YOLO ML Utils - Krish Bakshi

Overview

YOLO ML Utils is a modular collection of helper scripts and utilities built to streamline end-to-end YOLO computer vision workflows.
It focuses on eliminating repetitive boilerplate involved in dataset handling, annotation management, visualization, and training/debug cycles.

The toolkit is designed for rapid experimentation, cleaner pipelines, and production-friendly workflows, especially when working with custom datasets and iterative model training.

Why This Project

Working with YOLO models often involves:

Repeated dataset restructuring
Manual annotation sanity checks
Debugging incorrect bounding boxes or masks
Writing ad-hoc scripts for visualization and validation

This repository consolidates those recurring tasks into reusable, consistent utilities, enabling faster iteration and fewer data-related training failures.

Key Capabilities

Dataset Utilities
- Dataset restructuring and format normalization for YOLO training
- Train/validation/test split handling
- File integrity and consistency checks
Annotation Handling
- Parsing and validating YOLO annotation files
- Coordinate normalization and conversion helpers
- Detection of corrupted or misaligned labels
Visualization & Debugging
- Bounding box and annotation overlays on images
- Visual inspection tools to catch labeling errors early
- Lightweight OpenCV-based rendering for fast checks
Training Support
- Utilities to assist during training and evaluation cycles
- Debug helpers for common YOLO data-related issues
- Designed to plug into existing YOLO pipelines with minimal setup

Design Philosophy

Utility-first: Small, focused scripts that do one job well
Composable: Functions can be chained into larger pipelines
Framework-agnostic: Compatible with Ultralytics YOLO and custom training loops
Production-aware: Built from real experimentation and fine-tuning workflows, not toy examples

Use Cases

Rapid prototyping of custom YOLO datasets
Debugging bounding box or annotation issues before long training runs
Standardizing dataset pipelines across multiple experiments
Supporting research, internships, and production ML vision projects

Status

The repository is actively usable and extensible, with utilities added as new YOLO-related needs arise during experimentation and model development.