Omkar2118/datasets huggingface dataset:
AID
Full name: Aerial Image Dataset

AID is a new large-scale aerial image dataset, by collecting sample images from Google Earth imagery. Note that although the Google Earth images are post-processed using RGB renderings from the original optical aerial images, it has proven that there is no significant difference between the Google Earth images with the real optical aerial images even in the pixel-level land use/cover mapping. Thus, the Google Earth images can also be used as aerial images for evaluating scene classification algorithms.

The new dataset is made up of the following 30 aerial scene types: airport, bare land, baseball field, beach, bridge, center, church, commercial, dense residential, desert, farmland, forest, industrial, meadow, medium residential, mountain, park, parking, playground, pond, port, railway station, resort, river, school, sparse residential, square, stadium, storage tanks and viaduct. All the images are labelled by the specialists in the field of remote sensing image interpretation, and some samples of each class are shown in Fig.1. In all, the AID dataset has a number of 10000 images within 30 classes.

The images in AID are actually multi-source, as Google Earth images are from different remote imaging sensors. This brings more challenges for scene classification than the single source images like UC-Merced dataset. Moreover, all the sample images per each class in AID are carefully chosen from different countries and regions around the world, mainly in China, the United States, England, France, Italy, Japan, Germany, etc., and they are extracted at different time and seasons under different imaging conditions, which increases the intra-class diversities of the data.

Introducing paper: AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification
WHU-RS19

WHU-RS19 is a set of satellite images exported from Google Earth, which provides high-resolution satellite images up to 0.5 m. Some samples of the database are displayed in the following picture. It contains 19 classes of meaningful scenes in high-resolution satellite imagery, including airport, beach, bridge, commercial, desert, farmland, footballfield, forest, industrial, meadow, mountain, park, parking, pond, port, railwaystation, residential, river, and viaduct. For each class, there are about 50 samples. It’s worth noticing that the image samples of the same class are collected from different regions in satellite images of different resolutions and then might have different scales, orientations and illuminations.
SECOND
Full name: SEmantic Change detectiON Dataset

SECOND is a well-annotated semantic change detection dataset. To ensure data diversity, we firstly collect 4662 pairs of aerial images from several platforms and sensors. These pairs of images are distributed over the cities such as Hangzhou, Chengdu, and Shanghai. Each image has size 512 x 512 and is annotated at the pixel level. The annotation of SECOND is carried out by an expert group of earth vision applications, which guarantees high label accuracy. For the change category in the SECOND dataset, we focus on 6 main land-cover classes, i.e. , non-vegetated ground surface, tree, low vegetation, water, buildings and playgrounds , that are frequently involved in natural and man-made geographical changes. It is worth noticing that, in the new dataset, non-vegetated ground surface ( n.v.g. surface for short) mainly corresponds to impervious surface and bare land. In summary, these 6 selected land-cover categories result in 30 common change categories (including non-change ). Through the random selection of image pairs, the SECOND reflects real distributions of land-cover categories when changes occur.

Introducing paper: Semantic Change Detection with Asymmetric Siamese Networks
Shellcode_IA32
Full name: Shellcode_IA32

Shellcode_IA32 is a dataset containing 20 years of shellcodes from a variety of sources is the largest collection of shellcodes in assembly available to date.

This dataset consists of 3,200 examples of instructions in assembly language for IA-32 (the 32-bit version of the x86 Intel Architecture) from publicly available security exploits. We collected assembly programs used to generate shellcode from exploit-db and from shell-storm. We enriched the dataset by adding examples of assembly programs for the IA-32 architecture from popular tutorials and books. This allowed us to understand how different authors and assembly experts comment and, thus, how to deal with the ambiguity of natural language in this specific context. Our dataset consists of 10% of instructions collected from books and guidelines, and the rest from real shellcodes.

Our focus is on Linux, the most common OS for security-critical network services. Accordingly, we added assembly instructions written with Netwide Assembler (NASM) for Linux.

Each line of Shellcode_IA32 dataset represents a snippet - intent pair. The snippet is a line or a combination of multiple lines of assembly code, built by following the NASM syntax. The intent is a comment in the English language.

Further statistics on the dataset and a set of preliminary experiments performed with a neural machine translation (NMT) model are described in the following paper: [Shellcode_IA32: A Dataset for Automatic Shellcode Generation](https://aclanthology.org/2021.nlp4prog-1.7).

Introducing paper: Shellcode_IA32: A Dataset for Automatic Shellcode Generation
SILICONE Benchmark
Full name: SILICONE

The Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE (SILICONE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems specifically designed for spoken language. All datasets are in the English language and covers a  large variety of domains (e.g daily life, scripted scenarios, joint task completion, phone call conversations, and televsion dialogue). Some datasets additionally include emotion and/or sentiment labels.

Introducing paper: Hierarchical Pre-training for Sequence Labelling in Spoken Dialog
FKD
Full name: Football Keywords Dataset

The football keyword dataset (FKD), as a new keyword spotting dataset in Persian, is collected with crowdsourcing. This dataset contains nearly 31000 samples in 18 classes.

Introducing paper: EfficientNet-Absolute Zero for Continuous Speech Keyword Spotting
SDCNL (Suicide vs Depression Classification)

We develop a primary dataset based on our task of suicide or depression classification. This dataset is web-scraped from Reddit. We collect our data from subreddits using the Python Reddit API. We specifically scrape from two subreddits, r/SuicideWatch3 and r/Depression. The dataset contains 1,895 total posts. We utilize two fields from the scraped data: the original text of the post as our inputs, and the subreddit it belongs to as labels. Posts from r/SuicideWatch are labeled as suicidal, and posts from r/Depression are labeled as depressed. We make this dataset and the web-scraping script available in our code.

Introducing paper: Deep Learning for Suicide and Depression Identification with Unsupervised Label Correction
Reddit C-SSRS

The C-SSRS dataset contains 500 Reddit posts from the subreddit r/depression. These posts are labeled by psychologists on a five point scale according to guidelines established in the Columbia Suicide Severity Rating Scale, which progress according to
severity of depression. As this dataset is clinically verified and labeled, it is an adequate dataset to validate the label correction method, especially since it is from the same domain of mental health.

Source: [Deep Learning for Suicide and Depression Identification with Unsupervised Label Correction](https://paperswithcode.com/paper/deep-learning-for-suicide-and-depression)
Paper: [Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention](https://dl.acm.org/doi/10.1145/3308558.3313698)
SHHS
Full name: Sleep Heart Health Study

The Sleep Heart Health Study (SHHS) is a multi-center cohort study implemented by the National Heart Lung & Blood Institute to determine the cardiovascular and other consequences of sleep-disordered breathing. It tests whether sleep-related breathing is associated with an increased risk of coronary heart disease, stroke, all cause mortality, and hypertension.  In all, 6,441 men and women aged 40 years and older were enrolled between November 1, 1995 and January 31, 1998 to take part in SHHS Visit 1. During exam cycle 3 (January 2001- June 2003), a second polysomnogram (SHHS Visit 2) was obtained in 3,295 of the participants. CVD Outcomes data were monitored and adjudicated by parent cohorts between baseline and 2011. More than 130 manuscripts have been published investigating predictors and outcomes of sleep disorders.
AraCOVID19-MFH
Full name: AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News and Hate Speech Detection Dataset

AraCOVID19-MFH is a manually annotated multi-label Arabic COVID-19 fake news and hate speech detection dataset. The dataset contains 10,828 Arabic tweets annotated with 10 different labels.

Introducing paper: AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News and Hate Speech Detection Dataset
AvaSym

Global Symmetry Ground-truth for AVA dataset.

Release Date: 2016.

Users of this software are encouraged to cite the following article:
Elawady, Mohamed, Cécile Barat, Christophe Ducottet, and Philippe Colantoni. "Global Bilateral Symmetry Detection Using Multiscale Mirror Histograms." In International Conference on Advanced Concepts for Intelligent Vision Systems, pp. 14-24. Springer International Publishing, 2016.


Contents:

 GT_AVA/AVA_GT.mat : list of image names and axis groundtruth (x1,y1,x2,y2).

DwnImgs.m : MATLAB code file to download images from DpChallenge website and to show how to use groundtruth, the m-code will create two directories ('Imgs', 'ImgsGT').

Introducing paper: Global Bilateral Symmetry Detection Using Multiscale Mirror Histograms