Jordan-Pierce/CoralNet-Toolbox
Tools for panoptic segmentation and developing machine learning models for benthic imagery
This tool helps marine biologists and ecologists analyze coral reef images more efficiently. It takes your underwater benthic images and allows you to quickly annotate corals and other marine life, then trains AI models to automate future classification and segmentation tasks. The output is accurately labeled image data and trained AI models ready for research.
Used by 1 other package. Available on PyPI.
Use this if you routinely analyze large volumes of coral reef imagery and want to accelerate your annotation and analysis workflows using AI.
Not ideal if you are not working with benthic imagery or if your primary need is for a general-purpose image labeling tool outside of the CoralNet ecosystem.
Stars
44
Forks
7
Language
Python
License
—
Category
Last pushed
Mar 13, 2026
Commits (30d)
0
Dependencies
33
Reverse dependents
1
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Jordan-Pierce/CoralNet-Toolbox"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
ocean-data-factory-sweden/kso
Notebooks to upload/download marine footage, connect to a citizen science project, train machine...
NYU-MLDA/OpenABC
OpenABC-D is a large-scale labeled dataset generated by synthesizing open source hardware IPs....
IQTLabs/AISonobuoy
Maritime Situational Awareness: An Exploration
jackcook/bigger-fish
Code for the paper “There’s Always a Bigger Fish”
microsoft/Project_Natick_Analysis
GitHub Repository for Blogpost: Monitoring environmental conditions near underwater datacenters...