ODNET:用于印度食品分类的优化 DenseNet

IF 1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Jigar A. Patel, Hardik N. Talsania, Kirit Modi
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引用次数: 0

摘要

食品图像分类与识别在医学和保健领域的重要性日益增加,在学术研究中日益突出。食物图像分类的应用有可能以各种方式提高整体的食物体验。本研究针对迁移学习提出了优化的DenseNet架构。实验结果表明,优化后的DenseNet模型训练准确率为98.7%,测试准确率为95.10%,在直接对比中优于替代模型MobileNetv3。MobileNetV3在印度食品图像数据集上的训练准确率为98%,测试准确率为92.39%。它显示了印度食品图像数据集的最佳模型是优化的DenseNet,系统的性能超过了最先进的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ODNET: Optimized DenseNet for Indian food classification
The field of food image classification and recognition is gaining prominence in academic research, primarily driven by its increasing significance in the domains of medicine and healthcare. The application of food image classification has the potential to enhance overall food experiences in various ways. In this study, optimized DenseNet architecture proposed for transfer learning. The experimental findings demonstrate that the optimized DenseNet model, accuracy rate of Training is 98.7% and testing is 95.10%, surpassing the performance of alternative model MobileNetv3 in direct comparison. Accuracy of MobileNetV3 on Indian food image dataset is 98% on training and 92.39% testing. It shows best model for Indian food image dataset is optimized DenseNet and performance of the system surpasses state of the art methods.
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