Onegin
Custom Computer Vision Models
Objective
Allow users to build custom computer vision models optimized for the performance metrics that matter to them.
Motivation
A self-learning project to better understand machine learning mechanics and how architectural variations affect learning and performance. Named after "Eugene Onegin, A Novel in Verse" — the novel Andrey Markov used to develop Markov Chains, the fundamental underpinnings of modern machine learning.
Overview
Onegin is an intelligent hyperparameter optimization system designed to automatically tune machine learning models for optimal performance. By exploring many hyperparameter combinations and selecting the architecture that maximizes model health and test accuracy, it produces models that perform well not only in testing, but also in real-world production environments.
The system automates the traditionally tedious process of hyperparameter tuning, letting data scientists and ML engineers focus on higher-level architecture decisions while Onegin handles learning rates, batch sizes, regularization, and other critical parameters.
Key Features
Automated Optimization
Intelligent search algorithms explore the hyperparameter space to identify optimal configurations without manual intervention.
Efficient Trial Management
Smart resource allocation and early stopping minimize computational cost while maximizing optimization efficiency.
Real-time Visualization
Interactive dashboards provide insight into the optimization process, trial performance, and parameter importance.
Framework Agnostic
Compatible with PyTorch, TensorFlow, and scikit-learn for easy integration into existing workflows.
How It Works
- Define the search space — continuous, discrete, and categorical parameters and their valid ranges.
- Configure optimization — choose the algorithm, set trial limits, and configure early stopping for your budget.
- Run optimization — Onegin executes trials, learning from results to guide the search toward optimal regions.
- Deploy the best model — review results, analyze parameter importance, and ship the best configuration.