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: We propose Single-Path Neural Architecture Search, a differentiable neural architecture search approach for mobile device-efficient

Keyphrases: image classification images

Dataset description:
NAS-Bench-201: NAS-Bench-201 is a benchmark (and search space) for neural architecture search. Each architecture consists of a predefined skeleton with a stack of the searched cell.
ImageNet: The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.
NATS-Bench: A unified benchmark on searching for both topology and size, for (almost) any up-to-date NAS algorithm. NATS-Bench includes the search space of 15,625 neural cell candidates for architecture topology and 32,768 for architecture size on three datasets.
Freiburg Groceries: Freiburg Groceries is a groceries classification dataset consisting of 5000 images of size 256x256, divided into 25 categories. It has imbalanced class sizes ranging from 97 to 370 images per class.

Options:- A: NAS-Bench-201  B: ImageNet  C: NATS-Bench  D: Freiburg Groceries
ImageNet