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Investigating Classification Learning Curves for Automatically Generated and Labelled Plant Images by Beck, Michael A.; Bidinosti, Christopher P.; Henry, Christopher J.; Ajmani, Manisha is a scholarly article available to read on EtoBox.

What is Investigating Classification Learning Curves for Automatically Generated and Labelled Plant Images about?

In the context of supervised machine learning a learning curve describes how a model's performance on unseen data relates to the amount of samples used to train the model. In this paper we present a dataset of plant images with representatives of crops and weeds common to the Manitoba prairies at different growth stages. We determine the learning curve for a classification task on this data with the ResNet architecture. Our results are in accordance with previous studies and add to the evidence that learning curves are governed by power-law relationships over large scales, applications, and models. We further investigate how label noise and the reduction of trainable parameters impacts the learning curve on this dataset. Both effects lead to the model requiring disproportionally larger training sets to achieve the same classification performance as observed without these effects.

Author

Beck, Michael A.; Bidinosti, Christopher P.; Henry, Christopher J.; Ajmani, Manisha

Published

2022

Language

EN