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correlation-sfswin

Cross-modal object detection based on OpenMMLab MMDetection.

https://github.com/bazenr/correlation-sfswin

Science Score: 67.0%

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    Found 4 DOI reference(s) in README
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    Links to: sciencedirect.com
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    Low similarity (9.1%) to scientific vocabulary
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Repository

Cross-modal object detection based on OpenMMLab MMDetection.

Basic Info
  • Host: GitHub
  • Owner: Bazenr
  • License: apache-2.0
  • Language: Python
  • Default Branch: master
  • Size: 7.93 MB
Statistics
  • Stars: 4
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created about 2 years ago · Last pushed about 1 year ago
Metadata Files
Readme License Citation

README.md

Correlation-SFSwin

Cross-modal object detection based on PyTorch and OpenMMLab MMDetection.

Installation

Install PyTorch

(Please refer to the installation guide on the official PyTorch website) conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia

Install MMCV

(Please refer to the installation guide on the official MMDetection website) pip install -U openmim mim install mmcv

Clone this GitHub repository

git clone https://github.com/Bazenr/Correlation-SFSwin.git

Install this GitHub repository

cd [path to istalled folder] pip install -v -e .

Install third-party libraries

pip install tifffile Optional: pip install sahi pip install openpyxl pip install xlsxWriter

Training

tools/train.py

Test

tools/largeimagedemofusioncyclePredict.py

Links

|Content|Link| |:--------|:-------------| |Open source dataset|https://www.kaggle.com/datasets/bazenr/rgb-hsi-rgb-nir-municipal-solid-waste| |Related paper|Plastic waste identification based on multimodal feature selection and cross-modal Swin Transformer|

Citation

@article{JI202558, title = {Plastic waste identification based on multimodal feature selection and cross-modal Swin Transformer}, author = {Tianchen Ji and Huaiying Fang and Rencheng Zhang and Jianhong Yang and Zhifeng Wang and Xin Wang}, journal = {Waste Management}, volume = {192}, pages = {58-68}, year = {2025}, issn = {0956-053X}, doi = {https://doi.org/10.1016/j.wasman.2024.11.027}, url = {https://www.sciencedirect.com/science/article/pii/S0956053X24005841}, }

Owner

  • Name: Bazen
  • Login: Bazenr
  • Kind: user

A student

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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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
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,<2.2.0
  • mmengine >=0.7.1,<1.0.0
requirements/multimodal.txt pypi
  • fairscale *
  • nltk *
  • pycocoevalcap *
  • transformers *
requirements/optional.txt pypi
  • cityscapesscripts *
  • fairscale *
  • imagecorruptions *
  • scikit-learn *
requirements/readthedocs.txt pypi
  • mmcv >=2.0.0rc4,<2.2.0
  • 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 *
  • tqdm *
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
setup.py pypi