论文标题
基于转移学习的自动模型创建工具用于资源约束设备
Transfer Learning Based Automatic Model Creation Tool For Resource Constraint Devices
论文作者
论文摘要
随着机器学习的增强，正在设计许多工具，以帮助开发人员轻松创建其机器学习模型。在本文中，我们提出了一种新的方法，用于自动创建此类自定义模型，以使用传输学习来自定义模型，而无需编写任何机器学习代码。我们共享使用自动模型创建工具的架构和由IT创建的CNN模型，它使用预验证的模型（例如Yamnet和Mobilenetv2）作为功能提取器。最后，我们通过创建自动图像和音频分类器来证明由工具创建的模型的准确性和内存足迹，并使用Stanford Cars和ESC-50数据集报告我们的实验结果。
With the enhancement of Machine Learning, many tools are being designed to assist developers to easily create their Machine Learning models. In this paper, we propose a novel method for auto creation of such custom models for constraint devices using transfer learning without the need to write any machine learning code. We share the architecture of our automatic model creation tool and the CNN Model created by it using pretrained models such as YAMNet and MobileNetV2 as feature extractors. Finally, we demonstrate accuracy and memory footprint of the model created from the tool by creating an Automatic Image and Audio classifier and report the results of our experiments using Stanford Cars and ESC-50 dataset.
