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.

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

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.

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

Practical lessons on prompt engineering for code-generation datasets used to train LLMs. Patterns and failure modes from real task audits.

Agentic data intelligence tool using LangChain & Pandas for automated dataset cleaning, governance, and quality analysis.

Production-inspired AI engineering project demonstrating LLM function calling, JSON/schema validation, SQL execution, dataset quality assurance, workflow automation, and AI output debugging through an interactive analytics dashboard.

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.

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

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

Lightweight toolkit for multimodal data curation and quality triage

LLM Code Trainer & Dataset Quality Reviewer at Revelo. Prompt engineering, multi-language code review (Python, TS/JS, C, C++). Remote, EN/PT.

Evaluation dataset quality auditor for LLM and RAG applications. Checks golden sets for conflicting labels, duplicate prompts, weak reference answers, ambiguous questions, over-easy examples, and category coverage gaps.

How much labeled data do you actually need to deploy a parking occupancy system at a never-before-seen lot? A supervision study spanning CLIP zero-shot → ResNet-18 few-shot → full supervision on 432k parking space crops, with dataset annotation error discovery. Trained on NVIDIA A100 via IU Big Red 200.