  This work presents twelve fine-tuned deep learning architectures to solve the
bacterial classification problem over the Digital Image of Bacterial Species
Dataset. The base architectures were mainly published as mobile or efficient
solutions to the ImageNet challenge, and all experiments presented in this work
consisted of making several modifications to the original designs, in order to
make them able to solve the bacterial classification problem by using
fine-tuning and transfer learning techniques. This work also proposes a novel
data augmentation technique for this dataset, which is based on the idea of
artificial zooming, strongly increasing the performance of every tested
architecture, even doubling it in some cases. In order to get robust and
complete evaluations, all experiments were performed with 10-fold
cross-validation and evaluated with five different metrics: top-1 and top-5
accuracy, precision, recall, and F1 score. This paper presents a complete
comparison of the twelve different architectures, cross-validated with the
original and the augmented version of the dataset, the results are also
compared with several literature methods. Overall, eight of the eleven
architectures surpassed the 0.95 scores in top-1 accuracy with our data
augmentation method, being 0.9738 the highest top-1 accuracy. The impact of the
data augmentation technique is reported with relative improvement scores.
