annotex 2.0.6

Annotation Tool for Computer Vision Datasets

Annotex 🚀

AI-Powered Annotation Tool for Computer Vision Datasets

Annotex is a professional-grade annotation tool designed for creating high-quality computer vision datasets. With AI-powered assistance and an intuitive interface, it streamlines the annotation process for machine learning practitioners.

✨ Features

🎯 Professional Annotation Tools

• Rectangle Annotation - Precise bounding box creation
• AI-Assisted Annotation - Auto-annotation with pre-trained YOLO models
• Batch Processing - Process multiple images simultaneously

🔧 Advanced Workflow

• Project Management - Save/load projects (.anno format)
• Class Management - Dynamic class creation with custom colors
• Export Formats - YOLO11, YOLOv8, compatible
• Quality Control - Confidence scoring and validation

🚀 Performance Optimized

• Memory Efficient - Handles large datasets smoothly
• Real-time Preview - Instant annotation feedback
• Keyboard Shortcuts - Professional workflow acceleration

📦 Installation

Quick Install

pip install annotex

🚀 Quick Start

Launch Annotex

# Start the GUI
annotex

# Or
python -m annotex.main

# Load a project
annotex --project my_project.anno

# Load images from directory
annotex --images /path/to/images

Basic Workflow

1. Load Images - Import your image dataset
2. Create Classes - Define annotation classes
3. Annotate - Create bounding boxes manually or with AI
4. Export - Generate YOLO-format dataset

📚 Documentation

Keyboard Shortcuts

Shortcut	Action
Ctrl+N	New Project
Ctrl+O	Load Project
Ctrl+S	Save Current
Ctrl+E	Export Dataset
R	Rectangle Tool
Delete	Delete Selected
Ctrl+Z	Undo

🎨 Interface Overview

Main Components

• Tools Panel - Annotation tools and class management
• Image Viewer - Zoomable canvas with annotation overlay
• Image List - Project image management
• Export Panel - Dataset export configuration

Advanced Features

• Semi-Automated Annotation - AI model integration
• Batch Processing - Multi-image operations
• Quality Metrics - Annotation statistics and validation
• Custom Export - Flexible dataset formats (Currently:only YOLO formats)

🤖 AI Integration

Supported Models

• YOLO11 - Proven performance
• Custom Models - Load your own trained models

Auto-Annotation Workflow

1. Load pre-trained model
2. Set confidence threshold
3. Run individual or batch annotation
4. Review and refine results
5. Export final dataset

🏗️ Roadmap

Version 2.2 (Coming Soon)

• Polygon annotation tool
• Point annotation support
• Brush/segmentation tool
• COCO format export
• Pascal VOC format support

Version 2.3 (Planned)

• Cloud storage integration
• Team collaboration features
• Advanced AI suggestions
• Mobile app companion

📞 Support

• 📧 Email: [e-mail]

🌟 Acknowledgments

• Ultralytics for YOLO implementation
• OpenCV for computer vision tools
• Tkinter for GUI framework

---

⭐ Star us on GitHub if Annotex helps your projects!

Made by Randika