Download Prophesee’s GEN1 Automotive Detection Dataset,
the largest Event-Based Dataset to date. 

The dataset was recorded using a PROPHESEE GEN1 sensor with a resolution of 304×240 pixels, mounted on a car dashboard. The labels were obtained using the gray level estimation feature of the ATIS camera by labelling manually.

It contains 39 hours of open road and various driving scenarios ranging from urban, highway, suburbs and countryside scenes.

Manual bounding box annotations are available for two classes are present: pedestrians and cars. (Truck and buses are not labelled).



The dataset is split between train, test and val folders. 

Files consist of 60 seconds recordings that were cut from longer recording sessions. Cuts from a single recording session are all in the same training split.

Each dat file is a binary file in which events are encoded using 4 bytes (unsigned int32) for the timestamps and 4 bytes (unsigned int32) for the data, encoding is little-endian ordering.

The data is composed of 14 bits for the x position, 14 bits for the y position and 1 bit for the polarity (encoded as -1/1).

Annotations use the numpy format and can simply be loaded form python using numpy boxes = np.load(path)

Boxes have the following fields

  • x abscissa of the top left corner in pixels
  • y ordinate of the top left corner in pixels
  • w width of the boxes in pixel
  • h height of the boxes in pixel
  • ts timestamp of the box in the sequence in microseconds
  • class_id 0 for cars and 1 for pedestrians

Alongside the dataset, we also release sample code in Python to conveniently read the events and annotations: Github repository

For more information on how the dataset was created, please refer to the paper. 


When using the data in an academic context, please cite the following paper:

Pierre de Tournemire, Davide Nitti, Etienne Perot and Amos Sironi “A Large Scale Event-based Detection Dataset for Automotive”.

Preprint on ArXiv