coralnet-toolbox 0.0.83

Tools for annotating and developing ML models for benthic imagery

CoralNet-Toolbox 🪸🧰

🌊 Empowering Coral Reef Research with AI-Powered Annotation Tools 🌊

An unofficial toolkit to supercharge your CoralNet workflows with cutting-edge computer vision

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📊 Project Stats

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✨ Why CoralNet-Toolbox?

🎯 Smart Annotation	🤖 AI-Powered	🚀 Complete Pipeline
Create patches, rectangles, and polygons with intelligent assistance	Leverage SAM, YOLO, and foundation models	From data collection to deployment
Precision meets efficiency	Cutting-edge AI at your fingertips	End-to-end workflow automation

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⚡ Quick Start ⚡

Get up and running in seconds:

# 💻 Installation
pip install coralnet-toolbox

# 🚀 Launch
coralnet-toolbox

🎉 That's it! The toolbox will open and you're ready to start annotating!

For a complete installation guide (including CUDA setup), see the Installation Documentation.

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📚 Documentation Hub

📖 Guide	🎯 Purpose	🔗 Link
Overview	Get the big picture	📋 Read More
Installation	Detailed setup instructions	⚙️ Setup Guide
Usage	Learn the tools	🛠️ User Manual
Hot Keys	Keyboard shortcuts	⌨️ Shortcuts
Classification	Community tutorial	🧠 AI Tutorial

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🎥 Video Demonstrations

📺 Watch the Complete Tutorial Series

🎬 Complete playlist covering all major features and workflows

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🤖 AI Model Arsenal

The toolbox integrates state-of-the-art models for efficient annotation workflows:

🏋️ Trainable Models

YOLO Family	Versions Available
🦾 Legacy	YOLOv3 • YOLOv4 • YOLOv5
🚀 Modern	YOLOv6 • YOLOv7 • YOLOv8
⚡ Latest	YOLOv9 • YOLOv10 • YOLO11 • YOLO12

Powered by the Ultralytics ecosystem

🎯 Segment Anything Models

Model	Specialty	Use Case
🪸 SAM	General segmentation	High-quality masks
🌊 CoralSCOP	Coral-specific	Marine biology focus
⚡ FastSAM	Speed optimized	Real-time annotation
📱 MobileSAM	Mobile-friendly	Edge deployment
✂️ EdgeSAM	Efficient	Resource-constrained
🔍 RepViT-SAM	Vision transformers	Advanced features

Powered by our xSAM integration

👁️ Visual Prompting & Foundation Models

Framework	Models	Capability
YOLOE	See Anything	Visual prompt detection
Transformers	Grounding DINO • OWLViT • OmDetTurbo	Zero-shot detection

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🛠️ Feature Showcase

📝 Core Annotation Tools

🎯 Patch Annotation	📐 Rectangle Annotation	🔷 Multi-Polygon Annotation

🤖 AI-Powered Analysis

🧠 Image Classification	🎯 Object Detection	🎭 Instance Segmentation

🔬 Advanced Capabilities

🪸 Segment Anything (SAM)	🔍 Polygon Classification	📍 Region-based Detection

✂️ Editing & Processing Tools

✂️ Cut	🔗 Combine	🎨 Simplify

🌟 Specialized Features

👁️ See Anything (YOLOE)	🗺️ LAI Classification

📊 Analysis & Exploration

🎬 Video Inference & Analytics	🔍 Data Explorer & Clustering

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🔧 Complete Workflow Pipeline

📥 Data Input

• 🔥 CoralNet Download: Retrieve source data and annotations
• 🎬 Video Processing: Extract frames from video files
• 📸 Image Import: Support for various image formats

✏️ Annotation & Labeling

• 👆 Manual Annotation: Intuitive point, rectangle, polygon and semantic tools
• 🤖 AI-Assisted: SAM, YOLO, and visual prompting models
• 📐 Precision Editing: Cut, combine, subtract, and simplify shapes

🧠 Machine Learning

• 🔬 Hyperparameter Tuning: Optimize training conditions
• 🚀 Model Training: Build custom classifiers and detectors
• ⚡ Model Optimization: Production-ready deployment

📊 Analysis & Export

• 📈 Performance Evaluation: Comprehensive model metrics
• 🎯 Batch Inference: Process multiple images automatically
• 🎥 Video Analysis: Real-time processing with analytics
• 📋 Multi-format Export: CoralNet, Viscore, TagLab, GeoJSON

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📋 Roadmap

See the current tickets and planned features on the GitHub Issues Page

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💻 Installation Guide

🐍 Step 1: Environment Setup

# Create a dedicated environment (recommended)
conda create --name coralnet10 python=3.10 -y
conda activate coralnet10

⚡ Step 2: Fast Installation with UV

# Install UV for faster package management
pip install uv

# Install CoralNet-Toolbox
uv pip install coralnet-toolbox

Fallback: If UV fails, use regular pip: pip install coralnet-toolbox

🚀 Step 3: GPU Acceleration (Optional)

For CUDA-enabled systems:

# Example for CUDA 12.9
# Install PyTorch with CUDA support
uv pip install torch torchvision --index-url https://download.pytorch.org/whl/cu129 --upgrade

🏃‍♂️ Step 4: Launch

coralnet-toolbox

🎯 GPU Status Indicators

• 🐢 CPU only
• 🐇 Single GPU
• 🚀 Multiple GPUs
• 🍎 Mac Metal (Apple Silicon)

Click the icon in the bottom-left to see available devices

🔄 Upgrading

# When updates are available
uv pip install -U coralnet-toolbox==[latest_version]

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🏗️ Repository Structure

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🌊 Success Stories

Using CoralNet-Toolbox in your research?

We'd love to feature your work! Share your success stories to help others learn and get inspired.

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🌍 About CoralNet

🪸 Protecting our oceans, one annotation at a time 🪸

Coral reefs are among Earth's most biodiverse ecosystems, supporting marine life and coastal communities worldwide. However, they face unprecedented threats from climate change, pollution, and human activities.

CoralNet is a revolutionary platform enabling researchers to:

• Upload and analyze coral reef photographs
• Create detailed species annotations
• Build AI-powered classification models
• Collaborate with the global research community

The CoralNet-Toolbox extends this mission by providing advanced AI tools that accelerate research and improve annotation quality.

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📄 Citation

If you use CoralNet-Toolbox in your research, please cite:

@misc{CoralNet-Toolbox,
  author = {Pierce, Jordan and Battista, Tim and Kuester, Falko},
  title = {CoralNet-Toolbox: Tools for Annotating and Developing Machine Learning Models for Benthic Imagery},
  year = {2025},
  howpublished = {\url{https://github.com/Jordan-Pierce/CoralNet-Toolbox}},
  note = {GitHub repository}
}

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⚖️ Legal & Licensing

⚠️ Disclaimer

This is a scientific product and not official communication of NOAA or the US Department of Commerce. All code is provided 'as is' - users assume responsibility for its use.

📋 License

Software created by US Government employees is not subject to copyright in the United States (17 U.S.C. §105). The Department of Commerce reserves rights to seek copyright protection in other countries.

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🌊 Built with ❤️ for coral reef conservation 🌊

Empowering researchers • Protecting ecosystems • Advancing science