MCP Server for CVDLT(Computer Vision & Deep Learning Tools)

What is MCP Server for CVDLT?

MCP Server for CVDLT is a Python-based server that implements the Model Context Protocol (MCP) for various computer vision tasks, including object detection, segmentation, and pose estimation using deep learning models.

How to use MCP Server for CVDLT?

To use the MCP Server, install the required dependencies, start the server in either stdio or SSE mode, and provide valid image paths or URLs for processing.

Key features of MCP Server for CVDLT?

• Object detection using YOLOv10
• Image segmentation using YOLOv8 and Ultralytics SAM
• Human pose estimation using YOLOv8
• Support for local and network image inputs
• Integration with MCP tools for client interactions

Use cases of MCP Server for CVDLT?

1. Detecting and classifying objects in images for surveillance applications.
2. Segmenting images for autonomous driving systems.
3. Estimating human poses for fitness and health monitoring applications.

FAQ from MCP Server for CVDLT?

• What models are required to run the server?

The server requires model files: yolov10b.pt, yolov8n-seg.pt, yolov8n-pose.pt, and sam_b.pt.

• How do I start the server?

You can start the server using the command python server.py for stdio mode or python server.py sse [port] for SSE mode.

• Is there any documentation available?

Yes, detailed documentation is available on the GitHub repository.