00656},
   DOI={10.1109/cvpr.2019.00656},
   journal={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
   publisher={IEEE},
   author={Kirillov, Alexander and Girshick, Ross and He, Kaiming and Dollar, Piotr},
   year={2019},
   month={Jun}
}

Results and models

Cityscapes

Method Backbone Crop Size Lr schd Mem (GB) Inf time (fps) mIoU mIoU(ms+flip) download
FPN R-50 512x1024 80000 2.8 13.54 74.52 76.08 model | log
FPN R-101 512x1024 80000 3.9 10.29 75.80 77.40 model | log

ADE20K

Method Backbone Crop Size Lr schd Mem (GB) Inf time (fps) mIoU mIoU(ms+flip) download
FPN R-50 512x512 160000 4.9 55.77 37.49 39.09 model | log
FPN R-101 512x512 160000 5.9 40.58 39.35 40.72 model | log