The Face Mask Detection Dataset includes 853 labeled images categorized into three classes: people wearing masks, not wearing masks, and wearing masks incorrectly. Each image comes with bounding box annotations in PASCAL VOC format. This dataset enables the development of deep learning models to detect mask usage and assess its correctness, offering a valuable tool for public health applications, especially during pandemics like COVID-19.

The World Bank Indicators Dataset provides a comprehensive collection of time series data from the World Bank Open Data Platform, covering a wide range of global indicators from 1960 to 2023. It includes economic, social, environmental, and demographic metrics, making it a valuable resource for researchers, data scientists, and policymakers. The dataset is structured as a CSV file with aggregated data, and it is ideal for applications such as economic forecasting, socio-economic studies, environmental impact analysis, and demographic research.

The Face Verification Dataset offers a balanced and standardized collection of face image pairs, ideal for training and evaluating face recognition and verification models. It includes over 6,600 image pairs—equally divided between same-person and different-person categories. The faces, sourced from publicly available actor databases, have been automatically cropped using Haar cascades and resized to 100x100 pixels for consistency. This dataset is especially useful for deep learning approaches like Siamese Networks in face authentication tasks.

The Car Object Detection dataset is a practical resource for developing and testing object detection models, particularly YOLO. It includes over 1000 videos featuring cars from various angles, challenging the model to generalize across views. With a focus on real-time performance, this dataset is perfect for experimenting with YOLO's speed and accuracy advantages. Originally sourced from a TJHSST competition, it provides a hands-on playground for object detection research and development.

This dataset provides a comprehensive daily news feed about Alphabet Inc. (GOOGL) from 2000 to 2025. It's ideal for NLP applications, sentiment analysis, and exploring how financial news impacts stock prices. When combined with the accompanying dataset containing Google’s financial statements and stock prices, it becomes a powerful tool for building predictive models, conducting event-driven investment analysis, and understanding the interplay between corporate news and market behavior.