Overhead-MNIST: Machine Learning Baselines for Image Classification

• URL: http://arxiv.org/abs/2107.00436v1
• Date: Thu, 1 Jul 2021 13:30:39 GMT
• Title: Overhead-MNIST: Machine Learning Baselines for Image Classification
• Authors: Erik Larsen, David Noever, Korey MacVittie and John Lilly
• Abstract summary: Twenty-three machine learning algorithms were trained then scored to establish baseline comparison metrics. The Overhead-MNIST dataset is a collection of satellite images similar in style to the ubiquitous MNIST hand-written digits. We present results for the overall best performing algorithm as a baseline for edge deployability and future performance improvement.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Twenty-three machine learning algorithms were trained then scored to establish baseline comparison metrics and to select an image classification algorithm worthy of embedding into mission-critical satellite imaging systems. The Overhead-MNIST dataset is a collection of satellite images similar in style to the ubiquitous MNIST hand-written digits found in the machine learning literature. The CatBoost classifier, Light Gradient Boosting Machine, and Extreme Gradient Boosting models produced the highest accuracies, Areas Under the Curve (AUC), and F1 scores in a PyCaret general comparison. Separate evaluations showed that a deep convolutional architecture was the most promising. We present results for the overall best performing algorithm as a baseline for edge deployability and future performance improvement: a convolutional neural network (CNN) scoring 0.965 categorical accuracy on unseen test data.

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