Omkar2118/datasets huggingface dataset:
IMDB-WIKI

To the best of our knowledge this is the largest publicly available dataset of face images with gender and age labels for training. We provide pretrained models for both age and gender prediction.

Introducing paper: Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks
GMSC
Full name: Give Me Some Credit

Data for a Kaggle competition

Banks play a crucial role in market economies. They decide who can get finance and on what terms and can make or break investment decisions. For markets and society to function, individuals and companies need access to credit. 

Credit scoring algorithms, which make a guess at the probability of default, are the method banks use to determine whether or not a loan should be granted. This competition requires participants to improve on the state of the art in credit scoring, by predicting the probability that somebody will experience financial distress in the next two years.

The goal of this competition is to build a model that borrowers can use to help make the best financial decisions.

Historical data are provided on 250,000 borrowers and the prize pool is $5,000 ($3,000 for first, $1,500 for second and $500 for third).
BIRDeep
Full name: BIRDeep_AudioAnnotations

The BIRDeep Audio Annotations dataset is a collection of bird vocalizations from Doñana National Park, Spain. It was created as part of the BIRDeep project, which aims to optimize the detection and classification of bird species in audio recordings using deep learning techniques. The dataset is intended for use in training and evaluating models for bird vocalization detection and identification.

Introducing paper: A Bird Song Detector for improving bird identification through Deep Learning: a case study from Doñana