  In recent years, deep learning has made brilliant achievements in
Environmental Microorganism (EM) image classification. However, image
classification of small EM datasets has still not obtained good research
results. Therefore, researchers need to spend a lot of time searching for
models with good classification performance and suitable for the current
equipment working environment. To provide reliable references for researchers,
we conduct a series of comparison experiments on 21 deep learning models. The
experiment includes direct classification, imbalanced training, and
hyperparameter tuning experiments. During the experiments, we find
complementarities among the 21 models, which is the basis for feature fusion
related experiments. We also find that the data augmentation method of
geometric deformation is difficult to improve the performance of VTs (ViT,
DeiT, BotNet and T2T-ViT) series models. In terms of model performance,
Xception has the best classification performance, the ViT model consumes the
least time for training, and the ShuffleNet-V2 model has the least number of
parameters.
