wyoloservice-data-prep 0.2.0

Dataset validator and Slack notifier for NeuralForgeAI

🛠️ NeuralForgeAI Data Prep (wyoloservice-data-prep)

A robust CLI utility and Python library for validating YOLO datasets and sending real-time Slack notifications across the NeuralForgeAI ecosystem.

🌟 Features

• Deep YOLO Validation: Scans local directories or dataset.yaml files to verify the existence of train/val subsets, ensuring compatibility with NeuralForgeAI tasks (classify, detect, segment).
• Class Extraction: Automatically parses subdirectories or YAML parameters to count and map dataset classes.
• Slack Integrations: Provides native hooks to notify engineering teams when a dataset passes validation and is ready for model training.
• Data Augmentation Hooks: Prepared stubs for albumentations and opencv to auto-balance and augment datasets before training.

⚙️ Installation

git clone https://github.com/wisrovi/wyoloservice2_data_prep.git
cd wyoloservice2_data_prep
pip install -e .

This installs the library and registers the wyolo-validate command globally.

🚀 Usage

You can use the CLI tool directly to validate any YOLO dataset file:

wyolo-validate --yaml /datasets/AIDIAGNOST/classification/ages_classification/

Inside NeuralForge Containers

This tool is natively designed to be embedded within your Celery worker pipelines or CI/CD actions. It ensures data integrity before launching expensive Optuna hyperparameter sweeps.

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Author: William Rodriguez (Wisrovi)