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Nowadays, analysis of Transparent Environmental Microorganism Images (T-EMimages) in the field of computer vision has gradually become a new andinteresting spot. This paper compares different deep learning classificationperformance for the problem that T-EM images are challenging to analyze. Wecrop the T-EM images into 8 * 8 and 224 * 224 pixel patches in the sameproportion and then divide the two different pixel patches into foreground andbackground according to ground truth. We also use four convolutional neuralnetworks and a novel ViT network model to compare the foreground and backgroundclassification experiments. We conclude that ViT performs the worst inclassifying 8 * 8 pixel patches, but it outperforms most convolutional neuralnetworks in classifying 224 * 224 pixel patches.

A Comparison for Patch-level Classification of Deep Learning Methods on  Transparent Environmental Microorganism Images: from Convolutional Neural  Networks to Visual Transformers