Train and Deploy an Image Classifier for Disaster Response
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.
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Methods
1x1 Convolution •
AlexNet •
Average Pooling •
Batch Normalization •
Bottleneck Residual Block •
Concatenated Skip Connection •
Convolution •
Dense Block •
Dense Connections •
DenseNet •
Depthwise Convolution •
Depthwise Separable Convolution •
Dropout •
Global Average Pooling •
Grouped Convolution •
Inverted Residual Block •
Kaiming Initialization •
Local Response Normalization •
Max Pooling •
MobileNetV2 •
Pointwise Convolution •
ReLU •
Residual Block •
Residual Connection •
ResNet •
Softmax
