关于植物病理学分类高效网络架构的半超声声中学生预培训 (Semi-Supervised Noisy Student Pre-training on EfficientNet Architectures for Plant Pathology Classification)

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.

翻译：近年来,深层次的学习极大地改善了对植物中各种疾病的诊断和诊断,在本报告中,我们利用单一叶子的图像调查病理学分类问题,我们探索使用标准基准模型,如VGG16、ResNet101和DenseNet 161,以在这项任务上取得0.945分。此外,我们探索使用新的高效网络模型,将准确性提高到0.962分。最后,我们向高效网络介绍半监督的游牧学生培训的最先进的理念,从而显著提高准确性和趋同率。最后的混合的游牧学生模型在这项工作上表现得非常好,测试得分达到0.982分。