论文标题
训练和部署图像分类器进行灾难响应
Train and Deploy an Image Classifier for Disaster Response
论文作者
论文摘要
随着深度学习图像分类每年变得越来越强大，很明显，其对灾难响应的介绍将提高响应者可以使用的效率。使用几种神经网络模型，包括Alexnet，Resnet，Mobilenet，Densenets和4层CNN，我们将洪水灾害图像从大型图像数据中分类为具有高达79％精度的大图像。我们的模型和与数据集合作的教程为其他人创造了基础，以对图像中包含的其他类型的灾难进行分类。
With Deep Learning Image Classification becoming more powerful each year, it is apparent that its introduction to disaster response will increase the efficiency that responders can work with. Using several Neural Network Models, including AlexNet, ResNet, MobileNet, DenseNets, and 4-Layer CNN, we have classified flood disaster images from a large image data set with up to 79% accuracy. Our models and tutorials for working with the data set have created a foundation for others to classify other types of disasters contained in the images.
