MCP YOLOE: Zero-Shot Object Detection & Segmentation

@rjn32s

代码仓库

基本信息

分类

其他

传输方式

stdio

作者

rjn32s

提交者

Rajan shukla

关于 MCP YOLOE: Zero-Shot Object Detection & Segmentation

Provide your AI agents with "eyes." This server enables open-vocabulary object detection and instance segmentation using naturally phrased text prompts (e.g., "detect the laptop next to the coffee").

#vision #ai#object#detection#segmentation#zero-shot#yolo

MCP YOLOE: Zero-Shot Object Detection & Segmentation 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-yolo": {
      "command": "uvx",
      "args": [
        "mcp-yolo"
      ]
    }
  }
}

MCP YOLOE: Zero-Shot Object Detection & Segmentation 工具

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当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

What is MCP YOLOE: Zero-Shot Object Detection & Segmentation?

MCP YOLOE is a Model Context Protocol server that gives AI agents advanced computer vision using zero-shot learning. It detects and segments any object described in natural language, unlike fixed-list YOLO models.

How to use MCP YOLOE: Zero-Shot Object Detection & Segmentation?

You interact through an AI agent (e.g., Claude) by providing a natural language prompt such as “Find the ‘vintage typewriter’ in this image and give me its exact coordinates.” The server accepts local file paths, remote image URLs, and Base64-encoded images as input.

Key features of MCP YOLOE: Zero-Shot Object Detection & Segmentation

Zero-shot detection with natural language prompts.
Precision segmentation with exact polygon masks.
Flexible inputs: local paths, URLs, Base64.
Agent-first design for Claude, IDEs, and workspace agents.
Uses YOLOE26‑L architecture (55.0 mAP, ~6.2ms on T4).

Use cases of MCP YOLOE: Zero-Shot Object Detection & Segmentation

Find and locate arbitrary objects in images using descriptive text.
Obtain exact polygon masks for detected objects for further processing.
Integrate computer vision capabilities into AI agents for automated image analysis.
Support diverse input sources (local files, URLs, embedded data) in agent workflows.

常见问题

How is this different from traditional YOLO models?

Traditional YOLO only detects a fixed list of objects, while this server uses zero-shot learning to detect anything described in natural language.

What is the underlying model and its performance?

It uses the state-of-the-art YOLOE26‑L architecture, achieving high precision (55.0 mAP) and rapid inference (~6.2ms on T4 GPUs).

What input formats can I use?

The server works with local file paths, remote image URLs, and Base64-encoded strings.

Is it designed for agent integration?

Yes, it is specifically built for integration with Claude, IDEs, and autonomous workspace agents.

Does it require special runtime or dependencies?

The README does not specify runtime requirements beyond the model architecture (YOLOE26‑L).

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