Object detection is decoupled into two subtasks, namely, classification and localization, in mainstream detectors. Each task possesses a separate detection subnetwork and is trained with an independent loss function. In this way, the correlation between classification and localization is disregarded, and thus the classification score predicted by the model is not capable of representing the localization quality of the prediction box. Consequently, predictions of high localization quality may be suppressed by their poorly localized counterparts in the procedure of Non-Maximum Suppression (NMS), inducing precision degradation. To tackle this problem, a consistency loss is proposed to constrain the rank similarity between the classification score predicted by the model and the localization quality in training process to reinforce about their consistency. Based on FCOS-ResNet50 model and PASCAL VOC dataset, the proposed loss function brings about 1.3 percentage points of mAP0.5, 4.3 percentage points of mAP75, and 5.4 percentage points of mAP90 improvements.