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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

1

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.

2

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).

3

What input formats can I use?

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

4

Is it designed for agent integration?

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

5

Does it require special runtime or dependencies?

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

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