DAMM for the detection and tracking of multiple animals within complex social and environmental settings.

Present deep learning tools for animal localization require extensive laborious annotation and time-consuming training for the creation of setup-specific models, slowing scientific progress. Additionally, the effectiveness of these tools in naturalistic settings is impeded by visual variability of objects and environmental diversity, hindering animal detection in complex environments. Our study presents the 'Detect Any Mouse Model' (DAMM), a robustly validated object detector designed for localizing mice in complex environments. DAMM excels in generalization, robustly performing with zero to minimal additional training on previously unseen setups and multi-animal scenarios. Its integration with the SORT algorithm permits robust tracking, competitively performing with keypoint-estimation-based tools. These developments, along with our dissemination of DAMM, mark a significant step forward in streamlining ethologically-relevant animal behavioral studies.