Detecting Electric Vehicle Charging Stations (EVCS) is attracting increasing attention for autonomous and assisted driving of electric vehicles. One of the main challenges is the scarcity of EVCS detection data. Thus, we propose a camera-based EVCS detection dataset. The dataset is composed of two parts: The first part contains images labeled at a fine-grained level of categories with eight classes. The second part contains images of 13 additional EVCS types annotated at a supercategory level as “electric vehicle charging station”. The images are annotated with bounding boxes and object masks, together with a visibility level for each EVCS instance. For evaluation, protocols considering both fine-grained and supercategory EVCS detection, including fine-tuning, prompt tuning, and zero-shot detection are proposed. Four baseline methods, including both closed-set and open-set detectors, are evaluated. Our evaluation reveals that with our dataset, closed-set detectors can be trained with a reasonable performance. Also, it shows that tuning the open-set detector to work with EVCS at a fine-grained level while preserving its ability to detect common objects forms an interesting research direction. This dataset is the first dataset for camera-based electric vehicle charging station detection. The dataset is accessible here: https://evcs.viscoda.com .