yoloe-local

Name: yoloe-local
Availability: InStock
Author: EurecaMoment

Run a local YOLOE service for image annotation with text, visual, or prompt-free detection and segmentation.

Updated May 7, 2026

Search Tags:#detection #segmentation #image annotation #local service #yoloe

Ready to run

npx skills add https://github.com/EurecaMoment/BenchClaw --skill yoloe-local

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: yoloe-local
Source: https://github.com/EurecaMoment/BenchClaw/tree/main/BenchClaw/annotation-tool/yoloe
Command: npx skills add https://github.com/EurecaMoment/BenchClaw --skill yoloe-local

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires yoloe, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a local service for the YOLOE annotation tool, enabling users to perform open-vocabulary detection or segmentation tasks without the need to reload the model frequently.

Core Features & Use Cases

Local YOLOE Service: Exposes the YOLOE deployment as a reusable localhost service for annotation tasks.
Detection/Segmentation: Supports text-prompt, visual-prompt, and prompt-free inference for open-vocabulary detection or segmentation.
Use Case: Use this Skill to annotate objects in images with YOLOE, or generate pseudo-labels for training data.

Quick Start

Run the 'yoloe-local' skill to start the local YOLOE service.

Frequently Asked Questions about yoloe-local

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I run local image annotation for open-vocabulary detection without reloading the model?▼

Local image annotation for open-vocabulary detection without reloading the model is achieved by using the YOLOE local service to expose a reusable localhost endpoint for continuous annotation tasks. It supports text, visual, and prompt-free inference.

Does YOLOE support text-prompt, visual-prompt, and prompt-free segmentation?▼

Yes, YOLOE supports text-prompt, visual-prompt, and prompt-free inference for open-vocabulary detection and segmentation tasks. This allows flexible annotation workflows by leveraging various prompt types to identify and segment objects in local images.

What's the best way to generate pseudo-labels for training data using a local service?▼

The best way to generate pseudo-labels for training data using a local service is running the YOLOE annotation tool. It deploys a localhost service to continuously process images and output detection or segmentation annotations for your datasets.

Do I need Python libraries and the YOLOE model to start the local annotation service?▼

Yes, you need the YOLOE model and Python libraries to start the local annotation service. These dependencies are required for service management and to successfully execute the open-vocabulary detection and segmentation tasks locally.

Can I use YOLOE for image annotation if I want a reusable localhost service?▼

Yes, you can use YOLOE for image annotation if you want a reusable localhost service. The tool specifically exposes its deployment as a local service, allowing you to perform repeated detection and segmentation tasks efficiently.

What are the limitations of using a prompt-free approach for image segmentation?▼

The metadata does not specify the technical limitations of using a prompt-free approach for image segmentation. It confirms YOLOE supports prompt-free inference alongside text and visual prompts, but provides no constraint details for this specific mode.