Читать книгу The Digital Agricultural Revolution - Группа авторов - Страница 31

Many computer vision tasks have shown that deep neural networks perform exceptionally well. However, to avoid overfitting, huge data have to be provided to perfectly model the training data. The goal of data augmentation techniques is to artificially increase the quantity of training images. By providing the model with a variety of data, it helps to improve the overall learning method and performance, as well as generalization capability. For small data sets, this augmentation method is critical for training DL models. Some of the popular data augmentation techniques are flipping, rotating, cropping, scaling, translation, Gaussian noise, color casting, and so on.