You are provided with a research question, keyphrases about the question, a description of candidate datasets and dataset options. Read the description of popular datasets provided below and select the ones that can be used to validate the following research question. Use your knowledge of machine learning datasets to make the best judgement.
Your response should be formatted as a `json` array. For instance, for the query "Semi supervised image classification", a sample response might be: ["CIFAR-10", "CIFAR-100"]. Do not include any extra text in the response other than the answer array.

Query: We propose a novel whole-to-part network and a part-to-whole network for the

Keyphrases: image classification

Dataset description:
UMDFaces: UMDFaces is a face dataset divided into two parts:   Still Images - 367,888 face annotations for 8,277 subjects. Video Frames - Over 3.7 million annotated video frames from over 22,000 videos of 3100 subjects.
UNITOPATHO: Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma. Colorectal polyps characterization relies on the histological analysis of tissue samples to determine the polyps malignancy and dysplasia grade.
Localized Narratives: We propose Localized Narratives, a new form of multimodal image annotations connecting vision and language. We ask annotators to describe an image with their voice while simultaneously hovering their mouse over the region they are describing.
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.
Business Scene Dialogue: The Japanese-English business conversation corpus, namely Business Scene Dialogue corpus, was constructed in 3 steps:   selecting business scenes, writing monolingual conversation scenarios according to the selected scenes, and translating the scenarios into the other language. Half of the monolingual scenarios were written in Japanese and the other half were written in English.
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).

Options:- A: UMDFaces  B: UNITOPATHO  C: Localized Narratives  D: ImageNet  E: Business Scene Dialogue  F: CIFAR-10
["ImageNet", "CIFAR-10"]