# End-to-End Object Detection with Enhanced Positive Sample Filter
## 4 Experiments
### Comparisons with State-of-the-Arts  
To demonstrate the superiority of our proposed methods, we present comparisons with the state-of-the-art methods in this section. **Comparisons on COCO:** We compared the proposed detector with other state-of-the-art object detectors on the COCO val2017 set. Table 1 summarizes the comparisons. Our detector obtained superior performances compared to the most related NMS-free detector DeFCN and OneNet. Additionally, our proposed NMS-free detector outperforms other NMS-based detectors, e.g., surpassing Faster RCNN by 1.8% AP, FCOS by 0.6% AP and RetinaNet by 3.3% AP. These results firmly demonstrate the effectiveness of our proposed methods. **Comparisons on PASCAL VOC:** The proposed detector is compared with the state-of-the-art object detectors on the PASCAL VOC2007 test set in Table 2. For fair comparisons, all models were trained on the PASCAL VOC2007 trainval set and tested on the PASCAL VOC2007 test set. It can be seen that our proposed detector surpasses other mainstream object detectors by a remarkable margin, which further validates the effectiveness of our proposed methods. **Comparisons on CrowdHuman:** In crowded scenarios, end-to-end detection without NMS becomes more challenging. Compared with COCO, the CrowdHuman dataset is of much higher crowdedness, and contains approximately 22.6 pedestrians on average per image against 7.2 in COCO. To further show the performance of our proposed methods in crowd handling, we evaluated our model on the CrowdHuman dataset. As shown in Table 3, our end-to-end detector significantly outperforms several state-of-the-art detectors. Compared with one-stage NMS-free detectors DeFCN and OneNet, our detector achieves {2.9% mMR, 1.7% AP, 1.2% Recall} and {2.2% mMR, 0.1% AP, 0.1% Recall} gains, respectively. **Comparisons on Caltech:** To further evaluate the effectiveness and robustness of the proposed detector, it was compared with the state-of-the-art on the Caltech test set. Following the official setting, we present comparisons in terms of mMRs on three subsets with different difficulty levels. The reasonable set (R) and the reasonable heavy occlusion set (HO) contain pedestrian objects with visibility of [0.65, 1] and [0.2, 0.65], respectively. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Methods** & **Epochs** & **AP** & **AP\({}_{\text{50}}\)** & **AP\({}_{\text{75}}\)** & **AP\({}_{\text{5}}\)** & **AP\({}_{M}\)** & **AP\({}_{L}\)** \\ \hline \multicolumn{7}{l}{_NMS-based Detectors:_} \\ Faster RCNN [1] & 36 & 40.2 & 61.0 & 43.8 & 24.2 & 43.5 & 52.0 \\ RetinaNet [2] & 36 & 38.7 & 58.0 & 41.5 & 23.3 & 42.3 & 50.3 \\ FCOS [3] & 36 & 41.4 & 59.9 & 44.8 & 26.1 & 44.9 & 52.7 \\ \hline \multicolumn{7}{l}{_NMS-free Detectors:_} \\ OneNet [11] & 36 & 38.9 & 57.3 & 42.3 & 23.9 & 41.9 & 49.5 \\ DeFCN [12] & 36 & 41.4 & 59.5 & 45.7 & 26.1 & 44.9 & 52.0 \\ Ours & 36 & 42.0 & 60.0 & 46.3 & 26.3 & 45.2 & 52.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparisons with the state-of-the-art object detectors on COCO val2017 set. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Methods** & **NMS** & **mAP** \\ \hline Faster RCNN [1] & ✓ & 73.1 \\ RetinaNet [2] & ✓ & 74.0 \\ SSD [24] & ✓ & 71.6 \\ FCOS [3] & ✓ & 73.0 \\ DeFCN [12] & \(\times\) & 73.1 \\ Ours & \(\times\) & 74.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparisons with the state-of-the-art object detectors on PASCAL VOC2007 test set. ‘NMS’ column indicates whether the method uses NMS.