Image Labeling Tool GitHub

The tool provides a concise mathematical model to estimate labeling workload, duration, and cost for image annotation projects. It targets developers, labeling teams, and managers who require objective capacity planning aligned with GitHub-based workflows.
Conceptually, it treats dataset size, labels per image, labeling speed, and rate as inputs and derives total labels, project duration, and cost. Optional multipliers reflect real-world factors such as image difficulty or batching efficiencies; the model can split costs between in-house and outsourced segments when a fraction is assigned to outsourcing.
Key features include strict input validation, deterministic outputs, and scenario analysis across different rates and speeds. Use cases include budgeting a 100k-image labeling drive, comparing contractor versus internal labeling, and forecasting staffing resources for sprint planning.
Unique value lies in its developer focus, minimal overhead, and GitHub-friendly semantics: it emphasizes reproducible numbers, versionable assumptions, and export-ready results for reports and dashboards.