Omkar2118/datasets huggingface dataset:
G-VUE
Full name: General-purpose Visual Understanding Evaluation

**General-purpose Visual Understanding Evaluation (G-VUE)** is a comprehensive benchmark covering the full spectrum of visual cognitive abilities with four functional domains -- Perceive, Ground, Reason, and Act. The four domains are embodied in 11 carefully curated tasks, from 3D reconstruction to visual reasoning and manipulation.

Source: [Perceive, Ground, Reason, and Act: A Benchmark for General-purpose Visual Representation](https://arxiv.org/pdf/2211.15402v1.pdf)

Image Source: [https://github.com/wllmzhu/G-VUE](https://github.com/wllmzhu/G-VUE)

Introducing paper: Perceive, Ground, Reason, and Act: A Benchmark for General-purpose Visual Representation
RSSCN7

he RSSCN7 dataset contains satellite images acquired from Google Earth, which is originally collected for remote sensing scene classification. We conduct image synthesis on RSSCN7 to make it capable of the image inpainting task. It has seven classes: grassland, farmland, industrial and commercial regions, river and lake, forest field, residential region, and parking lot. Each class has 400 images, so there are total 2,800 images in the RSSCN7 dataset.

Introducing paper: Remote Sensing Image Scene Classification: Benchmark and State of the Art
OIR

OIR is a financial-domain dataset of the outbound intent recognition task. It aims to identify the intent of customer response in the outbound call scenario.

Introducing paper: VarMAE: Pre-training of Variational Masked Autoencoder for Domain-adaptive Language Understanding
MTC

MTC is a financial-domain dataset of the multi-label topic classification task. It aims to identify the topics of the spoken dialogue.

Introducing paper: VarMAE: Pre-training of Variational Masked Autoencoder for Domain-adaptive Language Understanding
PSM

PSM is a financial-domain dataset of the pairwise search matching task. It aims to identify the semantic similarity of a sentence pair in the search scenario.

Introducing paper: VarMAE: Pre-training of Variational Masked Autoencoder for Domain-adaptive Language Understanding
IEE

IEE is a financial-domain dataset of the Insurance-entity extraction task. Its goal is to locate named entities mentioned in the input sentence.

Introducing paper: VarMAE: Pre-training of Variational Masked Autoencoder for Domain-adaptive Language Understanding
UMD-i Affrodance Dataset

One-Shot Affordance Part Segmentation variant of the UMD dataset. Each object instance in the dataset contains a single image.
XBT Snapshot
Full name: XBT Snapshot from World Ocean Database

The World Ocean Database (WOD) is world's largest collection of uniformly formatted, quality controlled, publicly available ocean profile data. This dataset is a snapshot of the XBT observations which have been preprocessed for use in a machine learning pipeline.

The data is organised by year in CSV files, covering 1966-2015. This dataset does not include the actual temperature and depth profiles, as this dataset was focused on a project to improve the metadata.

Links
[World Ocean Database](https://www.ncei.noaa.gov/products/world-ocean-database )