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: I want to train a supervised model for crowd density and count estimation from images.

Keyphrases: crowd density count estimation images

Dataset description:
DIODE: Diode Dense Indoor/Outdoor DEpth (DIODE) is the first standard dataset for monocular depth estimation comprising diverse indoor and outdoor scenes acquired with the same hardware setup. The training set consists of 8574 indoor and 16884 outdoor samples from 20 scans each.
DeepFish: DeepFish as a benchmark suite with a large-scale dataset to train and test methods for several computer vision tasks. The dataset consists of approximately 40 thousand images collected underwater from 20 habitats in the marine environments of tropical Australia.
ShanghaiTech: The Shanghaitech dataset is a large-scale crowd counting dataset. It consists of 1198 annotated crowd images.
NWPU-Crowd: NWPU-Crowd consists of 5,109 images, in a total of 2,133,375 annotated heads with points and boxes. Compared with other real-world datasets, it contains various illumination scenes and has the largest density range (0~20,033).

Options:- A: DIODE  B: DeepFish  C: ShanghaiTech  D: NWPU-Crowd
ShanghaiTech