Omkar2118/datasets huggingface dataset:
FETAL_PLANES_DB
Full name: Common maternal-fetal ultrasound images

A large dataset of routinely acquired maternal-fetal screening ultrasound images collected from two different hospitals by several operators and ultrasound machines. All images were manually labeled by an expert maternal fetal clinician. Images are divided into 6 classes: four of the most widely used fetal anatomical planes (Abdomen, Brain, Femur and Thorax), the mother’s cervix (widely used for prematurity screening) and a general category to include any other less common image plane. Fetal brain images are further categorized into the 3 most common fetal brain planes (Trans-thalamic, Trans-cerebellum, Trans-ventricular) to judge fine grain categorization performance. Meta information (patient number, us machine, operator) is also provided, as well as the training-test split used in the Nature Sci Rep paper.
CelebA-Spoof-Enroll

CelebA-Spoof is a large-scale face anti-spoofing dataset
recently introduced in [53]. The dataset contains 625,537
images of 10,177 celebrities captured under different spoof
mediums, environments and illumination conditions. The
original dataset proposes three different evaluation protocols. For our experimentation, we focus on the most general
”intra” protocol, in which different spoof types, environments and illumination conditions are used for both training
and testing.

To generate CelebA-Spoof-Enroll, the personalized version of the CelebA-Spoof anti-spoofing dataset, we start by
setting the desired enrollment set size N. We decide for a
constant number of enrollment images per user to be consistent with the implementation in typical commercial applications and to simplify the dataset definition. For both the
training and test split, we count the number of live samples
per user and discard those users having ≤ N live samples.
So, if we desire a higher value of N, a larger number of
users would get rejected and hence the number of training
samples would be less. Note that it is not possible to include
all original data, as a number of users in CelebA-Spoof are
missing live samples. Nevertheless, only a very small percentage of training and test data is discarded through this
process when choosing N < 10.
For each accepted user, the first N live samples (ordered according to the ascending alphanumeric ordering of
the original filenames) are chosen to define its enrollment
set. The rest of the live samples and the spoof samples are
marked as query samples. It is important to note that using
this deterministic method of obtaining enrollment samples
does not introduce unwanted bias as most of the CelebASpoof images are randomly crawled from the internet and
are not ordered according to specific criteria. With this setting, we associate a user identifier with each query sample
such that queries can be easily mapped to the correct enrollment set. This method of filtering users for a desired enrollment size N is performed for both training and test split. In
the rest of the paper, we refer to CelebA-Spoof-EnrollN or
in short CASp-EnrollN to describe the personalized version
of the dataset with N enrollment images

(See paper for additional details)

Introducing paper: A personalized benchmark for face anti-spoofing
SiW-Enroll

SiW (Spoofing in the Wild) is a face anti-spoofing dataset recently introduced in [29] where images are extracted from short videos captured at high resolution and 30 frames per second. In total, 4,478 videos are collected from 165 subjects including variations in spoof type, recording device, illumination condition, pose and facial expression. 

To define train and test sets, we start by following the splitting system described as Protocol 1 in [29]. We further subsample train and test sets by extracting 1 every 10 frames (or every 0.33 seconds) in each video, since consecutive frames are almost identical. Finally, since we observe that simple models can reach very high accuracy, we further make the task harder by creating 2 separate training and evaluation folds in a way that the model is trained and tested on different spoof types. Since the original dataset is collected using two different capturing devices, we can use this information for the definition of enrollment sets. Indeed, in a real scenario, the subject would have to enroll twice when using two different devices, meaning that a unique enrollment set will be generated for each combination of subject and capturing devices. Capturing the sensor bias in the enrollment can be particularly beneficial, for example to detect if the resolution of a face changes, which can indicate a replay attack. To extract enrollment data we choose a single video from each subject and sensor among the available ones. We arbitrarily pick the video without illumination changes and with variation in subject pose, as it resembles the enrollment conditions required in real devices. We then equidistantly sample N frames over the video to construct the enrollment set. As shown in Fig. 2, this allows capturing different poses and facial expressions from the subject. Finally, we exclude all the frames in this video from training and test data to avoid in341 formation leakage and match all the remaining queries from the same subject and sensor to their unique enrollment set.

(See paper for additional details)

Introducing paper: A personalized benchmark for face anti-spoofing
SiW
Full name: Spoofing in the Wild

SiW provides live and spoof videos from 165 subjects. For each subject, we have 8 live and up to 20 spoof videos, in total 4,478 videos. All videos are in 30 fps, about 15 second length, and 1080P HD resolution. The live videos are collected in four sessions with variations of distance, pose, illumination and expression. The spoof videos are collected with several attacks such as printed paper and replay.

Introducing paper: Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision
AI-generated Twitter Timelines

This project contains instructions and codes to reconstruct a dataset for the development and evaluation of forensic tools for detecting machine-generated text in social media.

-We are not releasing full twitter data to comply with Twitter terms of service.
-We are also not releasing generators and machine generated data for ethical reasons.

Introducing paper: Stylometric Detection of AI-Generated Text in Twitter Timelines
PPTC
Full name: PowerPoint Task Completion (PPTC)

Recent evaluations of Large Language Models (LLMs) have centered around testing their zero-shot/few-shot capabilities for basic natural language tasks and their ability to translate instructions into tool APIs. However, the evaluation of LLMs utilizing complex tools to finish multi-turn, multi-modal instructions in a complex multi-modal environment has not been investigated. To address this gap, we introduce the PowerPoint Task Completion (PPTC) benchmark to assess LLMs' ability to create and edit PPT files based on user instructions. It contains 279 multi-turn sessions covering diverse topics and hundreds of instructions involving multi-modal operations.
Unified NSL Data Feb 2024
Full name: Unified NSL Data until Feb 2024

The dataset is hosted on GitHub: https://github.com/ucsdsysnet/nsl-empirical-analysis
and explained in our paper.

Introducing paper: An Empirical Analysis on the Use and Reporting of National Security Letters
Amazon-Sports
Full name: Amazon-Sports

Amazon-Sports is a sub-category of the Amazon dataset, which contains a series of product reviews crawled from Amazon.com.

Introducing paper: Image-based Recommendations on Styles and Substitutes
basqueparl
Full name: BasqueParl

This repository contains **BasqueParl**, a bilingual corpus for political discourse analysis. It covers transcriptions from the Parliament of the Basque Autonomous Community for eight years and two legislative terms (2012-2020), and its main characteristic is the presence of Basque-Spanish code-switching speeches.

Introducing paper: BasqueParl: A Bilingual Corpus of Basque Parliamentary Transcriptions
MUSIC-AVQA v2.0

MUSIC-AVQA v2.0 balances the original MUSIC-AVQA dataset in each QA category and sub-category. This balance results in a more reliable benchmark than the original MUSIC-AVQA dataset. See more details at paper "Tackling Data Bias in MUSIC-AVQA: Crafting a Balanced Dataset for Unbiased Question-Answering" published at WACV 2024.
Polymer Solar Cells Device Characteristics

Device characteristics data for 835 distinct donor/acceptor systems for polymer solar cells extracted from abstracts of journal papers. This includes power conversion efficiency, fill factor, open circuit voltage, and short circuit current depending on what was reported in the abstract. There are 1187 data points in total as several donor/acceptor systems are reported in multiple papers.

Introducing paper: Accelerating materials discovery for polymer solar cells: Data-driven insights enabled by natural language processing
The COLOSSEUM
Full name: The COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation

To realize effective large-scale, real-world robotic applications, we must evaluate how well our robot policies adapt to changes in environmental conditions. Unfortunately, a majority of studies evaluate robot performance in environments closely resembling or even identical to the training setup.

We present Colosseum, a novel simulation benchmark,with 20 diverse manipulation tasks, that enables systematical evaluation of models across 12 axes of environmental perturbations. These perturbations include changes in color, texture, and size of objects, table-tops, and backgrounds; we also vary lighting, distractors, and camera pose. Using Colosseum, we compare 4 state-of-the-art manipulation models to reveal that their success rate degrades between 30-50% across these perturbation factors.

When multiple perturbations are applied in unison, the success rate degrades > 75%. We identify that changing the number of distractor objects, target object color, or lighting conditions are the perturbations that reduce model performance the most. To verify the ecological validity of our results, we show that our results in simulation are correlated (R2 = 0.614) to similar perturbations in real-world experiments. We open source code for others to use Colosseum, and also release code to 3D print the objects used to replicate the real-world perturbations. Ultimately, we hope that Colosseum will serve as a benchmark to identify modeling decisions that systematically improve generalization for manipulation.

Introducing paper: THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation
CARBEN
Full name: Composite Adversarial Robustness Benchmark

Prior literature on adversarial attack methods has mainly focused on attacking with and defending against a single threat model, e.g., perturbations bounded in Lp ball. However, multiple threat models can be combined into composite perturbations. One such approach, composite adversarial attack (CAA), not only expands the perturbable space of the image, but also may be overlooked by current modes of robustness evaluation. To this end, we proposed CARBEN, a benchmark of composite adversarial robustness that accurately reflects the composite robustness of the considered models.

Introducing paper: CARBEN: Composite Adversarial Robustness Benchmark