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: A method based on self-training to perform unsupervised domain adaptation in the task of food classification.

Keyphrases: food classification images

Dataset description:
MaleX: MaleX is a curated dataset of malware and benign Windows executable samples for malware researchers. The dataset contains 1,044,394 Windows executable binaries with 864,669 labelled as malware and 179,725 as benign.
Food-101: The  Food-101 dataset consists of 101 food categories with 750 training and 250 test images per category, making a total of 101k images. The labels for the test images have been manually cleaned, while the training set contains some noise.
CASIA V2: CASIA V2 is a dataset for forgery classification. It contains 4795 images, 1701 authentic and 3274 forged.
Cross-Dataset Testbed: The Cross-dataset Testbed is a Decaf7 based cross-dataset image classification dataset, which contains 40 categories of images from 3 domains: 3,847 images in Caltech256, 4,000 images in ImageNet, and 2,626 images for SUN. In total there are 10,473 images of 40 categories from these three domains.

Options:- A: MaleX  B: Food-101  C: CASIA V2  D: Cross-Dataset Testbed
Food-101