Semi-Supervised Noisy Student Pre-training on EfficientNet Architectures
for Plant Pathology Classification
- URL: http://arxiv.org/abs/2012.00332v1
- Date: Tue, 1 Dec 2020 08:34:03 GMT
- Title: Semi-Supervised Noisy Student Pre-training on EfficientNet Architectures
for Plant Pathology Classification
- Authors: Sedrick Scott Keh
- Abstract summary: In this report, we investigate the problem of pathology classification using images of a single leaf.
We explore the use of standard benchmark models such as VGG16, ResNet101, and DenseNet 161 to achieve a 0.945 score on the task.
We introduce the state-of-the-art idea of semi-supervised Noisy Student training to the EfficientNet, resulting in significant improvements in both accuracy and convergence rate.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In recent years, deep learning has vastly improved the identification and
diagnosis of various diseases in plants. In this report, we investigate the
problem of pathology classification using images of a single leaf. We explore
the use of standard benchmark models such as VGG16, ResNet101, and DenseNet 161
to achieve a 0.945 score on the task. Furthermore, we explore the use of the
newer EfficientNet model, improving the accuracy to 0.962. Finally, we
introduce the state-of-the-art idea of semi-supervised Noisy Student training
to the EfficientNet, resulting in significant improvements in both accuracy and
convergence rate. The final ensembled Noisy Student model performs very well on
the task, achieving a test score of 0.982.
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