Abstract The promising results of deep learning in image recognition suggest a huge potential for microscopic analyses in materials science. One major challenge for its adoption in the study of materials is the limited number of images that are available to train models on. Herein, we present a methodology to create accurate image recognition models with small datasets. By explicitly taking into account the magnification and by introducing appropriate transformations, we incorporate as many insights from material science in the model as possible. This allows for a highly data-efficient training of complex deep learning models. Our results indicate that a model trained with the presented methodology is able to outperform human experts.

中文翻译：

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与机器赛跑：深度学习能否像训练有素的人眼一样识别微观结构？

摘要 深度学习在图像识别中的有希望的结果表明材料科学中微观分析的巨大潜力。在材料研究中采用它的一项主要挑战是可用于训练模型的图像数量有限。在此，我们提出了一种使用小数据集创建准确图像识别模型的方法。通过明确考虑放大倍数并引入适当的转换，我们将尽可能多的材料科学见解纳入模型。这允许对复杂的深度学习模型进行高度数据高效的训练。我们的结果表明，用所提出的方法训练的模型能够胜过人类专家。