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 study a Region-based Fully Convolutional Network (R-FCN) and a NoC

Keyphrases: object detection image

Dataset description:
BraTS 2017: The BRATS2017 dataset. It contains 285 brain tumor MRI scans, with four MRI modalities as T1, T1ce, T2, and Flair for each scan.
THEODORE: Recent work about synthetic indoor datasets from perspective views has shown significant improvements of object detection results with Convolutional Neural Networks(CNNs). In this paper, we introduce THEODORE: a novel, large-scale indoor dataset containing 100,000 high- resolution diversified fisheye images with 14 classes.
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.
Helen: The HELEN dataset is composed of 2330 face images of 400×400 pixels with labeled facial components generated through manually-annotated contours along eyes, eyebrows, nose, lips and jawline.
LOGO-Net: A large-scale logo image database for logo detection and brand recognition from real-world product images.
COCO-Text: The COCO-Text dataset is a dataset for text detection and recognition. It is based on the MS COCO dataset, which contains images of complex everyday scenes.
COCO: The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.

Options:- A: BraTS 2017  B: THEODORE  C: ImageNet  D: Helen  E: LOGO-Net  F: COCO-Text  G: COCO
["ImageNet", "COCO"]