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deep_pollutant

https://github.com/tianlongjia/deep_pollutant

Science Score: 57.0%

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Repository

Basic Info
  • Host: GitHub
  • Owner: TianlongJia
  • License: apache-2.0
  • Language: Python
  • Default Branch: master
  • Size: 12.3 MB
Statistics
  • Stars: 1
  • Watchers: 2
  • Forks: 3
  • Open Issues: 1
  • Releases: 0
Created over 2 years ago · Last pushed 12 months ago
Metadata Files
Readme Contributing License Code of conduct Citation

README.md

Detecting the interaction between microparticles and biomass in biological wastewater treatment process with Deep Learning method

This repository contains the code used for the following publication: bash Jia, T., Peng, Z., Yu, J., Piaggio, A. L., Zhang, S., & de Kreuk, M. K. (2024). Detecting the interaction between microparticles and biomass in biological wastewater treatment process with Deep Learning method. Science of The Total Environment, 175813. doi: 10.1016/j.scitotenv.2024.175813

The aim of this code is to use deep learning models to detect the interaction between microparticles and biomass in biological wastewater treatment process.

summary_figure

Acknowledgement:

This project was inspired by an open source project "MMDetection". Learn more about MMDetection at documentation.

Dataset

The TUD-IPB dataset is a new labelled dataset for detecting the interaction between microparticles and biomass in biological wastewater treatment process. This dataset and further details can be found in:

bash https://doi.org/10.5281/zenodo.13374998

Installation:

Requirements: - Windows 10 or Linux - Python 3.8.16 - Pytorch 1.13.1

(1) Install Pytorch 1.13.1 (CUDA 11.7) (for Windows 10)

bash conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia

(2) Install MMCV using MIM bash pip install -U openmim mim install mmengine==0.8.4 mim install mmcv==2.0.1

(3) Install MMDetection bash mim install mmdet==3.1.0

(4) Install other packages

bash pip install -r requirements.txt

Note

This repository only includes the implementation of six model architectures: (1) Mask RCNN (ResNet50), (2) Mask RCNN (ResNet101), (3) Cascade Mask RCNN(ResNet50), (4) Cascade Mask RCNN(ResNet101), (5) Yolact (ResNet50), and (6) Yolact (ResNet101). The implementation of YOLOv8 can be found here

Usage

  • main_InstanceSeg_train.ipynb is the code for training deep models for object detection.
  • main_InstanceSeg_evaluate.ipynb is the code for (1) evaluating model performances on test sets (e.g., outputing mAP50), (2) predicting objects in images, (3) outputing confusion matrix, and (4) outputing bounding box (bbox) and mask information (e.g., the area of each bbox and mask).

Citing this dataste or paper

If you find this code and dataset are useful in your research or wish to refer to the paper, please use the following BibTeX entry.

BibTeX @article{jia2024detecting, title={Detecting the interaction between microparticles and biomass in biological wastewater treatment process with Deep Learning method}, author={Jia, Tianlong and Peng, Zhaoxu and Yu, Jing and Piaggio, Antonella L and Zhang, Shuo and de Kreuk, Merle K}, journal={Science of The Total Environment}, pages={175813}, year={2024}, publisher={Elsevier} }

Authors

Contact

➡️ Tianlong Jia (T.Jia@tudelft.nl)

Owner

  • Name: Tianlong Jia
  • Login: TianlongJia
  • Kind: user
  • Company: Delft University of Technology

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - name: "MMDetection Contributors"
title: "OpenMMLab Detection Toolbox and Benchmark"
date-released: 2018-08-22
url: "https://github.com/open-mmlab/mmdetection"
license: Apache-2.0

GitHub Events

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  • Issues event: 1
  • Issue comment event: 2
  • Push event: 4
  • Pull request event: 6
  • Fork event: 4
Last Year
  • Issues event: 1
  • Issue comment event: 2
  • Push event: 4
  • Pull request event: 6
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Dependencies

.github/workflows/deploy.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
.circleci/docker/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
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
docker/serve_cn/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
requirements/albu.txt pypi
  • albumentations >=0.3.2
requirements/build.txt pypi
  • cython *
  • numpy *
requirements/docs.txt pypi
  • docutils ==0.16.0
  • myst-parser *
  • sphinx ==4.0.2
  • sphinx-copybutton *
  • sphinx_markdown_tables *
  • sphinx_rtd_theme ==0.5.2
  • urllib3 <2.0.0
requirements/mminstall.txt pypi
  • mmcv >=2.0.0rc4,<2.1.0
  • mmengine >=0.7.1,<1.0.0
requirements/multimodal.txt pypi
  • nltk *
  • pycocoevalcap *
  • transformers *
requirements/optional.txt pypi
  • cityscapesscripts *
  • imagecorruptions *
  • scikit-learn *
requirements/readthedocs.txt pypi
  • mmcv >=2.0.0rc4,<2.1.0
  • mmengine >=0.7.1,<1.0.0
  • scipy *
  • torch *
  • torchvision *
  • urllib3 <2.0.0
requirements/runtime.txt pypi
  • matplotlib *
  • numpy *
  • pycocotools *
  • scipy *
  • shapely *
  • six *
  • terminaltables *
requirements/tests.txt pypi
  • asynctest * test
  • cityscapesscripts * test
  • codecov * test
  • flake8 * test
  • imagecorruptions * test
  • instaboostfast * test
  • interrogate * test
  • isort ==4.3.21 test
  • kwarray * test
  • memory_profiler * test
  • nltk * test
  • onnx ==1.7.0 test
  • onnxruntime >=1.8.0 test
  • parameterized * test
  • prettytable * test
  • protobuf <=3.20.1 test
  • psutil * test
  • pytest * test
  • transformers * test
  • ubelt * test
  • xdoctest >=0.10.0 test
  • yapf * test
requirements/tracking.txt pypi
  • mmpretrain *
  • motmetrics *
  • numpy <1.24.0
  • scikit-learn *
  • seaborn *
requirements.txt pypi
  • ipykernel ==6.15.1
  • mmpretrain ==1.0.2
  • tensorboard ==2.12.0
  • xlwings ==0.30.5
setup.py pypi