About MLPractitioners Computer Vision

MLPractitioners Computer Vision is a complete YOLO labeling and training ecosystem designed to make object detection model development fast, easy, and accessible.

What is MLPractitioners Computer Vision?

MLPractitioners Computer Vision is a unified platform for creating high-quality object detection datasets and training state-of-the-art YOLO11 models. It consists of three interconnected platforms that share a common API contract:

Why MLPractitioners Computer Vision?

Purpose-Built for YOLO

Unlike generic labeling tools, MLPractitioners CV is specifically designed for YOLO object detection. All data is stored in YOLO format from the start—no conversion needed.

Mobile-First Design

Our innovative center-to-corner box drawing system makes mobile labeling significantly easier. Designed for thumb operation, it's perfect for on-the-go data collection.

Serverless Training

Deploy once to Modal.com and train unlimited models. Pay only for what you use—no infrastructure management required. Typical training costs just $0.15-$0.50 per job.

Unified API

All platforms follow the same API contract, ensuring complete interoperability. Label on desktop, mobile, or both—your data works everywhere.

Fast & Efficient

From labeling to trained model in minutes, not hours. Streamlined workflow means you spend less time on tooling and more time on your ML projects.

Key Innovations

Center-to-Corner Drawing

Traditional labeling apps require dragging from one corner to the opposite corner. This is difficult on touchscreens and often results in imprecise boxes.

MLPractitioners CV uses center-to-corner drawing:

1. Tap the center of the object
2. Drag to any corner
3. Box expands symmetrically

This approach is:

• ✅ More intuitive—you naturally identify object centers
• ✅ Easier on touchscreens—requires less precision
• ✅ Faster—fewer adjustments needed
• ✅ More accurate—symmetrical expansion from center

Auto-Save Everything

Every action is immediately persisted to disk. No manual save button needed—your work is always safe.

Multi-Project Support

Work on multiple labeling projects simultaneously. Each project maintains its own classes, images, and labels.

Use Cases

Research & Experimentation

Quickly create custom datasets for research projects. Test different model architectures and training strategies without infrastructure overhead.

Production ML Pipelines

Build production-ready object detection models. The API-first design makes it easy to integrate into existing ML workflows.

Education & Learning

Perfect for teaching computer vision and deep learning. Students can experience the complete ML workflow from data collection to model deployment.

Rapid Prototyping

Validate ML ideas quickly. From concept to working prototype in hours, not weeks.

Edge Deployment

Create models optimized for mobile and edge devices. Export to PyTorch, ONNX, or TensorFlow Lite formats.

Technology Stack

Android App

• Kotlin - Native Android development
• CameraX - Modern camera API
• Custom Canvas Views - High-performance drawing
• Gson - JSON serialization

Backend

• Modal.com - Serverless GPU compute
• FastAPI - REST API framework
• Ultralytics YOLO11 - Latest YOLO model training
• PyTorch - Deep learning framework

Getting Started

Ready to start labeling and training? Check out our Quick Start Guide to go from zero to a trained model in 10 minutes!

Open Source

MLPractitioners Computer Vision is an open-source project. We welcome contributions from the community!

• Documentation: Comprehensive guides and API reference
• Bug Reports: Help us improve by reporting issues
• Feature Requests: Suggest new features and improvements
• Pull Requests: Contribute code and documentation

Visit our GitHub repository to get involved!