Given the huge level of firearms trafficking in Europe, govern- ments, and in particular interior ministries, are actively engaged in developing artificial intelligence tools to identify firearms more effectively. Indeed, confronted with the huge number of different possible firearms, when a weapon is seized, police officers are not always able to identify it and in particular its illegal nature or the dangerousness of owning it. Consequently, we aim to develop a recognition approach based on a neural network. To this end, a training database is required. As firearms can be hierarchically classified, we have decided to build a hierarchical database. This article presents the Ceasefire Hierarchical Weapon Dataset, an RGB image dataset of firearms tailored for fine-grained image classi- fication. It contains 260 classes ranging from 25 to hundreds of images per class, with a total of 40,789 images. In addition, a 4-level hierarchy (family, group, type, model) is provided and validated by forensic experts. The dataset was built from more than 23,000 images acquired at the Toulouse forensics laboratory (France) using frames extracted from videos filmed on site, and completed with the existing Firearm 14k dataset enriched with 2,526 downloaded images from the Web. Results of hierarchical classification using the proposed dataset are also reported.