Omkar2118/datasets huggingface dataset:
MuReD Dataset
Full name: Multi-Label Retinal Diseases Dataset

Early detection of retinal diseases is one of the most important means of preventing partial or permanent blindness in patients. One of the major stumbling blocks for manual retinal examination is the lack of a sufficient number of qualified medical personnel per capita to diagnose diseases. Computer-aided diagnosis systems (CAD) have proven to be very effective in helping physicians reduce the time taken to make a diagnosis and minimize variability in image interpretation. Still, they are not flexible enough to accommodate the simultaneous presence of multiple retinal diseases, which is a common situation in real-world applications. In the past years, few datasets that focus on the classification of numerous retinal pathologies present at the same time, i.e., multi-label classification have been proposed, but there are some shared problems with all of them, such as a narrow range of pathologies to classify, high level of class imbalance, low amount of samples for the underrepresented labels, no assurance in image quality, among others. All these problems hinder the performance of any model trained with these datasets, which leads to poor robustness, lack of generalization, and reduced trustability in its predictions.
To address these problems, we constructed the Multi-Label Retinal Diseases (MuReD) dataset, using images collected from three different state-of-the-art sources, i.e., ARIA, STARE, and RFMiD datasets, and performing a sequence of post-processing steps to ensure the quality of the images, a wide range of diseases to classify, and a sufficient number of samples per disease label.
The MuReD dataset consists of 2208 images with 20 different labels, with varying image quality and resolution. At the same time, ensuring a minimal degree of quality in the data, with a sufficient number of samples per label. To the best of our knowledge, the MuReD dataset, is the only publicly available dataset that applies a sequence of post-processing steps to ensure the quality of the images, the variety of pathologies, and the number of samples per label, resulting in increased data quality and a significant reduction of the class imbalance present in the publicly available datasets.
It is envisaged that the MuReD dataset will enable the creation of more robust, general, and trustable models for the automatic detection and classification of retinal diseases.

Introducing paper: Multi-Label Retinal Disease Classification using Transformers
FETA Car-Manuals
Full name: FETA Car-Manuals dataset, image-text retrieval for foundation models' expert data performance.

**FETA** benchmark focuses on text-to-image and image-to-text retrieval in public car manuals and sales catalogue brochures. The FETA Car-Manuals dataset consists of a total of 349 PDF documents from 5 car manufacturers, namely Nissan, Toyota, Mazda, Renault, Chevrolet.

Introducing paper: FETA: Towards Specializing Foundation Models for Expert Task Applications
FETA IKEA

**FETA** benchmark focuses on text-to-image and image-to-text retrieval in public car manuals and sales catalogue brochures. The FETA IKEA dataset contains 26 documents with 7366 pages total, approximately 9574 images and 23927 texts automatically extracted from those pages.

Introducing paper: FETA: Towards Specializing Foundation Models for Expert Task Applications
Verifee

**Verifee** is a dataset of news articles with fine-grained trustworthiness annotations. It contains over 10, 000 unique articles from almost 60 Czech online news sources. These are categorized into one of the 4 classes across the credibility spectrum we propose, raging from entirely trustworthy articles all the way to the manipulative ones. 

Source: [Fine-grained Czech News Article Dataset: An Interdisciplinary Approach to Trustworthiness Analysis](https://arxiv.org/pdf/2212.08550v1.pdf)

Image Source: []()

Introducing paper: Fine-grained Czech News Article Dataset: An Interdisciplinary Approach to Trustworthiness Analysis
Werewolf Among Us

**Werewolf Among Us** is a dataset multimodal dataset for modeling persuasion behaviors. It contains 199 dialogue transcriptions and videos captured in a multi-player social deduction game setting, 26,647 utterance level annotations of persuasion strategy, and game level annotations of deduction game outcomes.

Source: [Werewolf Among Us: A Multimodal Dataset for Modeling Persuasion Behaviors in Social Deduction Games](https://arxiv.org/pdf/2212.08279v1.pdf)

Image Source: [https://arxiv.org/pdf/2212.08279v1.pdf](https://arxiv.org/pdf/2212.08279v1.pdf)

Introducing paper: Werewolf Among Us: A Multimodal Dataset for Modeling Persuasion Behaviors in Social Deduction Games