A minimal tool to generate and validate datasets.
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Updated
Mar 8, 2026 - Python
A minimal tool to generate and validate datasets.
Offline computer vision dataset auditing CLI tool for validating image datasets before model training.
YoloLint is a tool for automatic validation of dataset structure, annotation files, and image sizes in YOLO projects. It helps you catch typical errors in directory structure, YAML files, annotation files, and now also ensures all your images have the correct size before you start model training.
Image augmentation and YOLO annotation-validation utilities for computer-vision datasets.
Exact full-corpus validator and evidence for the LeRobot RoboTwin dataset-to-Gym action contract.
dataset validity comparison tool
Pinned semantic-integrity audit and source-backed repair slice for LeRobot DROID
Read-only integrity checks for LeRobot v2.1 datasets — catches the silent merge corruption the loader swallows. PASS/WARN/FAIL report, CI-friendly exit codes.
Football data quality auditor for soccer dataset validation, match data QA, duplicate checks and sports data governance.
Python workbench for image annotation QA, train/test splits, and overlay reports
Reference implementation of MGS (Model Gate Standard) — check a computer vision dataset's quality before you train on it. pip install modelgate-mgs
Chiral Narrative Synthesis workspace for Thinker/Tinker LoRA pipelines, semantic fact-checking, telemetry, and reviewer-ready CNS docs.
Full-corpus VLABench action-contract audit and non-mutating gripper-state repair sidecar.
result validation and error alerts
Vet your robot datasets — 37 checks, repair & scoring for LeRobot data. Know it's UNSAFE TO TRAIN before wasting the run. Vets hf:// repos from 82 KB.
Pytest for robot datasets: conformance checks, semantic diffs, JUnit/SARIF, LeRobot v3 and robomimic support.
Veridex is the trust certificate for physical-AI data that tells you, across any format, whether the robot or sensor dataset you're about to train on is clean, correctly synchronized, and traceable to its source, before it silently wrecks your model.
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