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。
Offline-first edge vision engineering workbench for YOLO data, experiments, benchmarking, and RDK X5/Linux delivery on macOS.
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.
All-in-one AI workbench program for Vision AI model inference, evaluation, benchmarking, optimization & dataset management
Image board browser to search up references for SD models.
Dataset operations and release-governance toolchain for the HeOCR Hebrew OCR/HTR ecosystem.
Waveform Database (WFDB) implementation in pure Rust.
Forensic recovery of undocumented robot action-tensor semantics from trajectories — calibrated equivalence sets, honest abstention, verified converters. C++20 with CUDA and pybind11.
TGC developer: t.me/forget_git
OpenAxiom — A local PySide6 UI Lab annotation MVP and batch-safe YOLO label tool.
Utility tools for dataset preparation and management, including image renaming, CSV to Excel conversion, and AI-powered image labeling.
Automatically translate structured datasets (CSV, JSON, JSONL, TSV, Parquet) using LLMs via Ollama — with caching, parallelism, checkpointing, and retry support.
Some small tools that you might need when cleaning data and creating training sets.大家在进行数据清洗和训练集制造时可能会需要的一些小工具。
Standalone Python scripts for computer vision: image augmentation (rotate, flip, perspective with Pillow/OpenCV), dataset splitting (scikit-learn), and model testing (TensorFlow + matplotlib).
Modular desktop toolkit for image collection curation — dataset prep, quality scoring, deduplication, cropping, and more.
Dataset preprocessing toolkit for YOLO detection and segmentation, including synchronized cropping, augmentation, label inspection and LabelMe conversion.
Browser-local computer-vision annotation examples, validators, converters, and dataset templates for COCO, YOLO, Pascal VOC, and MOT.
Collect, segment and split the Jiangnan University (JNU) bearing dataset across three rotating speeds, with leakage-aware splits and all six cross-speed transfer tasks.
Python CLI tools for XML annotation processing, COCO dataset restructuring and validation, and COCO-to-YOLO conversion.
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