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pidnet-with-rpem

https://github.com/guanchengzhou/pidnet-with-rpem

Science Score: 44.0%

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    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    Scientific vocabulary similarity
    Low similarity (5.3%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: GuanchengZhou
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Size: 5.9 MB
Statistics
  • Stars: 2
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 2 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

PIDNet with RPEM

Introduction

This is the official repository for our recent work (Zhou, G. C., Cheng, C., & Chen, Y. Z. (2024). Efficient multi-branch segmentation network for situation awareness in autonomous navigation. Ocean Engineering, 302, 117741.).

Usage

0. Prepare the dataset

  • Follow the guide of mmsegmentation to prepare the environment.
  • Download the MaSTr1325 or contact us for experiental data in data/.
  • Transform the format of the dataset to Cityscapes format. ### 1. Training
  • Use the config file (configs/RPEM/pidnet-s-rpem-sin.py) and follow the guide of mmsegmentation to train the model. The basic usage is as follows. bash python tools/train.py configs/RPEM/pidnet-s-rpem-sin.py
  • In config file, the option space_block=True represents the use of RPEM. The option space_emb='linear', space_emb='sinnn' and space_emb='sin' represents the use of linear positional encoding, sin positional encoding and normalized sin positional encoding.

2. Testing

  • Follow the guide of mmsegmentation or use basic usage as follows. bash python tools/test.py configs/RPEM/pidnet-s-rpem-sin.py

Owner

  • Name: Guancheng Zhou
  • Login: GuanchengZhou
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - name: "MMSegmentation Contributors"
title: "OpenMMLab Semantic Segmentation Toolbox and Benchmark"
date-released: 2020-07-10
url: "https://github.com/open-mmlab/mmsegmentation"
license: Apache-2.0

GitHub Events

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Dependencies

docker/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/serve/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
requirements/albu.txt pypi
  • albumentations >=0.3.2
requirements/docs.txt pypi
  • docutils ==0.16.0
  • myst-parser *
  • sphinx ==4.0.2
  • sphinx_copybutton *
  • sphinx_markdown_tables *
  • urllib3 <2.0.0
requirements/mminstall.txt pypi
  • mmcv >=2.0.0rc4
  • mmengine >=0.5.0,<1.0.0
requirements/optional.txt pypi
  • cityscapesscripts *
  • nibabel *
requirements/readthedocs.txt pypi
  • mmcv >=2.0.0rc1,<2.1.0
  • mmengine >=0.4.0,<1.0.0
  • prettytable *
  • scipy *
  • torch *
  • torchvision *
requirements/runtime.txt pypi
  • matplotlib *
  • numpy *
  • packaging *
  • prettytable *
  • scipy *
requirements/tests.txt pypi
  • codecov * test
  • flake8 * test
  • interrogate * test
  • pytest * test
  • xdoctest >=0.10.0 test
  • yapf * test
requirements.txt pypi
setup.py pypi