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: A system that leverages attribute features for end-to-end vehicle re-identification.

Keyphrases: vehicle re-identification images

Dataset description:
E2E: End-to-End NLG Challenge (E2E) aims to assess whether recent end-to-end NLG systems can generate more complex output by learning from datasets containing higher lexical richness, syntactic complexity and diverse discourse phenomena.
VeRi-Wild: Veri-Wild is the largest vehicle re-identification dataset (as of CVPR 2019). The dataset is captured from a large CCTV surveillance system consisting of 174 cameras across one month (30× 24h) under unconstrained scenarios.
DDD20: The dataset was captured with a DAVIS camera that concurrently streams both dynamic vision sensor (DVS) brightness change events and active pixel sensor (APS) intensity frames. DDD20 is the longest event camera end-to-end driving dataset to date with 51h of DAVIS event+frame camera and vehicle human control data collected from 4000km of highway and urban driving under a variety of lighting conditions.
VeRi-776: VeRi-776 is a vehicle re-identification dataset which contains 49,357 images of 776 vehicles from 20 cameras. The dataset is collected in the real traffic scenario, which is close to the setting of CityFlow.
VehicleID: The “VehicleID” dataset contains CARS captured during the daytime by multiple real-world surveillance cameras distributed in a small city in China. There are 26,267 vehicles (221,763 images in total) in the entire dataset.
MLe2e: MLe2 is a dataset for the evaluation of scene text end-to-end reading systems and all intermediate stages such as text detection, script identification and text recognition. The dataset contains a total of 711 scene images covering four different scripts (Latin, Chinese, Kannada, and Hangul).

Options:- A: E2E  B: VeRi-Wild  C: DDD20  D: VeRi-776  E: VehicleID  F: MLe2e
["VeRi-776", "VehicleID"]