7 · 只改一个条件，并用结果回答 · 实现代码

# Optional one-condition experiments: preserve the baseline arrays.
scores_rank_test = SCORES.copy()
scores_rank_test[1] = .60
print("P2 score 0.60, AP50:", evaluate_single_class(PRED, scores_rank_test, GT, .5)["ap"])

boxes_localization_test = PRED.copy()
boxes_localization_test[2, [0, 2]] -= .4
print("P3 shifted left 0.4, AP75:", evaluate_single_class(boxes_localization_test, SCORES, GT, .75)["ap"])

for threshold in [.50, .70]:
    candidate_keep, _ = class_aware_nms(NEIGHBOR_GT, NEIGHBOR_SCORES, [0, 0], threshold)
    print("Neighbor NMS", threshold, "keeps", (candidate_keep + 1).tolist(),
          "AP50", evaluate_single_class(NEIGHBOR_GT[candidate_keep],
                  NEIGHBOR_SCORES[candidate_keep], NEIGHBOR_GT, .5)["ap"])