SensorMCP Server

A SensorMCP Model Context Protocol (MCP) Server that enables automated dataset creation and custom object detection model training through natural language interactions. This project integrates computer vision capabilities with Large Language Models using the MCP standard.

🌟 About

SensorMCP Server combines the power of foundation models (like GroundedSAM) with custom model training (YOLOv8) to create a seamless workflow for object detection. Using the Model Context Protocol, it enables LLMs to:

• Automatically label images using foundation models

• Create custom object detection datasets

• Train specialized detection models

• Download images from Unsplash for training data

✨ Features

• Foundation Model Integration: Uses GroundedSAM for automatic image labeling

• Custom Model Training: Fine-tune YOLOv8 models on your specific objects

• Image Data Management: Download images from Unsplash or import local images

• Ontology Definition: Define custom object classes through natural language

• MCP Protocol: Native integration with LLM workflows and chat interfaces

• Fixed Data Structure: Organized directory layout for reproducible workflows

🛠️ Installation

Prerequisites

• uv for package management

• Python 3.13+ (uv python install 3.13)

• CUDA-compatible GPU (recommended for training)

Setup

1. Clone the repository:

2. Install dependencies:

3. Set up environment variables (create .env file):

🚀 Usage

Running the MCP Server

For MCP integration (recommended):

For standalone web server:

MCP Configuration

Add to your MCP client configuration:

Available MCP Tools

1. list_available_models() - View supported base and target models

2. define_ontology(objects_list) - Define object classes to detect

3. set_base_model(model_name) - Initialize foundation model for labeling

4. set_target_model(model_name) - Initialize target model for training

5. fetch_unsplash_images(query, max_images) - Download training images

6. import_images_from_folder(folder_path) - Import local images

7. label_images() - Auto-label images using the base model

8. train_model(epochs, device) - Train custom detection model

Example Workflow

Through your MCP-enabled LLM interface:

1. Define what to detect:

   Define ontology for "tiger, elephant, zebra"

2. Set up models:

   Set base model to grounded_sam Set target model to yolov8n.pt

3. Get training data:

   Fetch 50 images from Unsplash for "wildlife animals"

4. Create dataset:

   Label all images using the base model

5. Train custom model:

   Train model for 100 epochs on device 0

📁 Project Structure

🔧 Supported Models

Base Models (for auto-labeling)

• GroundedSAM: Foundation model for object detection and segmentation

Target Models (for training)

• YOLOv8n.pt: Nano - fastest inference

• YOLOv8s.pt: Small - balanced speed/accuracy

• YOLOv8m.pt: Medium - higher accuracy

• YOLOv8l.pt: Large - high accuracy

• YOLOv8x.pt: Extra Large - highest accuracy

🌐 API Integration

Unsplash API

To use image download functionality:

1. Create an account at Unsplash Developers

2. Create a new application

3. Add your access key to the .env file

🛠️ Development

Running Tests

Code Formatting

📋 Requirements

See pyproject.toml for full dependency list. Key dependencies:

• mcp[cli] - Model Context Protocol

• autodistill - Foundation model integration

• torch & torchvision - Deep learning framework

• ultralytics - YOLOv8 implementation

🤝 Contributing

1. Fork the repository

2. Create a feature branch

3. Make your changes

4. Add tests for new functionality

5. Submit a pull request

📖 Citation

If you use this code or data in your research, please cite our paper:

📄 License

This project is licensed under the MIT License.

📧 Contact

For questions about the zoo dataset mentioned in development: Email: yq@anysign.net