  Automated visual firearms classification from RGB images is an important
real-world task with applications in public space security, intelligence
gathering and law enforcement investigations. When applied to images massively
crawled from the World Wide Web (including social media and dark Web sites), it
can serve as an important component of systems that attempt to identify
criminal firearms trafficking networks, by analyzing Big Data from open-source
intelligence. Deep Neural Networks (DNN) are the state-of-the-art methodology
for achieving this, with Convolutional Neural Networks (CNN) being typically
employed. The common transfer learning approach consists of pretraining on a
large-scale, generic annotated dataset for whole-image classification, such as
ImageNet-1k, and then finetuning the DNN on a smaller, annotated,
task-specific, downstream dataset for visual firearms classification. Neither
Visual Transformer (ViT) neural architectures nor Self-Supervised Learning
(SSL) approaches have been so far evaluated on this critical task..
