I.I.D. hypothesis between training and testing data is the basis of numerous image classification methods. Such property can hardly be guaranteed in practice where the Non-IIDness is common, causing instable performances of these models. In literature, however, the Non-I.I.D. image classification problem is largely understudied. A key reason is lacking of a well-designed dataset to support related research. In this paper, we construct and release a Non-I.I.D. image dataset called NICO, which uses contexts to create Non-IIDness consciously. Compared to other datasets, extended analyses prove NICO can support various Non-I.I.D. situations with sufficient flexibility. Meanwhile, we propose a baseline model with ConvNet structure for General Non-I.I.D. image classification, where distribution of testing data is unknown but different from training data. The experimental results demonstrate that NICO can well support the training of ConvNet model from scratch, and a batch balancing module can help ConvNets to perform better in Non-I.I.D. settings.

中文翻译：

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走向非 IID 图像分类：数据集和基线

训练和测试数据之间的 IID 假设是众多图像分类方法的基础。在 Non-IIDness 普遍存在的情况下，这种性质在实践中很难得到保证，导致这些模型的性能不稳定。然而，在文献中，非 IID 图像分类问题在很大程度上没有得到充分研究。一个关键原因是缺乏设计良好的数据集来支持相关研究。在本文中，我们构建并发布了一个名为 NICO 的 Non-IID 图像数据集，它使用上下文有意识地创建了 Non-IIDness。与其他数据集相比，扩展分析证明 NICO 可以以足够的灵活性支持各种 Non-IID 情况。同时，我们提出了一个具有 ConvNet 结构的基线模型，用于一般非 IID 图像分类，其中测试数据的分布未知但与训练数据不同。