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default-annotation

Orchestrate VLM, YOLOE, SAM3, and Depth Anything 3 to generate image annotations.

Updated May 7, 2026
Search Tags:#local workflow#semantic segmentation#image annotation#depth mapping#benchmark preparation
One-click install
npx skills add https://github.com/EurecaMoment/BenchClaw --skill default-annotation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: default-annotation
Source: https://github.com/EurecaMoment/BenchClaw/tree/main/BenchClaw/annotation-tool/default-annotation
Command: npx skills add https://github.com/EurecaMoment/BenchClaw --skill default-annotation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires local VLM, local YOLOE, local SAM3, local Depth Anything 3, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the local image annotation process for generating semantic entity masks, depth maps, and entity annotations.

Core Features & Use Cases

  • Semantic Segmentation: Outputs tool-generated semantic entity masks from a folder of images.
  • Depth Mapping: Provides depth maps using Depth Anything 3.
  • Use Case: When preparing for Stage3 in a benchmarking process, use this Skill to annotate images with labels, depth maps, and other annotations.

Quick Start

Annotate the 'images' folder and generate the annotations at 'annotation_output'.

Frequently Asked Questions about default-annotation

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

FAQPage Schema
How do I automate semantic segmentation and depth mapping for local image annotation?▼

To automate local image annotation for semantic segmentation and depth mapping, you can orchestrate VLM, YOLOE, SAM3, and Depth Anything 3 services to generate candidate annotations from a folder of images.

Do I need local VLM and SAM3 services to generate semantic entity masks?▼

Yes, you need local VLM, YOLOE, SAM3, and Depth Anything 3 services running, as this Skill orchestrates these dependencies to output semantic entity masks and depth maps.

What's the best way to prepare semi-supervised image data for benchmark preparation?▼

The best way to prepare semi-supervised data for benchmark preparation is to preprocess images by generating tool-based semantic entity masks and depth maps locally before the final annotation stage.

How does depth mapping work with Depth Anything 3 in local workflows?▼

Depth mapping works by integrating the local Depth Anything 3 service into the workflow, which processes a folder of images to automatically generate depth map outputs.

Can I use this annotation Skill for Stage3 benchmarking processes?▼

Yes, you can use this Skill for Stage3 benchmarking, as it is specifically designed to annotate images with labels, depth maps, and other annotations required for semi-supervised data preparation.

What are the limitations of using local YOLOE and SAM3 for image annotation?▼

The local image annotation process generates candidate annotations using YOLOE and SAM3, meaning the outputs are tool-generated and designed for semi-supervised workflows requiring subsequent human review.

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