The high-performance, privacy-focused image annotation platform for precision workflows.
Reticle is a professional-grade image labeling tool designed for speed, accuracy, and developer autonomy. Originally developed in 2025 as a specialized personal tool, it has been open-sourced in 2026 to share its keyboard-optimized precision workflow with the wider engineering community.
Unlike cloud-based alternatives, Reticle runs entirely locally or on your private infrastructure, ensuring your data never leaves your control. Whether you're building computer vision models for healthcare, autonomous systems, or research, Reticle provides the pixel-perfect precision you need.
- ⚡ Blazing Fast Interface: Built with optimized React components and efficient state management for lag-free annotation of high-resolution images.
- 🎹 Keyboard-Optimized Workflow: Extensive hotkeys for class selection, navigation, and saving streamline the labeling process, minimizing rsi-inducing repetitive motions.
- 🔒 Privacy by Design: Zero external dependencies for data storage. Your images and annotations stay on your machine.
- 📊 Real-time Analytics: Instant feedback on class distribution, box counts, and progress tracking to ensure balanced datasets.
- 🛠️ Extensible Backend: Powered by FastAPI, the backend is easily customizable to integrate with your existing ML pipelines or storage solutions.
- 🧠 Smart Assist Ready: Plug-and-play architecture for connecting custom detection models (YOLO, etc.) to pre-label images.
We provide automated scripts to handle installation and startup for you.
1. Installation Run the script for your OS to set up the environment and install dependencies:
- Windows:
installation scripts/windows_installation.bat - Mac:
installation scripts/mac_installation.sh - Linux:
installation scripts/linux_installation.sh
2. Start Application Launch both backend and frontend with a single command:
- Windows:
start scripts/windows_start.bat - Mac:
start scripts/mac_start.sh - Linux:
start scripts/linux_start.sh
- Python 3.8+
- Node.js 16+
cd backend
pip install -r requirements.txt
uvicorn app.main:app --reloadThe API will be available at http://localhost:8000.
cd frontend
npm install
npm run devOpen http://localhost:5173 to start labeling.
Reticle follows a modern, decoupled architecture designed for scalability and maintainability.
- Frontend: React 18 (Vite), CSS Modules for scoped styling, Custom Canvas engine for high-performance rendering.
- Backend: FastAPI (Python) for asynchronous request handling and efficient file I/O.
- Data Layer: Local file system based storage (configurable) for maximum portability and ease of backup.
- SAM Integration: Segment Anything Model support for one-click object segmentation.
- Active Learning Loop: Intelligent sampling to prioritize the most informative images for labeling.
- Model Distillation: Built-in utilities to compress bulky teacher models into efficient edge-ready detectors.
- Dataset Health Checks: Automated detection of label noise, outliers, and blurry images.
We welcome contributions from the community! Please see our Contributing Guide for details on how to submit pull requests, report issues, and shape the future of Reticle.
This project is licensed under the MIT License - see the LICENSE file for details.
Maintained by Rahul Sangamker. © 2025-2026