Result: shahabahreini/YOLOmatic: v1.0.0 - YOLOmatic: Automated YOLO Model Training Pipeline with Interactive CLI

Title:
shahabahreini/YOLOmatic: v1.0.0 - YOLOmatic: Automated YOLO Model Training Pipeline with Interactive CLI
Authors:
Publisher Information:
Zenodo
Publication Year:
2024
Collection:
Zenodo
Document Type:
Electronic Resource software
Language:
unknown
DOI:
10.5281/zenodo.14545917
Rights:
Apache License 2.0 ; apache-2.0 ; http://www.apache.org/licenses/LICENSE-2.0
Accession Number:
edsbas.BBAEF36B
Database:
BASE

Further Information

YOLOmatic v1.0.0 - First Official Release Overview YOLOmatic is a comprehensive tool for automating YOLO model training and management with an interactive CLI interface. This release provides a robust foundation for researchers and developers working with various YOLO model versions. Key Features Model Management Support for multiple YOLO versions (v8, v9, v10, v11) Interactive model selection and configuration interface Automated model download and verification system Pre-configured model variants (n, s, m, l, x) support [1] Training Pipeline Automated training workflow with ClearML integration Custom dataset support with COCO format Configurable training parameters and hyperparameters Real-time training monitoring and logging Automated experiment tracking and version control [2] User Interface Interactive CLI with arrow-key navigation Rich formatting for performance metrics display Real-time model comparison tables Status indicators and progress monitoring Intuitive configuration management interface Technical Specifications System Requirements Python 3.8+ CUDA compatible GPU (recommended) 8GB RAM (minimum) 20GB disk space (recommended) Dependencies ultralytics>=8.0.0 clearml>=1.10.0 rich>=13.0.0 blessed>=1.20.0 pyyaml>=6.0.0 Documentation Comprehensive documentation is available in the repository: README.md: Getting started guide and basic usage docs/: Detailed documentation and advanced features examples/: Sample configurations and use cases Installation # Clone the repository git clone https://github.com/shahabahreini/YOLOmatic.git # Install dependencies cd YOLOmatic pip install -r requirements.txt Configuration Basic configuration example: model_settings: version: "YOLOv11" variant: "n" input_size: 640 batch_size: 16 training_settings: epochs: 100 save_period: 10 device: "cuda:0" Performance Features Automated experiment tracking Real-time performance monitoring Comprehensive metrics logging Custom metric visualization support [3] Integration Features ClearML integration for experiment ...