mcp-server-cvdlt
3
This project is a Python server implementing the Model Context Protocol (MCP) for advanced image processing tasks such as object detection, segmentation, and pose estimation. It utilizes well-known models like YOLOv10 and YOLOv8 for these capabilities, offering support for both local and network image inputs and integration with client interactions.
MCP Server for CVDLT (Computer Vision & Deep Learning Tools)
- Based on Ultralytics and Model Context Protocol Python SDK.
- Implements image object detection, segmentation, and pose estimation.
- Supports YOLOv10 for object detection, YOLOv8 for segmentation, and Ultralytics SAM for full image segmentation.
- Features local and network image inputs, MCP tool integration, stdio, and SSE transport protocols.
- Planned features include 3D detection, AIGC, Denso estimation, and DL model deployment.
Features
- Detect objects using YOLOv10.
- Segment objects using YOLOv8.
- Segment entire images using Ultralytics SAM.
- Estimate human poses using YOLOv8.
TODO
- 3D Detection
- AIGC (GAN, Diffusion)
- Denso Estimation
- Deploy DL Models