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 saliency detection from images.

Keyphrases: saliency detection images

Dataset description:
SNIPS: The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:   SearchCreativeWork (e.g. Find me the I, Robot television show), GetWeather (e.g.
LFSD: The Light Field Saliency Database (LFSD) contains 100 light fields with 360×360 spatial resolution. A rough focal stack and an all-focus image are provided for each light field.
ECSSD: The Extended Complex Scene Saliency Dataset (ECSSD) is comprised of complex scenes, presenting textures and structures common to real-world images. ECSSD contains 1,000 intricate images and respective ground-truth saliency maps, created as an average of the labeling of five human participants.
CoSal2015: Cosal2015 is a large-scale dataset for co-saliency detection which consists of 2,015 images of 50 categories, and each group suffers from various challenging factors such as complex environments, occlusion issues, target appearance variations and background clutters, etc. All these increase the difficulty for accurate co-saliency detection.

Options:- A: SNIPS  B: LFSD  C: ECSSD  D: CoSal2015
ECSSD