mini-ImageNet was proposed by Matching networks for one-shot learning for few-shot learning evaluation, in an attempt to have a dataset like ImageNet while requiring fewer resources. Similar to the statistics for CIFAR-100-LT with an imbalance factor of 100, we construct a long-tailed variant of mini-ImageNet that features all the 100 classes and an imbalanced training set with $N_1 = 500$ and $N_K = 5$ images. For evaluation, both the validation and test sets are balanced and contain 10K images, 100 samples for each of the 100 categories.