Reimplementation of Dynamic Multi-scale filters for Semantic Segmentation.

Overview

DMNet-paddlepaddle

1.Introduction

Paddle implementation of Dynamic Multi-scale filters for Semantic Segmentation. Our team is computer vision(计算机幻觉).

Official Repository  | mmsegmentation version  | Paper

2.Results and models

2.1Cityscapes

The following results are from mmsegmentation version. Pretrained weighs can be downloaded from

R-101-D8 or baidu cloud   with code hhnn.

Method Backbone Crop Size Lr schd Inf time (fps) mIoU config download
DMNet R-101-D8 512x1024 80000 - 79.64 config model | log

We only trained the Cityscapes dataset with backnone R-101-D8,the results:

Method Backbone Crop Size Lr schd Inf time (fps) mIoU config download
DMNet R-101-D8 512x1024 80000 -

3.Quick start

3.1Prerequisites

  • Linux
  • Python 3.6+
  • Paddlepaddle
  • CUDA 10.0+
  • GCC 5+

3.2Installation

a.Install dependencies

pip install -r requestments.txt

License

This project is released under the Apache 2.0 license.

Contributing

@Hongqiang.Wang

@luyuxuan

Citation

@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}
}
Owner
Hongqiang.Wang
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