Welcome to Masterful: The Training Platform for Computer Vision Models¶

Intro¶

Masterful is the training platform for computer vision models. Masterful supports most types of classification, detection, and segmentation.

It’s primary objective is training a more accurate model through:

Comprehensive regularization.
Semi-supervised learning (e.g. learning from raw, unlabeled images).

It’s secondary objective is to reduce developer time through high-speed metalearning. This avoids the guessing and checking of hyperparameters or doing long runs with a black box optimizer.

Masterful’s tertiary objective is to train using the minimum compute resources (GPU-hours and wall clock time), which it achieves through high-speed metalearning to discover ideal optimization hyperparameters.

Currently available for Tensorflow2, with PyTorch support coming soon.

Getting Started¶

Install with pip install --upgrade pip; pip install masterful.

For detailed installation instructions, visit the Installation Tutorial.

Then execute your first training run with Masterful with the Quickstart Tutorial.

Finally, visualize Masterful’s performance with the Frontend Tutorial.

Where Masterful Fits¶

Users define the two inputs to Masterful:

Model architecture
Data

Masterful processes these inputs through four modules:

Regularization, which improves accuracy from the existing information
Semi-supervised learning (SSL), which improves accuracy by learning from raw, unlabeled images
Optimization, which minimizes GPU-hours to train
Meta-learning, which minimizes developer “guessing and checking” or long-runs for black box optimization.

Masterful returns a train model (e.g. model weights).

Citing This Work¶

If you use Masterful for academic research, you are encouraged to cite the following technical report:

@article{wookeyhorikert2022masterful,
title={Masterful: A Training Platform for Computer Vision Models},
author={Wookey, Samuel and Ho, Yaoshiang and Rikert, Tom},
year={2022}
}

Documentation¶

The rest of the documentation is organized into the following sections:

Image Classification, Detection, and Segmentation: Code examples to help you get started with the Masterful AutoML platform.
Semi-Supervised Learning: Code examples exploring Semi-Supervised learning using Masterful.
Advanced Topics: Code examples for advanced use cases.
Concepts: The theory and thinking behind Masterful.
API Reference: Documenting the classes, methods, and functions.
FAQ: Frequently Asked Questions
Release Notes: Changes in each release.