# Git Issue: Dataset Annotation Plan

[openchlai]

Git Issue: Dataset Annotation Plan

User Story

As an ML Engineer, I want to define and document an effective annotation plan so that I can ensure high-quality labeled data for training and evaluation.

Objective

• Establish labeling guidelines for consistent annotations.
• Choose the best annotation method (manual, semi-automated, or automated).
• Evaluate annotation tools (e.g., Label Studio, Doccano).
• Define quality assurance procedures to maintain accuracy.
• Document the annotation process, validation steps, and timelines.

Tasks

📑 Define Annotation Guidelines

• Establish labeling criteria for different types of data.
• Define annotation formats (structured labels, JSON, CSV, etc.).

🛠️ Select Annotation Methods

• Research and compare manual, semi-automated, and automated annotation techniques.
• Choose the most suitable annotation approach based on dataset size and complexity.

🔍 Identify Annotation Tools

• Evaluate Label Studio, Doccano, and other annotation tools.
• Compare tools based on features, ease of use, and integration options.
• Select the best tool for efficient data annotation.

✅ Quality Assurance

• Develop validation procedures to ensure annotation accuracy.
• Define a review and approval process for annotated data.

📝 Documentation

• Record the annotation process, methodologies, and tool selection.
• Document validation steps, QA procedures, and project timelines.
• Generate an Annotation Plan summarizing key findings and recommendations.

✅ Acceptance Criteria

• A clear set of annotation guidelines is documented.
• The best annotation method (manual/semi-auto/auto) is selected.
• The most suitable annotation tool is chosen and justified.
• A quality assurance plan for annotation accuracy is established.
• A comprehensive Annotation Plan is finalized and documented.

📍 Milestone

✅ Annotation Guidelines and Tool Evaluation Complete

[wanyamabitz]

https://github.com/midas-research/audino