Context: The Appa Real Face Cropped dataset, originally divided into separate training, testing, and validation sets, has been consolidated into a single collection of 7,591 images. These images are meticulously cropped and aligned, ensuring centered faces for optimal machine learning model training. Researchers widely use this dataset for tasks like age estimation, facial recognition, and other AI applications focused on age-related feature prediction. Each image includes both real and apparent age labels, crowd-sourced from an average of 38 votes per image. This voting process provides highly reliable apparent age estimations with a standard error as low as 0.3, ensuring high-quality labels for precise age prediction tasks.