  We introduce a new dataset for image-based parking space occupancy
classification: ACPDS. Unlike in prior datasets, each image is taken from a
unique view, systematically annotated, and the parking lots in the train,
validation, and test sets are unique. We use this dataset to propose a simple
baseline model for parking space occupancy classification, which achieves 98%
accuracy on unseen parking lots, significantly outperforming existing models.
We share our dataset, code, and trained models under the MIT license.
