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In general, sufficient data is essential for the better performance andgeneralization of deep-learning models. However, lots of limitations(cost,resources, etc.) of data collection leads to lack of enough data in most of theareas. In addition, various domains of each data sources and licenses also leadto difficulties in collection of sufficient data. This situation makes us hardto utilize not only the pre-trained model, but also the external knowledge.Therefore, it is important to leverage small dataset effectively for achievingthe better performance. We applied some techniques in three aspects: data, lossfunction, and prediction to enable training from scratch with less data. Withthese methods, we obtain high accuracy by leveraging ImageNet data whichconsist of only 50 images per class. Furthermore, our model is ranked 4th inVisual Inductive Printers for Data-Effective Computer Vision Challenge.

Data-Efficient Deep Learning Method for Image Classification Using Data Augmentation, Focal Cosine Loss, and Ensemble