Practical tools built around real computer vision research workflows — from checking datasets before training to analysing experiments and preparing publication-ready results.
Each tool addresses a specific part of the computer vision workflow rather than wrapping general-purpose AI behind another interface.
Dataset checks, experimental settings, comparisons, seeds, exports, and supporting evidence are structured so results can be inspected, repeated, and reported clearly.
Current tools perform their core work in the browser, reducing unnecessary data transfer and keeping research files under the user's control.
Reports, tables, figures, structured exports, and cleaned datasets are designed to move directly into research, engineering, and publication workflows.
Validate computer vision datasets before training. Identify corrupted images, annotation errors, duplicate samples, potential data leakage, class imbalance, and object-size issues. Includes visual inspection, dataset conversion, balanced splitting, and exportable reports.
Organize, analyze, and compare computer vision experiments. Evaluate ablation studies, model efficiency, and experimental consistency. Aggregate repeated runs and generate publication-ready tables and figures.
Explore the current toolkit, or continue into OcculinsLab projects, technical resources, and research-driven computer vision work.