Omkar2118/datasets huggingface dataset:
LSVTD

**LSVTD** is a large scale video text dataset for promoting the video text spotting community, which contains 100 text videos from 22 different real-life scenarios. LSVTD covers a wide range of 13 indoor (eg. bookstore, shopping mall) and 9 outdoor scenarios, which is more than 3 times the diversity of IC15.

Introducing paper: You Only Recognize Once: Towards Fast Video Text Spotting
DriverMHG

**Driver Micro Hand Gestures** (**DriverMHG**) is a dataset for dynamic recognition of driver micro hand gestures, which consists of RGB, depth and infrared modalities.

Introducing paper: DriverMHG: A Multi-Modal Dataset for Dynamic Recognition of Driver Micro Hand Gestures and a Real-Time Recognition Framework
BCI Competition Datasets

The goal of the "BCI Competition" is to validate signal processing and classification methods for Brain-Computer Interfaces (BCIs).

Introducing paper: Feature Weighting and Regularization of Common Spatial Patterns in EEG-Based Motor Imagery BCI
UQuAD
Full name: Urdu Question Answering Dataset

Large scale machine reading comprehension dataset in Urdu language.

Introducing paper: UQuAD1.0: Development of an Urdu Question Answering Training Data for Machine Reading Comprehension
Adaptiope

Adaptiope is a domain adaptation dataset with 123 classes in the three domains synthetic, product and real life. One of the main goals of Adaptiope is to offer a clean and well curated set of images for domain adaptation. This was necessary as many other common datasets in the area suffer from label noise and low quality images. Additionally, Adaptiope's class set was chosen in a way that minimizes the overlap with the class set of the commonly used ImageNet pretraining, therefore preventing information leakage in a domain adaptation setup.

Introducing paper: Adaptiope: A Modern Benchmark for Unsupervised Domain Adaptation
Modern Office-31

Modern Office-31 is a refurbished version of the commonly used [Office-31](https://paperswithcode.com/dataset/office-31) dataset. Modern Office-31 rectifies many of the annotation errors and low quality images in the Amazon domain of the original Office-31 dataset. Additionally, this dataset adds another synthetic domain based on the [Adaptiope](https://paperswithcode.com/dataset/adaptiope) dataset.

Introducing paper: Adaptiope: A Modern Benchmark for Unsupervised Domain Adaptation
AVASpeech-SMAD
Full name: AVASpeech-SMAD: A Strongly Labelled Speech and Music Activity Detection Dataset with Label Co-Occurrence

We propose a dataset, AVASpeech-SMAD, to assist speech and music activity detection research. With frame-level music labels, the proposed dataset extends the existing AVASpeech dataset, which originally consists of 45 hours of audio and speech activity labels. To the best of our knowledge, the proposed AVASpeech-SMAD is the first open-source dataset that features strong polyphonic labels for both music and speech. The dataset was manually annotated and verified via an iterative cross-checking process. A simple automatic examination was also implemented to further improve the quality of the labels. Evaluation results from two state-of-the-art SMAD systems are also provided as a benchmark for future reference.

Introducing paper: AVASpeech-SMAD: A Strongly Labelled Speech and Music Activity Detection Dataset with Label Co-Occurrence
Ballroom

This data set includes beat and bar annotations of the ballroom dataset, introduced by Gouyon et al. [1].

[1] Gouyon F., A. Klapuri, S. Dixon, M. Alonso, G. Tzanetakis, C. Uhle, and P. Cano. An experimental comparison of audio tempo induction algorithms. Transactions on Audio, Speech and Language Processing 14(5), pp.1832-1844, 2006.
Beatles

This dataset includes the beat and downbeat annotations for  Beatles albums. The annotations are provided by M. E. P. Davies et. al [1].

M. E. P. Davies, N. Degara, and M. D. Plumbley, “Evaluation methods for musical audio beat tracking algorithms,” in Technical Report C4DM-TR-09-06, Centre for Digital Music, Queen Mary University of London, 2009.
Rock Corpus

This dataset contains 200 famous songs in different genres (mostly in rock) and the beats and downbeat annotations are provided by  T. de Clercq and D. Temperley [1].

[1] T. de Clercq and D. Temperley., “A corpus analysis of
rock harmony,” Popular Music, vol. 30, no. 1, pp. 47–
70, 2011.
Carnatic

This dataset includes music time information i.e. Beat, Bar, and meter annotations of the Indian Carnatic music dataset. The dataset is gathered by A. Srinivasamurthy and X. Serra [1].

[1] A. Srinivasamurthy and X. Serra, “A supervised approach to hierarchical metrical cycle tracking from audio music recordings,” in In Proc. of the IEEE Int. Conference on Acoustics, Speech, and Signal Processing
(ICASSP), 2014.
SINGA:PURA
Full name: SINGApore: Polyphonic URban Audio

This repository contains the SINGA:PURA dataset, a strongly-labelled polyphonic urban sound dataset with spatiotemporal context. The data were collected via a number of recording units deployed across Singapore as a part of a wireless acoustic sensor network. These recordings were made as part of a project to identify and mitigate noise sources in Singapore, but also possess a wider applicability to sound event detection, classification, and localization. The taxonomy we used for the labels in this dataset has been designed to be compatible with other existing datasets for urban sound tagging while also able to capture sound events unique to the Singaporean context. Please refer to our conference paper published in APSIPA 2021 (which is found in this repository as the file "APSIPA.pdf") or download the readme ("Readme.md") for more details regarding the data collection, annotation, and processing methodologies for the creation of the dataset.

Introducing paper: A Strongly-Labelled Polyphonic Dataset of Urban Sounds with Spatiotemporal Context