AddaxAI
AddaxAI: A no-code platform to train and deploy custom YOLOv5 object detection models - Published in JOSS (2023)
Science Score: 100.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 5 DOI reference(s) in README and JOSS metadata -
✓Academic publication links
Links to: joss.theoj.org -
✓Committers with academic emails
2 of 12 committers (16.7%) from academic institutions -
○Institutional organization owner
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✓JOSS paper metadata
Published in Journal of Open Source Software
Keywords
Scientific Fields
Repository
Simplify camera trap image analysis with AI species recognition models based around the MegaDetector model
Basic Info
- Host: GitHub
- Owner: PetervanLunteren
- License: mit
- Language: Python
- Default Branch: main
- Homepage: https://addaxdatascience.com/addaxai/
- Size: 130 MB
Statistics
- Stars: 149
- Watchers: 14
- Forks: 24
- Open Issues: 6
- Releases: 58
Topics
Metadata Files
README.md
Official website: https://addaxdatascience.com/addaxai/
AddaxAI is an application designed to streamline the work of ecologists dealing with camera trap images. It’s an AI platform that allows you to analyse images with machine learning models for automatic detection, offering ecologists a way to save time and focus on conservation efforts.
Project name change
To avoid any legal concerns, we have renamed our project from EcoAssist to AddaxAI. The project itself remains the same—only the name has changed.
Cite AddaxAI in your research
If you used AddaxAI in your research, please include the following citation, along with the models used to analyze your data.
BibTeX
@article{van Lunteren2023,
title = {AddaxAI: A no-code platform to train and deploy custom YOLOv5 object detection models},
author = {Peter van Lunteren},
journal = {Journal of Open Source Software},
doi = {10.21105/joss.05581},
url = {https://doi.org/10.21105/joss.05581},
year = {2023},
publisher = {The Open Journal},
volume = {8},
number = {88},
pages = {5581}
}
Contribute
Interested in contributing to this project? There are always things to do. The list of to-do items, bug reports, and feature requests is always evolving. I try to keep a semi-structured list here. Is there something you would be interested in? Get in touch and I will guide you in the right direction. Thanks!
Owner
- Name: Peter van Lunteren
- Login: PetervanLunteren
- Kind: user
- Location: Utrecht, NL
- Repositories: 3
- Profile: https://github.com/PetervanLunteren
Wildlife ecologist and data scientist with a special interest in ML
JOSS Publication
AddaxAI: A no-code platform to train and deploy custom YOLOv5 object detection models
Authors
Independent Researcher, The Netherlands
Editor
Marcel Stimberg
Tags
artificial intelligence machine learning deep learning object detection yolov5 annotation tool cameratrapsCitation (citation.cff)
cff-version: "1.2.0"
authors:
- family-names: Lunteren
given-names: Peter
name-particle: van
orcid: "https://orcid.org/0000-0001-5488-4225"
doi: 10.5281/zenodo.7223363
message: If you use this software, please cite our article in the
Journal of Open Source Software.
preferred-citation:
authors:
- family-names: Lunteren
given-names: Peter
name-particle: van
orcid: "https://orcid.org/0000-0001-5488-4225"
date-published: 2023-08-04
doi: 10.21105/joss.05581
issn: 2475-9066
issue: 88
journal: Journal of Open Source Software
publisher:
name: Open Journals
start: 5581
title: "AddaxAI: A no-code platform to train and deploy custom
YOLOv5 object detection models"
type: article
url: "https://joss.theoj.org/papers/10.21105/joss.05581"
volume: 8
title: "AddaxAI: A no-code platform to train and deploy custom YOLOv5
object detection models"
GitHub Events
Total
- Create event: 22
- Issues event: 23
- Release event: 12
- Watch event: 18
- Delete event: 13
- Issue comment event: 46
- Push event: 437
- Pull request review event: 1
- Pull request event: 16
- Fork event: 6
Last Year
- Create event: 22
- Issues event: 23
- Release event: 12
- Watch event: 18
- Delete event: 13
- Issue comment event: 46
- Push event: 438
- Pull request review event: 1
- Pull request event: 16
- Fork event: 6
Committers
Last synced: 5 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Peter van Lunteren | c****t@p****m | 1,372 |
| Peter van Lunteren | p****n@P****l | 88 |
| github-actions[bot] | g****] | 27 |
| Peter van Lunteren | p****r@P****l | 12 |
| Evan Hallein | e****n@i****m | 7 |
| Peter van Lunteren | p****n@P****e | 4 |
| Dan Morris | d****s@c****u | 2 |
| wsyxbcl | y****i@s****n | 2 |
| Robert Kampf | R****f@g****e | 1 |
| ESSNIA | s****e@t****a | 1 |
| Peter van Lunteren | p****n@P****n | 1 |
| = | = | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 35
- Total pull requests: 27
- Average time to close issues: 7 days
- Average time to close pull requests: 1 day
- Total issue authors: 23
- Total pull request authors: 8
- Average comments per issue: 2.86
- Average comments per pull request: 0.59
- Merged pull requests: 21
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 17
- Pull requests: 18
- Average time to close issues: 13 days
- Average time to close pull requests: 2 days
- Issue authors: 10
- Pull request authors: 5
- Average comments per issue: 2.35
- Average comments per pull request: 0.11
- Merged pull requests: 13
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- arky (5)
- ESSNIA (3)
- SimonKravis (3)
- ehallein (3)
- Anujkrishna12 (2)
- PetervanLunteren (2)
- sergewich (1)
- rolanddu (1)
- Lorenzofrangini (1)
- JoejynWan (1)
- iTsMaaT (1)
- andrea-petrullo (1)
- jillwettlaufer (1)
- FedakD (1)
- alexnkorovin (1)
Pull Request Authors
- PetervanLunteren (13)
- ehallein (4)
- agentmorris (3)
- UlvHare (2)
- ESSNIA (2)
- wsyxbcl (1)
- mstimberg (1)
- davidmarcos98 (1)
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- jgehrcke/github-repo-stats RELEASE composite