Table 1.
Two mice home cage | Four mice operant chamber | |||||
---|---|---|---|---|---|---|
MOTA | MOTP | mAP | MOTA | MOTP | mAP | |
AlphaTracker | 82.2 | 86.2 | 87.2 | 84.0 | 87.2 | 85.6 |
DeepLabCut | 40.6 | 77.6 | 14.1 | 71.4 | 86.5 | 68.0 |
SLEAP | 73.6 | 76.7 | 26.8 | 77.9 | 86.5 | 83.9 |
AlphaTracker, DeepLabCut, and SLEAP were evaluated on two datasets: two mice in a home cage and four mice in an operant chamber. Each model was trained on 600 annotated frames and evaluated on 200 frames with human-labeled ground truth. The evaluation results show that AlphaTracker outperforms DeepLabCut and SLEAP in both datasets, achieving higher keypoint detection accuracy (mAP) and tracking consistency (MOTA and MOTP).