Dynamic Multi-scale Filters for Semantic Segmentation (DMNet ICCV'2019)

Related tags

Deep Learning semantic-segmentation dcm dfn cityscapes ade20k dynamic-filter dynamic-filter-generation mmsegmentation dmnet dynamic-conv-module dynamic-multiscale-filters dynamic-filter-network
Overview

Dynamic Multi-scale Filters for Semantic Segmentation (DMNet ICCV'2019)

Introduction

Official implementation of Dynamic Multi-scale Filters for Semantic Segmentation (Paper).
🔥 🔥 DMNet is on MMsegmentation. 🔥 🔥 

@InProceedings{He_2019_ICCV,
author = {He, Junjun and Deng, Zhongying and Qiao, Yu},
title = {Dynamic Multi-Scale Filters for Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}

Overview

Framework

image

Results and models

Cityscapes

Method Backbone Crop Size Lr schd Mem (GB) Inf time (fps) mIoU mIoU(ms+flip) config download
DMNet R-50-D8 512x1024 40000 7.0 3.66 77.78 79.14 config model | log
DMNet R-101-D8 512x1024 40000 10.6 2.54 78.37 79.72 config model | log
DMNet R-50-D8 769x769 40000 7.9 1.57 78.49 80.27 config model | log
DMNet R-101-D8 769x769 40000 12.0 1.01 77.62 78.94 config model | log
DMNet R-50-D8 512x1024 80000 - - 79.07 80.22 config model | log
DMNet R-101-D8 512x1024 80000 - - 79.64 80.67 config model | log
DMNet R-50-D8 769x769 80000 - - 79.22 80.55 config model | log
DMNet R-101-D8 769x769 80000 - - 79.19 80.65 config model | log

ADE20K

Method Backbone Crop Size Lr schd Mem (GB) Inf time (fps) mIoU mIoU(ms+flip) config download
DMNet R-50-D8 512x512 80000 9.4 20.95 42.37 43.62 config model | log
DMNet R-101-D8 512x512 80000 13.0 13.88 45.34 46.13 config model | log
DMNet R-50-D8 512x512 160000 - - 43.15 44.17 config model | log
DMNet R-101-D8 512x512 160000 - - 45.42 46.76 config model | log
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Owner
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GitHub https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
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