Which Checkpoint? Ask the Others

Selecting a checkpoint for domain-adaptive object detection requires evaluating target-domain performance without target annotations. Existing label-free evaluation criteria based on confidence, box stability, and prediction consistency characterize observed detections, but do not directly account for false negatives caused by missed objects. Trajectory-based Error Decomposition(TED) addresses this limitation by exploiting relationships between checkpoints, using detections at one checkpoint to expose possible misses at another. Our contributions are as follows. (1) TED performs trajectory-based latent error reconstruction by linking detections into shared object candidates and using their presence, absence, and duplicate detections to reconstruct TP-, FN-, and FP-like trajectories. This label-free reconstruction brings possible misses into checkpoint evaluation. (2) TED turns the resulting reconstruction evidence into a selection criterion through common-mode evidence removal by removing candidates whose presence is invariant at the threshold used to collect them, focusing comparison on candidates whose presence differs across checkpoints at that threshold. In experiments, TED achieves the lowest selection RMSE among the evaluated methods under $A{P}_{50}$, $A{P}_{75}$, and mAP. Under $A{P}_{50}$, TED achieves a selection RMSE of 0.963 AP points, compared with 1.768 for the second-best selector.