🎯 RCT Detector Platform - Ultralytics MCP Server

Advanced AI-powered object detection platform with intelligent dataset upload, custom model training, and MCP integration for N8N automation.

A comprehensive Model Context Protocol (MCP) server that seamlessly integrates Ultralytics YOLO models with N8N workflows, providing a complete AI-powered computer vision solution with 10GB dataset upload support and intelligent background processing.

Docker CUDA Streamlit N8N

✨ Key Features

🎯 Core Capabilities

🔬 Advanced AI Detection: YOLO-based object detection and analysis
📦 Smart Dataset Upload: 10GB limit with intelligent ZIP structure detection
🎯 Custom Model Training: Train your own models with any YOLO dataset
🤖 YOLO11 Model Variants: Choose from nano/small/medium/large/x-large base models
⚡ GPU Acceleration: NVIDIA CUDA support for fast training/inference
🌐 Web Interface: Beautiful Streamlit dashboard
📊 Real-time Monitoring: Live GPU stats and training progress
🔌 MCP Integration: Connect with N8N for workflow automation
🛡️ Background Processing: Stable upload handling for large files

Related MCP server: mcp-server-unitycatalog

� Quick Start

One-Command Setup

For Windows users:

setup.bat

For Linux/Mac users:

chmod +x setup.sh ./setup.sh

Manual setup:

docker-compose up --build -d

Access the Platform

🌐 Main Interface: http://localhost:8501
📊 TensorBoard: http://localhost:6006
🔌 MCP Server: http://localhost:8092
📓 Jupyter: http://localhost:8888

� Requirements

Docker & Docker Compose
NVIDIA Docker Runtime (for GPU support)
8GB+ RAM recommended
50GB+ free disk space

🎯 Dataset Upload

Supported ZIP Structures

The platform automatically detects and organizes various ZIP structures:

✅ Structure 1 (Flat): dataset.zip ├── data.yaml ├── images/ │ ├── img1.jpg │ └── img2.jpg └── labels/ ├── img1.txt └── img2.txt ✅ Structure 2 (Nested): dataset.zip └── my_dataset/ ├── data.yaml ├── images/ │ ├── train/ │ └── val/ └── labels/ ├── train/ └── val/ ✅ Structure 3 (Split folders): dataset.zip ├── data.yaml ├── train/ │ ├── images/ │ └── labels/ └── val/ ├── images/ └── labels/

Upload Process

Navigate to Training page
Click Upload Custom Dataset
Select your ZIP file (up to 10GB)
Enter dataset name
Click Upload Dataset
Do NOT refresh during processing
Wait for completion message

🌐 Streamlit UI: http://localhost:8501
📊 TensorBoard: http://localhost:6006
📓 Jupyter Lab: http://localhost:8888
🔗 MCP Server: http://localhost:8092

🎮 Available Services

Service	Port	Description	Status
Streamlit Dashboard	8501	Interactive YOLO model interface	✅ Ready
MCP Server	8092	N8N integration endpoint	✅ Ready
TensorBoard	6006	Training metrics visualization	✅ Ready
Jupyter Lab	8888	Development environment	✅ Ready

🛠️ MCP Tools Available

Our MCP server provides 7 specialized tools for AI workflows:

detect_objects - Real-time object detection in images
train_model - Custom YOLO model training
evaluate_model - Model performance assessment
predict_batch - Batch processing for multiple images
export_model - Model format conversion (ONNX, TensorRT, etc.)
benchmark_model - Performance benchmarking
analyze_dataset - Dataset statistics and validation

🔌 N8N Integration

Connect to N8N using our MCP server:

Server Endpoint: http://localhost:8092
Transport: Server-Sent Events (SSE)
Health Check: http://localhost:8092/health

Example N8N Workflow

{ "mcp_connection": { "transport": "sse", "endpoint": "http://localhost:8092/sse" } }

📁 Project Structure

ultralytics_mcp_server/ ├── 🐳 docker-compose.yml # Orchestration configuration ├── 🔧 Dockerfile.ultralytics # CUDA-enabled Ultralytics container ├── 🔧 Dockerfile.mcp-connector # Node.js MCP server container ├── 📦 src/ │ └── server.js # MCP server implementation ├── 🎨 main_dashboard.py # Streamlit main interface ├── 📄 pages/ # Streamlit multi-page app │ ├── train.py # Model training interface │ └── inference.py # Inference interface ├── ⚡ startup.sh # Container initialization script ├── 📋 .dockerignore # Build optimization └── 📖 README.md # This documentation

🔧 Configuration

Environment Variables

CUDA_VISIBLE_DEVICES - GPU device selection
STREAMLIT_PORT - Streamlit service port (default: 8501)
MCP_PORT - MCP server port (default: 8092)
TENSORBOARD_PORT - TensorBoard port (default: 6006)

Custom Configuration

Edit docker-compose.yml to customize:

Port mappings
Volume mounts
Environment variables
Resource limits

📊 Usage Examples

Object Detection via Streamlit

Navigate to http://localhost:8501
Upload an image or video
Select YOLO model variant and confidence threshold
Run inference and view annotated results

Training Custom Models with YOLO11 Variants

Go to Training page in Streamlit
Upload custom dataset or select built-in datasets
Choose YOLO11 Model Variant:
- yolo11n: Fast training, good for testing (1.9M parameters)
- yolo11s: Balanced performance (9.1M parameters)
- yolo11m: Better accuracy (20.1M parameters)
- yolo11l: High accuracy training (25.3M parameters)
- yolo11x: Maximum accuracy (43.9M parameters)
Configure epochs, batch size, image size
Monitor real-time training progress with live GPU stats
Models automatically save to workspace

Training Custom Models

Access Training page in Streamlit interface
Select YOLO11 Model Variant (nano/small/medium/large/x-large)
Choose your dataset (built-in or custom upload)
Configure training parameters (epochs, batch size, image size)
Click Start Training and monitor progress
Models auto-save to workspace for later use

Model Variant Selection:

yolo11n.pt - Nano: Fastest, lowest accuracy (1.9M params)
yolo11s.pt - Small: Good balance (9.1M params)
yolo11m.pt - Medium: Better accuracy (20.1M params)
yolo11l.pt - Large: High accuracy (25.3M params)
yolo11x.pt - X-Large: Highest accuracy (43.9M params)

N8N Automation

Create N8N workflow
Add MCP connector node
Configure endpoint: http://localhost:8092
Use available tools for automation

🔍 Monitoring & Debugging

Container Status

docker ps docker-compose logs ultralytics-container docker-compose logs mcp-connector-container

Health Checks

# MCP Server curl http://localhost:8092/health # Streamlit curl http://localhost:8501/_stcore/health # TensorBoard curl http://localhost:6006

🔄 Restart & Maintenance

Restart Services

docker-compose restart

Update & Rebuild

docker-compose down docker-compose up --build -d

Clean Reset

docker-compose down docker system prune -f docker-compose up --build -d

🎯 Performance Optimization

GPU Memory: Automatically managed by CUDA runtime
Batch Processing: Optimized for multiple image inference
Model Caching: Pre-loaded models for faster response
Multi-threading: Concurrent request handling

🚨 Troubleshooting

Common Issues

Container Restart Loop

# Check logs docker-compose logs ultralytics-container # Restart with rebuild docker-compose down docker-compose up --build -d

Streamlit Not Loading

# Verify container status docker ps # Check if files are copied correctly docker exec ultralytics-container ls -la /ultralytics/

GPU Not Detected

# Check NVIDIA drivers nvidia-smi # Verify CUDA in container docker exec ultralytics-container nvidia-smi

🔧 Development

Local Development Setup

Clone repository
Install dependencies: npm install (for MCP server)
Set up Python environment for Streamlit
Run services individually for debugging

Adding New MCP Tools

Edit src/server.js
Add tool definition in tools array
Implement handler in handleToolCall
Test with N8N integration

🤝 Contributing

Fork the repository
Create feature branch (git checkout -b feature/amazing-feature)
Commit changes (git commit -m 'Add amazing feature')
Push to branch (git push origin feature/amazing-feature)
Open Pull Request

📄 License

This project is licensed under the AGPL-3.0 License - see the Ultralytics License for details.

🙏 Acknowledgments

Ultralytics - For the amazing YOLO implementation
N8N - For the workflow automation platform
Streamlit - For the beautiful web interface framework
NVIDIA - For CUDA support and GPU acceleration

📞 Support

🐛 Issues: GitHub Issues
💬 Discussions: GitHub Discussions
📧 Contact: Create an issue for support

Made with ❤️ for the AI Community

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