deep_plastic_yolov8
Science Score: 57.0%
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✓codemeta.json file
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✓.zenodo.json file
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✓DOI references
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○Scientific vocabulary similarity
Low similarity (9.7%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: TianlongJia
- License: gpl-3.0
- Language: Python
- Default Branch: master
- Size: 360 KB
Statistics
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Detection of floating plastic litter and water hyacinths using Yolov8 deep learning model
This repository contains the code used for the following publication:
bash
To do: XXXXXXXX
The aim of this code is to use Yolov8 deep learning model to detect floating plastic litter and water hyacinths in the Saigon rivers, Vietnam.
Acknowledgement:
This project was inspired by the work of Ultralytics YOLOv8 (https://github.com/ultralytics/ultralytics). Learn more about Ultralytics YOLOv8 at documentation.
Dataset
"XXX" dataset is a new labelled dataset for detecting floating plastic litter and water hyacinths with computer vision. It includes 272 images and 9,352 annotated plastic litter items and water hyacinths (with bounding boxes). This dataset and further details can be found in:
bash
To do: XXXXXXXX
Requirements:
- Windows 10
- Python 3.9.12
- Pytorch 2.0.0
(1) Install Pytorch 2.0.0 (CUDA 11.7) in Windows10
bash
conda install pytorch==2.0.0 torchvision==0.15.0 torchaudio==2.0.0 pytorch-cuda=11.7 -c pytorch -c nvidia
(2) Install other packages
bash
pip install -r requirements.txt
Usage
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main_Train_.ipynbis the code for training the Yolov8 model for object detection. -
main_Evaluate.ipynbis the code for (1) evaluating model performances on test sets (e.g., output mAP50, precision and recall), (2) predicting objects in images and videos, and (3) outputing bounding box (bbox) information (e.g., the area of each bbox).
Model weights
The trained model weight files from the pubilication can be found in:
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https://doi.org/10.5281/zenodo.12800597
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
XXXXX
Contact
➡️ Tianlong Jia (T.Jia@tudelft.nl)
Owner
- Name: Tianlong Jia
- Login: TianlongJia
- Kind: user
- Company: Delft University of Technology
- Repositories: 1
- Profile: https://github.com/TianlongJia
Citation (CITATION.cff)
cff-version: 1.2.0
preferred-citation:
type: software
message: If you use this software, please cite it as below.
authors:
- family-names: Jocher
given-names: Glenn
orcid: "https://orcid.org/0000-0001-5950-6979"
- family-names: Chaurasia
given-names: Ayush
orcid: "https://orcid.org/0000-0002-7603-6750"
- family-names: Qiu
given-names: Jing
orcid: "https://orcid.org/0000-0003-3783-7069"
title: "YOLO by Ultralytics"
version: 8.0.0
# doi: 10.5281/zenodo.3908559 # TODO
date-released: 2023-1-10
license: GPL-3.0
url: "https://github.com/ultralytics/ultralytics"
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Dependencies
- pytorch/pytorch latest build
- Pillow ==9.4.0
- PyYAML >=5.3.1
- chardet ==5.1.0
- ipykernel ==6.15.0
- ipython ==8.10.0
- matplotlib >=3.2.2
- numpy >=1.18.5
- opencv-python >=4.6.0
- pandas >=1.1.4
- psutil *
- requests >=2.23.0
- scipy >=1.4.1
- seaborn >=0.11.0
- tensorboard >=2.4.1
- thop >=0.1.1
- tqdm >=4.64.0
- wheel >=0.38.0
- xlwings ==0.30.4