  In the realm of autonomous transportation, there have been many initiatives
for open-sourcing self-driving cars datasets, but much less for alternative
methods of transportation such as trains. In this paper, we aim to bridge the
gap by introducing FRSign, a large-scale and accurate dataset for vision-based
railway traffic light detection and recognition. Our recordings were made on
selected running trains in France and benefited from carefully hand-labeled
annotations. An illustrative dataset which corresponds to ten percent of the
acquired data to date is published in open source with the paper. It contains
more than 100,000 images illustrating six types of French railway traffic
lights and their possible color combinations, together with the relevant
information regarding their acquisition such as date, time, sensor parameters,
and bounding boxes. This dataset is published in open-source at the address
\url{https://frsign.irt-systemx.fr}. We compare, analyze various properties of
the dataset and provide metrics to express its variability. We also discuss
specific challenges and particularities related to autonomous trains in
comparison to autonomous cars.
