UCF-QNRF was released by the University of Florida in 2018 and includes a total of 1,535 crowd images, including 1,201 images in the training set and 334 images in the test set. In terms of the number of annotations, UCF-QNRF is the largest dataset to date and can be used to train and evaluate large-scale crowd counting models.

Compared with similar datasets, UCF-QNRF contains large-scale annotated human bodies in multiple scenes, multiple perspectives, multiple light and density variations, making it very suitable for training deep convolutional neural networks.

The images in the UCF-QNRF dataset are all high-definition large images.The image resolution is 2013*2902.In addition, it also contains real outdoor scenes from all over the world, such as buildings, vegetation, sky and roads, which is of great significance for studying the population density in different regions.