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dataset-loader

Construct dataset loaders and transforms for image datasets in OpenWorld-AI-Image-Detection.

Updated Feb 18, 2026
Search Tags:#manifests#env-vars#data-parsing#dataset-loading#dataset-loader#image-datasets
One-click install
npx skills add https://github.com/rilical/OpenWorld-AI-Image-Detection --skill dataset-loader
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dataset-loader
Source: https://github.com/rilical/OpenWorld-AI-Image-Detection/tree/main/.agents/skills/dataset-loader
Command: npx skills add https://github.com/rilical/OpenWorld-AI-Image-Detection --skill dataset-loader

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

dataset-loader provides a standardized approach to add or update dataset loaders, transforms, split manifests, sample-id policies, and env-var based dataset access for image datasets such as CommunityForensics-Small, VCT2, RAID, and ARIA.

Core Features & Use Cases

  • Centralizes dataset parsing in src/owaid/data/ and returns a shared schema: image, label, meta.
  • Keeps loader interfaces stable across train and eval, with actionable error messages for missing env vars or unexpected paths.
  • Use Case: Add a new dataset by implementing a parser under src/owaid/data/ that returns the standardized schema so downstream components can consume it consistently.

Quick Start

Create a new dataset loader under src/owaid/data/, implement parsing to return image, label, meta, and ensure stable IDs for caches and manifests.

Frequently Asked Questions about dataset-loader

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

FAQPage Schema
How do I standardize image dataset loading for training and evaluation pipelines?▼

To standardize image dataset loading, implement a parser under src/owaid/data/ that returns a shared schema of image, label, and meta. This ensures consistent interfaces and actionable error messages for downstream training and evaluation pipelines.

What is the best way to add a new image dataset loader to an existing detection framework?▼

The best way to add a new image dataset loader is to implement parsing in src/owaid/data/ that returns the standardized image, label, and meta schema. Ensure stable sample IDs for caches and manifests to maintain consistency across pipelines.

How do I manage environment-variable access for datasets like CommunityForensics-Small or RAID?▼

You manage environment-variable access by centralizing dataset parsing in src/owaid/data/. The loader validates env vars and provides actionable error messages for missing variables or unexpected paths during dataset loading.

Why does my dataset loader return unexpected paths or missing environment variable errors?▼

Dataset loaders return missing environment variable errors when required env vars are not set for datasets like VCT2 or ARIA. The centralized parser in src/owaid/data/ validates these variables and provides actionable error messages for unexpected paths.

Do I need to update manifests and sample IDs when updating existing dataset loaders?▼

Yes, when updating existing dataset loaders, you must ensure stable sample IDs for caches and manifests. Centralizing parsing in src/owaid/data/ maintains consistent interfaces and manages split manifests for the image datasets.

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