dataset-quality

KALOS: Evaluate the quality of computer vision datasets

Systematic quality evaluation suite for AI/ML datasets. 103 ego datasets audited. ISO 5259-2 aligned.

Official repository for paper "Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet"

面向研究、竞赛与论文场景的可追溯数据采集与交付工具

A Python toolkit for cleaner datasets in computer vision.

A Python library and CLI for dataset validation, schema checks, and basic drift signals.

Validate OpenCV checkerboard camera-calibration datasets for blur, coverage, pose diversity, duplicates, and reprojection error.

Safe, auditable YOLO missing-label recovery | 安全可审计的 YOLO 漏标恢复工具

Local-first AI-assisted object detection annotation and quality-control desktop app

Evaluation QA harness for misinformation datasets: stress tests evidence quality, shortcuts, ambiguity, and ranking fragility.

Industrial computer vision workflow for welding defect inspection using YOLO, OpenCV preprocessing, dataset QA, threshold governance, and edge-readiness analysis.

(WIP): 'Aporia' in Greek means 'inconsistent'. A Python library that detects and fixes dataset issues using both rule-based methods and ML models. It evaluates dataset quality across multiple metrics, including missing values, duplicates, outliers, class imbalance, and label consistency. It also suggests fixes based on the metric scores.

A health check for your fine-tuning datasets — diagnoses diversity, balance, and size risks before you waste a training run.

GenProof detects model collapse risk in pre-training datasets before training begins. It combines semantic entropy, tail-density, and AI detection into a composite probability score (ICS). Built with FastAPI and scikit-learn to help ensure data quality and compliance.

Companion code for 'A Two-Stage Provenance and Consistency Audit of Pose-Derived Speed Channels in Public Driving Datasets'

Time-aware dataset forensics and early-warning risk prediction for LLM fine-tuning. Predicts whether a dataset will damage a model — before the fine-tuning damage becomes visible.

The Dataset Quality Scoring Engine (DQS) evaluates the quality of any dataset using automated, model-agnostic metrics. The system processes user-uploaded datasets, computes embeddings, analyzes statistical and semantic properties, and outputs a standardized quality score

CV Dataset Quality Inspector — React-based tool for detecting quality issues in computer vision annotation datasets. Auto-detects bbox errors, visualizes class imbalance, and exports quality reports — built for AV/CV ML pipelines.

Deterministic synthetic contextual-PII corpus generation, validation, auditing, and span-preserving export.

Offline prompt and eval dataset linting for JSONL/CSV quality gates, PII, duplicates, split leakage, reports, and CI.