  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.
