论文标题
半监督的嘈杂学生对植物病理学分类的有效网络体系结构进行预培训
Semi-Supervised Noisy Student Pre-training on EfficientNet Architectures for Plant Pathology Classification
论文作者
论文摘要
近年来，深度学习已大大改善了植物中各种疾病的识别和诊断。在本报告中，我们使用单叶的图像研究了病理分类的问题。我们探讨了标准基准模型（例如VGG16，Resnet101和Densenet 161）的使用，以在任务上获得0.945分数。此外，我们探索了较新的EfficityNet模型的使用，将精度提高到0.962。最后，我们介绍了半监督嘈杂的学生培训的最先进的想法，从而有效网络，从而显着提高了准确性和收敛速度。最后的结合嘈杂的学生模型在任务上表现出色，测试得分为0.982。
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.
