Given a short query with keyphrases about a research idea in 2019 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.

Query: I want to train a model for image classification in unsupervised fashion.

Keyphrases: image classification

Dataset description:
Fashion 144K: Fashion 144K is a novel heterogeneous dataset with 144,169 user posts containing diverse image, textual and meta information.
CASIA V2: CASIA V2 is a dataset for forgery classification. It contains 4795 images, 1701 authentic and 3274 forged.
CIFAR-10: The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck).
Fashion-MNIST: Fashion-MNIST is a dataset comprising of 28×28 grayscale images of 70,000 fashion products from 10 categories, with 7,000 images per category. The training set has 60,000 images and the test set has 10,000 images.

Options:- A: Fashion 144K  B: CASIA V2  C: CIFAR-10  D: Fashion-MNIST
CIFAR-10