一个功能完整的 SemanticKITTI 数据集处理工具包，支持点云可视化、语义分割评估和多种数据格式转换。
Bring a folder of images, leave with a trained LoRA. Agent skills (SKILL.md) for quality-gated LoRA training: dataset doctor + safe fixer + one-confirmation training orchestration for SD-Trainer / lora-scripts-next. Anima-first, SD1.5/SDXL/Flux.
Local Python/Tkinter dataset curation tool for LoRA image training.
Computer vision CLI for dataset work: convert annotations, auto-label, caption, classify, OCR, generate images and clean datasets.
Image board browser to search up references for SD models.
A local, browser-based tool for building character LoRA training datasets with AI-generated captions. EDICO turns a folder of character images into a clean, captioned training dataset — no command-line juggling, no manual caption typing.
Offline-first edge vision engineering workbench for YOLO data, experiments, benchmarking, and RDK X5/Linux delivery on macOS.
End-to-end YOLO11 computer vision for Arena Breakout: high-speed Windows capture, AI-assisted annotation, dataset QA, training, evaluation, and real-time inference.
Локальный AI-инструмент для подготовки датасетов изображений и генерации описаний для обучения LoRA
All-in-one AI workbench program for Vision AI model inference, evaluation, benchmarking, optimization & dataset management
Open-source dataset inspection and quality analysis toolkit for ML datasets.
Local-first YOLO workbench for edge AI: dataset inspection, training, evaluation, active learning, export, quantization, inference, and benchmarking via MCP, CLI, and a VS Code Studio.
Dataset operations and release-governance toolchain for the HeOCR Hebrew OCR/HTR ecosystem.
Функции для подготовки датасетов Object Detection в Google Colab
Waveform Database (WFDB) implementation in pure Rust.
第一人称临床配药视频的大规模自动化检索、采集、多维科学质检与量化评估工具集
Episode-aware diff for LeRobot dataset - see what changed between two dataset revisions
YOLO数据集训练一体化工具箱：视频抽帧→标注→训练→半自动预标注→微调→最终划分的一站式GUI工具
Full stack transformer language model reinforcement learning and dataset preparation with an intuitive CLI
Auto-labeling and VLM scene understanding pipeline for driving data — open-vocabulary detection (Grounding DINO), SAM segmentation, traffic-rule-aware scene tagging (Qwen2.5-VL), pseudo-label mAP evaluation and long-tail mining. Designed to fit in 6 GB VRAM.
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