SlowGuess/NeurIPS_2023_OCR huggingface dataset:
procedure by endowing the classification score with GIoU-awareness, resulting in more accurate ranking in the query ranking layer. Meanwhile, the high-order matching cost selects the queries with both high classification confidence and superior IoU scores as positive samples, effectively suppressing challenging negative predictions with high classification scores but low localization IoU scores. This is achieved by magnifying the advantage of a more accurate localization score using  $\gamma^{\alpha}$ , where  $\gamma$  is the ratio of accurate localization score to less accurate score. Empirical results show significant boosts in  $\mathrm{AP}_{75}$  with GIoU-aware classification loss  $(52.9\% \rightarrow 54.1\%)$  or high-order matching cost design  $(52.9\% \rightarrow 54.0\%)$ . Detailed comparisons are provided in the experiment section.
The concept of GIoU-aware classification loss has been explored in several prior works [31, 37, 76, 10] predating the era of DETR. These works aimed to address the discrepancy between classification scores and localization accuracy. In line with recent concurrent research [1, 45], our method shares the same insight in designing rank-oriented matching cost and loss functions. However, our approach distinguishes itself by emphasizing the ranking aspect and introducing an additional rank-oriented architecture design, which includes the rank-adaptive classification head and query rank layer. Furthermore, our empirical results demonstrate the complementarity between the rank-oriented architecture design and the rank-oriented matching cost and loss design.
We perform object detection experiments using the COCO object detection benchmark [42] with detrex [54] toolbox. Our model is trained on the train set and evaluated on the val set. We adhere to the same experimental setup as the original papers for H-DETR [30] and DINO-DETR [75].
Comparison with competing methods. Table 1 compares Rank-DETR with other high-performing DETR-based methods on the COCO object detection val dataset. The evaluation demonstrates that Rank-DETR achieves remarkable results, attaining an AP score of  $50.2\%$  with only 12 training epochs. This performance surpasses H-DETR [30] by  $+1.5\%$  and outperforms the recent state-of-the-art method DINO-DETR [75] by  $+1.2\%$  AP. Notably, we observe significant improvements in  $\mathrm{AP}_{75}$ , highlighting the advantage of our approach at higher IoU thresholds.
Method	Backbone	#query	#epochs	AP	\( AP_{50} \)	\( AP_{75} \)	\( AP_S \)	\( AP_M \)	\( AP_L \)
Deformable-DETR [78]	R50	300	50	46.9	65.6	51.0	29.6	50.1	61.6
DN-DETR [34]	R50	300	12	43.4	61.9	47.2	24.8	46.8	59.4
DINO-DETR [75]	R50	900	12	49.0	66.6	53.5	32.0	52.3	63.0
H-DETR [30]	R50	300	12	48.7	66.4	52.9	31.2	51.5	63.5
Rank-DETR	R50	300	12	50.2	67.7	55.0	34.1	53.6	64.0
Table 1: Comparison with previous highly performant DETR-based detectors on COCO val2017 with R50.
Improving H-DETR [30]. Table 2 presents a detailed comparison of our proposed approach with the highly competitive H-DETR [30]. The experimental evaluation demonstrates consistent enhancements in object detection performance across various backbone networks and training schedules. For example, under the 12 epochs training schedule, our method achieves superior AP scores of  $50.2\%$ ,  $52.7\%$ , and  $57.3\%$  with ResNet-50, Swin-Tiny, and Swin-Large backbone networks, respectively. These results surpass the baseline methods by  $+1.5\%$ ,  $+2.1\%$ , and  $+1.4\%$ , respectively. Extending the training schedule to 36 epochs consistently improves the AP scores, resulting in  $+1.2\%$  for ResNet-50,  $+1.5\%$  for Swin-Tiny, and  $+1.1\%$  for Swin-Large. The AP improvement in performance is more significant under high IoU thresholds, which outperform the baseline by  $+2.1\%$ ,  $+2.7\%$ , and  $+1.9\%$  in  $\mathrm{AP}_{75}$  with ResNet-50, Swin-Tiny, and Swin-Large, respectively. These findings validate our proposed mechanism's consistent and substantial performance improvements across diverse settings and with different backbone networks, especially under high IoU thresholds. We also validate our performance gain by providing the PR curves under different IoU thresholds in Figure 3a.
Method	Backbone	#epochs	AP	\(AP_{50}\)	\(AP_{75}\)	\(AP_S\)	\(AP_M\)	\(AP_L\)
H-Deformable-DETR	R50	12	48.7	66.4	52.9	31.2	51.5	63.5
Ours	R50	12	50.2	67.7	55.0	34.1	53.6	64.0
H-Deformable-DETR	Swin-T	12	50.6	68.9	55.1	33.4	53.7	65.9
Ours	Swin-T	12	52.7	70.6	57.8	35.3	55.8	67.5
H-Deformable-DETR	Swin-L	12	55.9	75.2	61.0	39.1	59.9	72.2
Ours	Swin-L	12	57.3	75.9	62.9	40.8	61.3	73.2
H-Deformable-DETR	R50	36	50.0	68.3	54.4	32.9	52.7	65.3
Ours	R50	36	51.2	68.9	56.2	34.5	54.9	64.9
H-Deformable-DETR	Swin-T	36	53.2	71.5	58.2	35.9	56.4	68.2
Ours	Swin-T	36	54.7	72.5	60.0	37.7	58.5	69.5
H-Deformable-DETR	Swin-L	36	57.1	76.2	62.5	39.7	61.4	73.4
Ours	Swin-L	36	58.2	76.7	63.9	42.4	62.2	73.6
Table 2: Improving object detection results based on H-DETR.
Method	Backbone	#epochs	AP	\( AP_{50} \)	\( AP_{75} \)	\( AP_S \)	\( AP_M \)	\( AP_L \)
DINO-DETR	R50	12	49.0	66.6	53.5	32.0	52.3	63.0
Ours	R50	12	50.4	67.9	55.2	33.6	53.8	64.2
DINO-DETR	Swin-L	12	56.8	75.4	62.3	41.1	60.6	73.5
Ours	Swin-L	12	57.6	76.0	63.4	41.6	61.4	73.8
Table 3: Improving object detection results based on DINO-DETR.
Improving DINO-DETR [75]. Table 3 shows the results of applying our approach to improve the DINO-DETR [75]. Notably, our method demonstrates an increase of  $+1.4\%$  with the ResNet-50 backbone and  $+0.8\%$  with the Swin-Large backbone. Under the higher IoU setting, our method further obtains  $+1.8\% \mathrm{AP}_{75}$  improvement with ResNet-50 and  $+1.1\%$  with Swin-Large. These results provide evidence for the generalization ability of our approach across different DETR-based models.
We conduct a systematic analysis to assess each proposed component's influence within our method. We followed a step-by-step approach, progressively adding modules on top of the baseline (Table 4a), incorporating each module into the baseline (Table 4b), and subsequently removing each module from our method (Table 4c). This procedure allowed us to understand the contribution of each individual component to the final performance. Furthermore, we conducted statistical and qualitative analyses to comprehensively assess the functionality of each component. We mark the best-performing numbers with colored regions in each table along each column.
Effect of Adding Each Component Progressively. We choose H-DETR with ResNet-50 backbone as our baseline method. By progressively adding the proposed mechanisms on top of the baseline model, it is observed that the performance is steadily increasing, and the best performance is achieved by using all the proposed components (Table 4a). It is also observed that the lowest false negative rate  $(\mathrm{oLRP}_{\mathrm{FN}})$  is achieved when only using the rank-oriented architecture designs.
Rank-adaptive Classification Head (RCH). Tables 4b and 4c demonstrate that rank-adaptive classification head can slightly improve the AP (+0.2% when adding RCH to the H-DETR baseline, comparing row1 and row2 in Table 4b; +0.4% when adding RCH to complete our methods, comparing row1 and row2 in Table 4c). Furthermore, RCH improves  $\mathrm{AP}_{75}$  more than AP.
Query Ranking Layer (QRL). The proposed QRL mechanism effectively integrates ranking information into the DETR architecture, compensating for the absence of sequential handling of queries in attention layers. The detector's performance is also consistently improved by utilizing QRL  $(+0.3\%)$  when adding QRL to the H-DETR baseline;  $+0.7\%$  to complete our method). We further compute the cumulative probability distribution of classification scores of positive and negative queries. QRL yields enhanced classification confidence for matched positive queries (Figure 3b),
RCH	QRL	GCL	HMC	AP	\( AP_{50} \)	\( AP_{75} \)	\( AP_S \)	\( AP_M \)	\( AP_L \)	\( AR_1 \)	\( AR_{10} \)	\( AR_{100} \)	\( AR_S \)	\( AR_M \)	\( AR_L \)	oLRP	oLRPLoc	oLRRFP	oLRRFN
✘	✘	✘	✘	48.7	66.4	52.9	31.2	51.5	63.5	37.2	63.4	68.4	49.7	72.5	85.9	61.0	13.3	24.5	39.5
✓	✘	✘	✘	48.9	66.9	53.3	31.2	52.4	63.7	37.5	64.4	71.2	53.5	75.5	87.1	61.2	13.3	24.0	39.2
✓	✓	✘	✘	49.3	67.3	53.7	32.4	52.2	63.4	37.8	65.0	71.7	54.6	75.7	88.4	60.8	13.3	24.1	38.6
✓	✓	✓	✘	49.8	67.5	54.3	33.3	53.4	63.7	37.9	65.1	71.8	54.8	76.1	87.8	60.7	13.0	23.5	39.3
✓	✓	✓	✓	50.2	67.7	55.0	34.1	53.6	64.0	38.1	64.9	71.6	56.5	75.8	86.4	60.4	12.9	22.4	39.3
(a) Effect of gradually adding modules on the baseline.
RCH	QRL	GCL	HMC	AP	\( AP_{50} \)	\( AP_{75} \)	\( AP_S \)	\( AP_M \)	\( AP_L \)	\( AR_1 \)	\( AR_{10} \)	\( AR_{100} \)	\( AR_S \)	\( AR_M \)	\( AR_L \)	oLRP	oLRPLoc	oLRPFP	oLRPFN
X	X	X	X	48.7	66.4	52.9	31.2	51.5	63.5	37.2	63.4	68.4	49.7	72.5	85.9	61.0	13.3	24.5	39.5
✓	X	X	X	48.9	66.9	53.3	31.2	52.4	63.7	37.5	64.4	71.2	53.5	75.5	87.1	61.2	13.3	24.0	39.2
X	✓	X	X	49.0	67.2	53.2	32.3	51.9	63.5	37.9	64.8	71.5	55.1	75.6	87.5	61.1	13.4	23.8	39.2
X	X	✓	X	49.4	67.0	54.1	32.0	52.8	64.0	37.9	64.9	71.7	55.4	75.7	88.5	60.7	12.9	22.5	39.8
X	X	X	✓	49.3	67.3	54.0	31.8	52.4	63.4	37.7	64.4	71.1	53.7	74.9	85.8	61.1	13.2	23.8	39.2
(b) Effect of incorporating each module on the baseline.
RCH	QRL	GCL	HMC	AP	\( AP_{50} \)	\( AP_{75} \)	\( AP_S \)	\( AP_M \)	\( AP_L \)	\( AR_1 \)	\( AR_{10} \)	\( AR_{100} \)	\( AR_S \)	\( AR_M \)	\( AR_L \)	oLRP	oLRPLoc	oLRPFP	oLRPFN
✓	✓	✓	✓	50.2	67.7	55.0	34.1	53.6	64.0	38.1	64.9	71.6	56.5	75.8	86.4	60.4	12.9	22.4	39.3
✘	✓	✓	✓	49.8	67.3	54.5	33.5	53.4	63.6	38.1	64.9	71.5	55.9	75.3	86.6	60.5	12.9	22.9	39.6
✓	✘	✓	✓	49.5	67.4	54.2	33.1	52.8	63.5	38.0	64.7	71.6	55.8	75.4	85.9	60.9	12.9	24.6	39.3
✓	✓	✘	✓	49.5	67.6	54.1	32.4	52.6	64.3	37.9	64.3	71.0	54.4	75.1	85.4	60.8	13.4	23.7	38.7
✓	✓	✓	✘	49.8	67.5	54.3	33.3	53.4	63.7	37.9	65.1	71.8	54.8	76.1	87.8	60.7	13.0	23.5	39.3
(c) Effect of removing each module in our method.
Table 4: Ablation experiments based on H-DETR + R50. RCH: rank-adaptive classification head. QRL: query rank layer. GCL: GIoU-aware classification loss. HMC: high-order matching cost.
Classification loss target	AP	\( AP_{50} \)	\( AP_{75} \)	\( AP_S \)	\( AP_M \)	\( AP_L \)	\( AR_1 \)	\( AR_{10} \)	\( AR_{100} \)	\( AR_S \)	\( AR_M \)	\( AR_L \)	oLRP	oLRP Loc	oLRP FP	oLRP FN
\( t = IoU(\hat{b},\mathbf{b})^{0.5} \)	50.1	67.6	54.7	32.6	53.4	64.5	38.2	64.9	71.5	55.5	75.4	86.4	60.4	12.9	22.5	39.4
\( t = IoU(\hat{b},\mathbf{b})^1 \)	50.0	67.3	54.7	34.0	53.6	64.8	38.1	65.2	71.7	56.7	75.5	85.8	60.6	12.9	22.9	39.6
\( t = IoU(\hat{b},\mathbf{b})^2 \)	49.5	66.0	54.0	32.5	53.3	64.1	37.8	64.2	70.9	53.8	75.3	86.1	61.0	12.3	23.1	41.0
\( t = [(GIoU(\hat{b},\mathbf{b}) + 1)/2]^{0.5} \)	49.9	67.9	54.3	32.8	53.2	64.3	37.9	64.9	71.6	55.8	75.5	86.1	60.6	13.2	23.3	38.7
\( t = [(GIoU(\hat{b},\mathbf{b}) + 1)/2]^1 \)	50.2	67.7	55.0	34.1	53.6	64.0	38.1	64.9	71.6	56.5	75.8	86.4	60.4	12.9	22.4	39.3
\( t = [(GIoU(\hat{b},\mathbf{b}) + 1)/2]^2 \)	50.1	67.4	54.9	33.3	53.5	64.3	38.1	64.9	71.5	56.3	75.3	85.5	60.4	12.9	22.9	39.3
(a) Effect of the GIoU-aware classification loss target formulation.
Matching cost	AP	\( AP_{50} \)	\( AP_{75} \)	\( AP_S \)	\( AP_M \)	\( AP_L \)	\( AR_1 \)	\( AR_{10} \)	\( AR_{100} \)	\( AR_S \)	\( AR_M \)	\( AR_L \)	oLRP	oLRPLoc	oLRPFP	oLRPFN
\( \hat{p}[c] \cdot IoU^1 \)	48.3	67.9	51.8	31.4	51.3	62.3	37.8	62.9	67.7	52.8	70.9	82.6	60.4	13.5	22.8	38.4
\( \hat{p}[c] \cdot IoU^2 \)	49.5	68.3	53.5	32.5	52.4	63.9	38.1	63.9	69.6	54.5	73.3	84.8	60.2	13.3	23.1	37.8
\( \hat{p}[c] \cdot IoU^3 \)	50.0	68.1	54.2	32.9	53.2	64.4	37.9	64.7	70.9	55.3	74.9	85.9	60.4	12.9	22.9	39.1
\( \hat{p}[c] \cdot IoU^4 \)	50.2	67.7	55.0	34.1	53.6	64.0	38.1	64.9	71.6	56.5	75.8	86.4	60.4	12.9	22.4	39.3
\( \hat{p}[c] \cdot IoU^5 \)	50.0	67.1	54.9	32.4	53.5	64.5	38.1	65.0	71.8	55.1	75.9	86.5	60.7	12.8	23.4	39.4
\( \hat{p}[c] \cdot IoU^6 \)	50.0	66.6	54.9	33.9	53.3	64.5	38.0	65.4	72.5	56.3	76.5	87.7	61.0	12.8	23.0	40.3
\( \hat{p}[c] \cdot (\frac{GIoU+1}{2})^1 \)	46.6	65.9	49.9	31.0	49.8	59.9	37.2	60.3	63.4	47.9	66.9	77.5	60.6	13.8	22.9	38.2
\( \hat{p}[c] \cdot (\frac{GIoU+1}{2})^2 \)	47.8	67.2	51.0	31.6	50.8	61.8	37.6	61.9	65.9	50.8	68.6	80.4	60.4	13.5	22.5	38.2
\( \hat{p}[c] \cdot (\frac{GIoU+1}{2})^3 \)	48.0	67.6	51.3	31.1	51.3	62.2	37.6	61.8	65.8	50.2	69.3	79.8	59.9	13.4	21.9	37.9
\( \hat{p}[c] \cdot (\frac{GIoU+1}{2})^4 \)	49.1	68.2	53.0	32.2	52.1	63.5	37.7	63.9	69.2	55.0	72.2	83.6	60.1	13.3	22.9	37.7
\( \hat{p}[c] \cdot (\frac{GIoU+1}{2})^5 \)	49.4	68.5	53.0	32.6	52.7	63.7	38.2	63.9	69.1	54.0	72.7	83.8	60.0	13.2	21.6	38.4
\( \hat{p}[c] \cdot (\frac{GIoU+1}{2})^6 \)	49.8	68.4	54.1	32.9	53.4	63.9	38.2	64.8	70.6	55.3	74.8	85.5	60.1	13.1	23.7	37.7
\( \hat{p}[c] \cdot (\frac{GIoU+1}{2})^7 \)	49.9	67.9	54.2	32.6	53.0	64.6	38.1	64.5	70.5	55.0	74.5	85.7	60.3	13.0	23.0	38.7
\( \hat{p}[c] \cdot (\frac{GIoU+1}{2})^8 \)	50.0	67.8	54.6	33.9	53.6	64.7	37.9	65.0	71.2	55.5	75.2	86.5	60.4	12.8	23.8	38.8
\( \hat{p}[c] \cdot (\frac{GIoU+1}{2})^9 \)	49.8	67.3	54.4	33.3	53.2	63.7	38.1	65.1	71.7	56.4	75.7	87.1	60.5	12.9	23.9	39.0
(b) Effect of the matching cost formulation.
Table 5: The influence of classification loss target and matching cost function choice.
while the classification confidence for unmatched queries is effectively suppressed (Figure 3c), thereby ranking true predictions higher than potential false predictions. This phenomenon is further apparent from the  $\mathrm{oLRP_{FP}}$  results showcased in Table 4b, which is reduced from  $24.5\%$  to  $23.8\%$  by using QRL, reducing the false positive rates. These results are in accordance with our design intent.
GIoU-aware Classification Loss (GCL). Table 4 also shows the effectiveness of the proposed GIoU-aware classification loss, which gains  $0.7\%$  mAP over the vanilla baseline by comparing row1 and row4 in Table 4b and  $0.7\%$  mAP increase by comparing row1 and row4 in Table 4c. We
Figure 3: (a) Comparing the PR-curves between baseline and our method under different IoU thresholds. (b) Density distribution of the classification scores on the matched queries with or without QRL. (c) Density distribution of the classification scores on the unmatched queries with or without QRL.
(a) Layer #1
Figure 4: Cumulative distribution of the IoU scores on the unmatched queries with or without HMC.
(b) Layer #2
(c) Layer #3
(d) Layer #4
(e) Layer #5
(f) Layer #6
also ablate the formulation of the learning target  $t$  in Eq. (6) in Table 5a. The results show that the performance of adopting IoU (and its exponent) as the optimization target is inferior to use  $(\mathrm{GIoU} + 1) / 2$  because GIoU can better model the relationship of two non-overlapped boxes. We use  $(\mathrm{GIoU} + 1) / 2$  rather than GIoU because  $-1 < \mathrm{GIoU} \leq 1$  and  $0 < (\mathrm{GIoU} + 1) / 2 \leq 1$ .
High-order Matching Cost (HMC). We also show how the high-order matching cost affects the overall performance in Table 4. The HMC can also significantly improve the overall performance of the object detector (+0.6% when comparing row1 and row5 in Table 4b, +0.4% when comparing row1 and row5 in Table 4c). We further ablate the formulation of the matching cost. As illustrated in Table 5b, a high-order exponent IoU can consistently improve the performance and achieve a peak when the power is 4. Using a high-order exponent can suppress the importance of the predicted boxes with low IoU. We can also observe the same trend from Table 5b when replacing IoU with (GIoU+1)/2, but the latter practice has a slightly inferior performance.
HMC Suppresses the Overlap between Negative Query and Ground Truth. Figure 4 illustrates the cumulative probability distribution of the IoU of unmatched queries. The IoU of each unmatched query is defined as the largest IoU between it and all ground truth boxes. As shown in Figure 4, the adoption of HMC can decrease the IoU between unmatched queries and all the ground truth bounding boxes, effectively pushing the negative queries away from the ground truth boxes. Furthermore, this phenomenon is increasingly remarkable in the latter Transformer decoder layers.
Comparison with Varifocal loss. In order to assess the effectiveness of the proposed GIoU-aware classification loss (GCL, Eq. (6)), we compare it with varifocal loss (VFL) [76] due to their similar mathematical formulations. Following VFL [76], we utilize the training target  $t = \mathrm{IoU}$  in Eq. (7). To simplify the comparison and focus on the impact of GCL, we conduct the evaluation without HMC (row3 in Table 4a). Our method, leveraging the GCL, achieves a mAP of  $49.8\%$  (row4 in Table 4a), whereas VFL achieves only  $49.5\%$  mAP. The primary distinctions between GCL and VFL lie in the optimization target. By employing the normalized GIoU as the training target, our approach better models the distance between two non-overlapping boxes, leading to improved performance. In addition, VFL removes scaling factors on positive samples, as they are rare compared to negatives in CNN-based detectors. However, for DETR-based detectors, where positive examples are relatively more abundant, we empirically show that retaining a scaling factor can enhance performance.
Method	Backbone	Params(M)	FLOPs(G)	Training Cost (min)	Testing FPS (img/s)	AP
H-DETR	R50	47.56	280.30	69.8	19.2	48.7
Ours	R50	49.10	280.60	71.8	19.0	50.2
Table 6: Computational efficiency analysis for our method.
Figure 5: Illustration of positive boxes and negative box of H-DETR (row1) and Rank-DETR (row2). The green, red and yellow boxes (scores) refer to the positive, negative, and ground truth boxes (scores), respectively.
Computational Efficiency. In Table 6, we provide comprehensive data on the number of parameters, computational complexity measured in FLOPs, training time per epoch, inference frames per second (FPS), and Average Precision performance, for both the H-DETR baseline and our approach. These assessments were conducted on RTX 3090 GPUs, allowing us to evaluate testing and training efficiency. The results unequivocally highlight a substantial enhancement in detection performance achieved by our proposed method, with only a slight increase in FLOPs and inference latency. Considering the effectiveness and efficiency, our method has the potential to be adapted into 3D object detection [55, 29], semantic segmentation [39, 33] tasks, or other applications [23, 24, 25, 21, 22].
Qualitative Analysis. Figure 5 visualizes the predicted bounding boxes and their classification confidence scores of both the matched positive queries and the unmatched hard negative queries, respectively. We find that, compared to the baseline method (row1), the proposed approach (row2) effectively promotes the classification scores of positive samples, while that of hard negative queries is rapidly suppressed, progressing layer by layer. These qualitative results further illustrate how the proposed approach achieves high performance by decreasing the false positive rate.
This paper presents a series of simple yet effective rank-oriented designs to boost the performance of modern object detectors and result in the development of a high-quality object detector named Rank-DETR. The core insight behind the effectiveness lies in establishing a more precise ranking order of predictions, thereby ensuring robust performance under high IoU thresholds. By incorporating accurate ranking order information into the network architecture and optimization procedure, our approach demonstrates improved performance under high IoU thresholds within the DETR framework. While there remains ample scope to explore leveraging rank-oriented designs, we hope that our initial work serves as an inspiration for future efforts in building high-quality object detectors.
This work is supported by National Key R&D Program of China under Grant 2022ZD0114900 and 2018AAA0100300, National Nature Science Foundation of China under Grant 62071013 and 61671027. We also appreciate Ding Jia, Yichao Shen, Haodi He and Yutong Lin for their insightful discussions, as well as the generous donation of computing resources by High-Flyer AI.
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