  In the last decade, crowd counting and localization attract much attention of
researchers due to its wide-spread applications, including crowd monitoring,
public safety, space design, etc. Many Convolutional Neural Networks (CNN) are
designed for tackling this task. However, currently released datasets are so
small-scale that they can not meet the needs of the supervised CNN-based
algorithms. To remedy this problem, we construct a large-scale congested crowd
counting and localization dataset, NWPU-Crowd, consisting of 5,109 images, in a
total of 2,133,375 annotated heads with points and boxes. Compared with other
real-world datasets, it contains various illumination scenes and has the
largest density range (0~20,033). Besides, a benchmark website is developed for
impartially evaluating the different methods, which allows researchers to
submit the results of the test set. Based on the proposed dataset, we further
describe the data characteristics, evaluate the performance of some mainstream
state-of-the-art (SOTA) methods, and analyze the new problems that arise on the
new data. What's more, the benchmark is deployed at
\url{https://www.crowdbenchmark.com/}, and the dataset/code/models/results are
available at \url{https://gjy3035.github.io/NWPU-Crowd-Sample-Code/}.
