Omkar2118/datasets huggingface dataset:
P2GB

A benchmark designed to evaluate MLLMs’ proficiency in understanding inter-object relationships and textual content.

Introducing paper: Plug-and-Play Grounding of Reasoning in Multimodal Large Language Models
OnlySports Dataset

The OnlySports Dataset is a comprehensive collection of sports-related text data, comprising approximately 600 billion tokens. This massive corpus was carefully curated from the FineWeb dataset, a cleaned subset of CommonCrawl spanning from 2013 to present. The dataset creation involved a two-step process:

1. URL filtering using sports-related keywords to identify potentially relevant content.
2. Application of a custom-built sports text classifier to accurately extract sports-specific documents.

At 1.2 TB in size, OnlySports Dataset represents the largest known sports-domain dataset to date. It encompasses a wide range of content including news articles, blogs, match reports, interviews, and tutorials, providing a rich resource for training domain-specific language models in the sports field.

Introducing paper: OnlySportsLM: Optimizing Sports-Domain Language Models with SOTA Performance under Billion Parameters
HECKTOR

Head and Neck Tumor Segmentation

Introducing paper: Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images
AutoPET

A whole-body FDG-PET/CT dataset with manually annotated tumor lesions (FDG-PET-CT-Lesions)
1,014 studies (900 patients)

Introducing paper: AutoPET Challenge 2022: Automatic Segmentation of Whole-body Tumor Lesion Based on Deep Learning and FDG PET/CT
SpCQL
Full name: Text-to-CQL

The first dataset contains annotated natural language queries (i.e. Mandarin) with their Cypher equivalent.
It is made up of:
    - A Neo4j database
    - 10000 pairs of Text-Cypher queries

Introducing paper: SpCQL: A Semantic Parsing Dataset for Converting Natural Language into Cypher
THuman2.0 Dataset

THuman2.0 Dataset contains 500 high-quality human scans captured by a dense DLSR rig. For each scan, we provide the 3D model (.obj) and the corresponding texture map (.jpeg).
Image Source: [Original Paper](https://github.com/ytrock/THuman2.0-Dataset)

Introducing paper: Function4D: Real-time Human Volumetric Capture from Very Sparse Consumer RGBD Sensors
Emilia Dataset
Full name: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation

Recent advancements in speech generation models have been significantly driven by the use of large-scale training data. However, producing highly spontaneous, human-like speech remains a challenge due to the scarcity of large, diverse, and spontaneous speech datasets. In response, we introduce Emilia, the first large-scale, multilingual, and diverse speech generation dataset. Emilia starts with over 101k hours of speech across six languages, covering a wide range of speaking styles to enable more natural and spontaneous speech generation. To facilitate the scale-up of Emilia, we also present Emilia-Pipe, the first open-source preprocessing pipeline designed to efficiently transform raw, in-the-wild speech data into high-quality training data with speech annotations. Experimental results demonstrate the effectiveness of both Emilia and Emilia-Pipe. Demos are available at: https://emilia-dataset.github.io/Emilia-Demo-Page/.

Introducing paper: Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation
VNEMOS
Full name: Vietnamese Speech Emotion Dataset

This research introduces the dataset that we created to test voice emotional recognition models with Vietnamese data. The data set is the result of research, testing, and filtering 250 emotional segments from movie, movie series and live show divided equally for 5 basic emotional states of humans: “anger, happiness, sadness, neutral and anxiety”, VNEMOS contains approximately 30 minutes long. This dataset brings a balanced and diverse emotional set to study emotional recognition issues related to understanding human mood and emotions. Through evaluation Our dataset achieved a 89\% accuracy, showcasing the adeptness at capturing the essence of positive emotions and robust learning capabilities in real-world scenarios. By clarifying these nuances. Our research will contribute much to future work on emotion recognition and signal processing in Vietnamese and data using human psychological science. Detailed information of the dataset is in the following link: “bit.ly/VNEMOS”.