XAI-DETR: Explainable AI Tool for Detection Transformers

Deep learning has significantly advanced object detection, with transformer-based models achieving notable improvements in both accuracy and efficiency. Despite their success, these models are inherently complex, making their decision-making processes difficult to interpret. This lack of transparency poses challenges, particularly in critical applications where inter-pretability is essential. Although various Explainable AI (XAI) methods have been developed to address these challenges, many of them are not always straightforward for users to implement and interpret. This can make it more difficult for users to fully benefit from these methods. To improve accessibility and encourage wider adoption, this paper introduces XAI-DETR, an Explainable AI tool designed for seamless integration with transformer-based models used in object detection. Additionally, a novel evaluation method is presented to assess the faithfulness of the explanations provided by the tool, offering users greater confidence in the reliability and utility of the generated insights. The code is available at https://github.com/SuciuCaesar12/xaidetr.