Given a short query with keyphrases about a research idea in 2018 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 generalisable model on a variety of datasets with different data distribution.

Keyphrases: knowledge distillation images videos

Dataset description:
VGGFace2: The VGGFace2 dataset is made of around 3.31 million images divided into 9131 classes, each representing a different person identity. The dataset is divided into two splits, one for the training and one for test.
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).
I-HAZE: The I-Haze dataset contains 25 indoor hazy images (size 2833×4657 pixels) training. It has 5 hazy images for validation along with their corresponding ground truth images.
ShapeStacks: A simulation-based dataset featuring 20,000 stack configurations composed of a variety of elementary geometric primitives richly annotated regarding semantics and structural stability.

Options:- A: VGGFace2  B: CIFAR-10  C: I-HAZE  D: ShapeStacks
CIFAR-10