Image classification with deep neural networks has seen a surge of technological breakthroughs with promising applications in areas such as face recognition, object detection, etc. However, in engineering problems, e.g. high-speed imaging of engine fuel injector sprays, deep neural networks face a fundamental challenge - the availability of adequate and diverse data. Typically, only hundreds or thousands of samples are available for training. In addition, the transition between different spray classes is a "continuum" and requires a high level of domain expertise to label images accurately. Thus, this work leverages pre-trained Neural Network models to build classifiers and employed Mixup to systematically deal with the data scarcity and ambiguous class boundaries found in industrial spray applications. Comparing to traditional data augmentation methods, Mixup that linear interpolates different classes naturally aligns with the continuous transition between different classes in spray applications. Results also show that Mixup can train a more accurate and robust deep neural network classifier with only hundreds samples