YOLOv10: Real-Time End-to-End Object Detection
Authors: Ao Wang
Hui Chen
Lihao Liu
Kai CHEN
Zijia Lin
Jungong Han
Guiguang Ding
Venue: NeurIPS 2024 poster (2024)
Keywords: YOLO
object detection
computer vision
Primary area: deep_learning_architectures
TL;DR: A YOLO model for object detection

Abstract: Over the past years, YOLOs have emerged as the predominant paradigm in the field of real-time object detection owing to their effective balance between computational cost and detection performance. Researchers have explored the architectural designs, optimization objectives, data augmentation strategies, and others for YOLOs, achieving notable progress. However, the reliance on the non-maximum suppression (NMS) for post-processing hampers the end-to-end deployment of YOLOs and adversely impacts the inference latency. Besides, the design of various components in YOLOs lacks the comprehensive and thorough inspection, resulting in noticeable computational redundancy and limiting the model's capability. It renders the suboptimal efficiency, along with considerable potential for performance improvements. In this work, we aim to further advance the performance-efficiency boundary of YOLOs from both the post-processing and the model architecture. To this end, we first present the consistent dual assignments for NMS-free training of YOLOs, which brings the competitive performance and low inference latency simultaneously. Moreover, we introduce the holistic efficiency-accuracy driven model design strategy for YOLOs. We comprehensively optimize various components of YOLOs from both the efficiency and accuracy perspectives, which greatly reduces the computational overhead and enhances the capability. The outcome of our effort is a new generation of YOLO series for real-time end-to-end object detection, dubbed YOLOv10. Extensive experiments show that YOLOv10 achieves the state-of-the-art performance and efficiency across various model scales. For example, our YOLOv10-S is 1.8$\times$ faster than RT-DETR-R18 under the similar AP on COCO, meanwhile enjoying 2.8$\times$ smaller number of parameters and FLOPs. Compared with YOLOv9-C, YOLOv10-B has 46\% less latency and 25\% fewer parameters for the same performance. Code and models are available at https://github.com/THU-MIG/yolov10.

YOLOv10: Real-Time End-to-End Object Detection
Ao Wang' Hui Chen Lihao Liu'
Kai Chen
Zijia Lin' Jungong Han Guiguang Ding
School of Software, Tsinghua University BBNRist, Tsinghua University Department of Automation, Tsinghua University
wa220mails twinghua......c... edu en hui cminnmmmal. tsinghua. edu. cn Iinzifia07etssinguuu org. (louisliu2048, chenka12010. jungonghan77) @gmail com dinggg@tsinghua. edu.
55.0
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YoL
VOLOx6-e5
VOLOv7
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YOLOVS
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YOLOsk
VOLOv9
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YOLOv9
PPYOLOE
PFYOLOE
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YOLO MS
YOLO-MI MS
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RE-DETR
RE-DETR
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20
Latency (ms)
Number of Paramelurs (M)
Figure 1: Comparisons with others in terms of latency-accurant (left) and size-accuracy (right) trade-offs. We measure the end-to-end latency using the official pre-trained models.
Abstract
Over the past years, YOLOs have emerged the predominant paradigm the field of real-time object detection owing to their effective balance between computa- tional cost and detection performance. Researchers have explored the architectural designs, optimization objectives, data augmentation strategies, and others for YO- LOs, achieving notable progress. However, the reliance on the non max-max suppression (NMS) for post-processing hampers the end-to-end deployment of YOLOs and adversely impacts the inference latency. Besides, the design various components in YOLOs lacks the comprehensive and thorough inspection, resulting in noticeable computational redundancy and limiting the model's capability. ren- ders the suboptimal efficiency, along with considerable potential for performance improvements. In this work, we aim to further advance the performance effficiecy boundary of YOLOs from both the post-processing and the model architecture. To this end, we first present the consistent dual assignments for NMS-free training of YOLOs, which brings the competitive performance and low inference latency simultaneously. Moreover, we introduce the holistic efficiency-accurage driven model design strategy for YOLOs. We comprehensively optimize various compo- nents of YOLOs from both the efficiency and accuracy perspectives, which greatly reduces the computational overhead and enhances the capability. The outcome of our effort is new generation of YOLO series for real-time end-to-end object detection, dubbed YOLOv10. Extensive experiments show that YOLOv achieves the state-of ofiee-of-hhee ohettheart art performance and efficiency across various model scales. For
"Corresponding author.
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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example, our YOLOv10-S is 1.8x faster than RT-DETR-RIB under the simi- lar AP on COCO, meanwhile enjoying 2.8x smaller number of parameters and FL.OPs. Compared with YOLOv9-C, YOLOv10-B B has 46% less latency and 25% fewer parameters for the same performance. Code and models are available at https://giie http://gtttub.... wtw.ub..oom//a/m MC/m/TUUUMG//ooovll/
Introduction
Real-time object detection has always been a focal point of research in the area of computer vision, which aims to accurately predict the categories and positions of objects in an image under low latency. It is widely adopted in various practical applications, including autonomous driving ( robot navigation [12), and object tracking (72), etc. In recent years, researchers have concentrated
on devising CNN-based object detectors to achieve real-time detection 19]
23
48
49
50 57
B Among them, YOLOs have gained increasing popularity due to their adept balance between
17 29 The detection pipeline of YOLOs consists of two parts: the model forward process and the NMS post-processing. However, both of them still have deficiencies, resulting in suboptimal accuracy-lateng boundaries.
performance and efficiency [2] 20 29 20
21
65
60
70
8
7
Specifically, YOLOs usually employ one-to-many label assignment strategy during training, whereby one ground-truth object corresponds to multiple positive samples. Despite yielding superior perfor- mance, this approach necessitates NMS to select the best positive prediction during inference. This slows down the inference speed and renders the performance sensitive to the hyperparameters of NMS, thereby preventing YOLOS from achieving optimal end-to-end deployment (78). One line tackle this issue is to adopt the recently introduced end-to-end DETR architectures 4 81 81 73] 30 36 42 67]. For example, RT-DETR (78) presents an efficient hybrid encoder and uncertainty-minn minimal query selection, propelling DETRs into the realm of real-time applications. Nevertheless, when considering only the forward process of model during deployment, the efficiency of the DETRs still has room for improvements compared with YOLOs. Another line is to explore end-to-end detection for CNN- based detectors. which typically leverages one-to-one assignment strategies suppress the redundant predictions 6 55 66 80 I7I. However, they usually introduce additional inference overhead or achieve suboptimal performance for YOLOs.
Furthermore, the model architecture design remains a fundamental challenge for YOLOs, which exhibits an important impact the accuracy and speed [50, [7] 71 8. To achieve more efficient and effective model architectures, researchers have explored different design strategies. Various primary computational units are presented for the backbone enhance the feature extraction ability,
etc. For the neck, PAN 337 BiC 22 GD 600 and RepGFPN 771, etc., are explored enhance the multi-scale feature
including DarkNet 48
49 50]. CSPNet 2) EfficientRep [29] and ELAN 62 64
fusion. Besides, model scaling strategies 162 61 and re-parameterization [I] 22 techniques are also investigated. While these efforts have achieved notable advancements, a comprehensive inspection for various components in YOLOs from both the efficiency and accuracy perspectives is still lacking. As a result, there still exists considerable computational redundancy within YOLOs, leading to inefficient parameter utilization and suboptimal efficiency. Besides, the resulting constrained model capability also leads to inferior performance, leaving ample room for accuracy improvements.
In this work, we aim to address these issues and further advance the accuracy-speed boundaries of YOLOs. We target both the post-processing and the model architecture throughout the detection pipeline. To this end, we first tackle the problem of redundant predictions in the post-processing by presenting consistent dual assignments strategy for NMS-free YOLOs with the dual label assignments and consistent matching metric. It allows the model to enjoy rich and harmonious supervision during training while eliminating the need for NMS during inference, leading to com- petitive performance with high efficiency. Secondly, we propose the holistic sndeiency-accurran driven model design strategy for the model architecture by performing the comprehensive inspection for various components in YOLOs. For efficiency, we propose the lightweight classification head, spatial-channel decoupled downsampling. and rank-guided block design, to reduce the manifested computational redundancy and achieve more efficient architecture. For accuracy, we explore the large-kernel convolution and present the effective partial self-attention module enhance the model capability, harnessing the potential for performance improvements under low cost.

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Based on these approaches, we succeed in achieving new family of real-time end-to-ent detectors with different model scales, i.e., YOLOv10-N/5 S M B/L///LLX X. Extensive experiments on standard benchmarks for object detection, i.e., COCO R5] demonstrate that our YOLOv10 can significantly trade-offs across
outperform previous state-of-the-art models in terms of computation-accuraa various model scales. As shown in Fig. [] our YOLOvI0-S / X are 1.8x / x.3x faster than RT-DETR- R18 / R101, respectively, under the similar performance. Compared with YOLOv9-C, YOLOV 10-B achieves 46% reduction in latency with the same performance. Moreover, YOLOv exhibits highly efficient parameter utilization. Our YOLOv / X outperforms YOLOv8-L/ X by 0.3 AP and 0.5 AP, with 1.8x and 2.3x smaller number of parameters, respectively. YOLOv10-N achieves the similar AP compared with YOLOv9-M. YOLO-MS, with 23% / 31 % fewer parameters, respectively. We hope that our work can inspire further studies and advancements in the field.
2 Related Work
Real-time object detectors. Real-time object detection aims to classify and locate objects under low latency, which is crucial for real-world applications. Over the past years, substantial efforts have been directed towards developing efficient detectors [19
31 41]. Particularly, the (50 2 20 22 62 65) stand out as the mainstream ones. YOLOv1, YOLOv2,
57
48 34
79
75
32
YOLO series [48 49
and YOLOv3 identify the typical detection architecture consisting of three parts, L.., backbone, neck, and head 48
49 50). YOLOv4 (2) and YOLOv5 (20] introduce the CSPNet [63] design to replace
DarkNet 47 coupled with data augmentation strategies, enhanced PAN, and greater variety of model scales, etc. YOLOv6 [29] presents BiC and SimCSPSPPF for neck and backbone, respectively, with anchor-aided training and self-distillation strategy. YOLOv7 [62] introduces E-ELAN for rich gradient flow path and explores several trainable bag-of-freebice methods. YOLOv8 (21 presents C2f building block for effective feature extraction and fusion. YOLO [60] provides the advanced
GD mechanism to boost the multi-scale feature fusion capability. YOLOV9 65] proposes GELAN to improve the architecture and PGI to augment the training process.
End-to-end object detectors. End-to-end object detection has emerged as paradigm shift from traditional pipelines, offering streamlined architectures (53). DETR [4) introduces the transformer architecture and adopts Hungarian loss to achieve one-to-one matching prediction, thereby eliminating hand-crafted components and post-processing. Since then, various DETR variants have been proposed
[8] 30, 36 28 77 82). Deformable-DETI leverages multi-scale deformable attention module accelerate the convergence speed. DINO (73
to enhance its performance and efficiency 42 67 56
integrates contrastive denoising, mix query selection, and look forward twice scheme into DETRs. RT-DETR 778 further designs the efficient hybrid encoder and proposes the uncertainty-minine query selection to improve both the accuracy and latency. Another line to achieve end-to-en object detection is based CNN detectors. Learnable NMS 24 and relation networks (26 present another network to remove duplicated predictions for detectors. OneNet [55] and DeFCN [66] propose one- to-one matching strategies to enable end-to-end object detection with fully convolutional networks. FCOS pss 80 introduces positive sample selector to choose the optimal sample for prediction.
3 Methodology
3.1 Consistent Dual Assignments for NMS-free Training
During training, YOLOs [1 65 70 usually leverage TAL [15] to allocate multiple positive sam- ples for each instance. The adoption of one-to-many assignment yields plentiful supervisory signals, facilitating the optimization and achieving superior performance. However, it necessitates YOLOs to rely the NMS proc-prrc,,,,ng, which causes the suboptimal inference efficiency for deploy- ment. While previous works [55] 66 80 explore one-to-one matching to suppress the redundant predictions, they usually introduce additional inference overhead or yield suboptimal performance. In this work, we present a NMS-fre training strategy for YOLOs with dual label assignments and consistent matching metric, achieving both high efficiency and competitive performance.
Dual label assignments. Unlike one-to-many assignment, one-to-one matching assigns only one prediction to each ground truth, avoiding the NMS post-processing. However, it leads to weak supervision, which causes suboptimal accuracy and convergence speed [82]. Fortunately, this deficiency can be compensated for by the one-to-many assignment [6]. To achieve this, we introduce
3

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Dual Label A Asignments
Consistent Match. Metric
10
One-to-many Head
Coneistent
Regression
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Backbone
PAN
m pd
(6.6)
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Top-20
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Figure 2: (a) Consistent dual assignments for NMS-free training. (b) Frequency of one-to-one assignments Top-1/5/10 of one-to-many results for YOLOv8-S which employs 0.2m==055 and 962m=6 by default 21. For consistency, =0.5= 3.2.=6. For inconsistency, ao2.=0.5;; 8.20 =2.
dual label assignments for YOLOs to combine the best of both strategies. Specifically, as shown in Fig. 2 (a), incorporate another one-to-one head for YOLOS. retains the identical structure and adopts the same optimization objectives as the original one-to-many branch but leverages the one-to-one matching obtain label assignments. During training, two heads are jointly optimized with the model, allowing the backbone and neck to enjoy the rich supervision provided by the one- to-many assignment. During inference, we discard the one-to-many head and utilize the one-to-one head to make predictions. This enables YOLOs for the end-to-end deployment without incurring any additional inference cost. Besides, in the one-to-one matching, we adopt the top one selection, which achieves the same performance as Hungarian matching 4 with less extra training time.
Consistent matching metric. During assignments, both one-to-one and aue-to-many approaches leverage metric to quantitatively assess the level of concordance between predictions and instances. To achieve prediction aware matching for both branches, employ a uniform matching metric, i.e.,
m(a, (3) = 8 pd loU b)
(1)
where p is the classification score, and b denote the bounding box of prediction and instance, respectively. s represents the spatial prior indicating whether the anchor point of prediction is within the instance [21 65 29 70]. a and B are two important hyperparameters that balance the impact of the semantic prediction task and the location regression task. We denote the one-to-many and one-to-one metrics as me2m=m((a2mm fo2m) and m022==((00200 902o), respectively. These metrics influence the label assignments and supervision information for the two heads.
In dual label assignments, the one-to- one-to-many branch provides much richer supervisory signals than one-to-one branch. Intuitively, if we can harmonize the supervision of the one-to-one head with that of one-to-many head, can optimize the one-to-one head towards the direction of one-to-many head's optimization. As a result, the one-to-one head can provide improved quality of samples during inference, leading to better performance. To this end, we first analyze the supervision gap between the two heads. Due to the randomness during training, we initiate our examination in the beginning with two heads initialized with the same values and producing the same predictions, i.e., one-to-one head and one-to many head generate the same p and IoU for each ceddiction-instanne pair. We note that the regression targets of two branches do not conflict, as matched predictions share the same targets and unmatched predictions are ignored. The supervision gap thus lies in the different classification targets. Given an instance, we denote its largest loU with predictions as ",", and the largest one-to-many and one-to-one matching scores as m22m and m520 respectively. Suppose that one-to-many branch yields the positive samples and one-to-one branch selects i-th prediction with the metric m 020r we can then derive the classification target 122m. =u*
ma2m..1
mazad =11" S u² for j E Ω and 1220,=="* m2ze
maam 70 The supervision gap between two branches can thus 46 of different classification objectives, i.e.,
for task aligned loss as in 21 65 be derived by the I-Wasserstein distance
to20. ll(i E M)
Σ ExErs()) 102m,k
(2)
We can observe that the gap decreases as tozm. increases, i.e., i ranks higher within Q. It reaches the minimum when 122m,=='', i.e., i the best positive sample in Ω as shown in Fig. (a). To achieve this, we present the consistent matching metric, i.e., 0.20=r O22m and B, 9.2m. which implies mazo=m22m Therefore, the hest positive sample for one-to-many head also the best for one-to-one head. Consequently, both heads can be optimized consistently and barmoniously. For simplicity,

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take r=1, by default, i.e., 062o=Oc2mm and Bo20=Bozm- To verify the improved supervision alignment, we count the number of one-to-one matching pairs within the top- / / / 10 of the one-to-many results after training. As shown in Fig. ( the alignment is improved under the consistent matching metric. For more comprehensive understanding of the mathematical proof. please refer to the appendix.
Discussion with other counter-parts. Similarly, previous works [28 77 54 6 82 explore the
different assignments to accelerate the training convergence and improve the performance for different networks. For example, H-DETR (28). Group-DETR [S] and MS-DETR [77] introduce one-to-many matching in conjunction with the original one-to-ons one matching by hybrid or multiple group label assignments, to improve upon DETR. Differently, to achieve the one-to-man matching, they usually introduce extra queries or repeat ground truths for bipartite matching, or select top several queries from the matching scores, while we adopt the prediction aware assignment that incorporates the spatial prior. Besides, Let [54] employs the dense assignment and sparse assignment branches for training, which all belong to the one-to-many assignment, while we adopt the one-to-many and
investigates the step-by-step training with one-to-many matching in the first stage for convolutional encoder and one-to-one matching in the second stage for transformer decoder, while we avoid the transformer decoder for end-to-end inference. Compared with works [6 [0] which incorporate dual assignments for CNN-based detectors, we further analyze the supervision gap between the two heads and present the consistent matching metric for YOLOs to reduce the theoretical supervision gap. It improves performance through better supervision alignment and eliminates the need for hyper- apper-prrameter tuning.
one-to-one branches. DEYO 45 4 4
3.2 Holistic Efficiency-Accuracy Driven Model Design
In addition to the post-processing, the model architectures of YOLOs also pose great challenges the fffeurancyaccuuracy trade-offs [50] g 29]. Although previous works explore various design strategies, the comprehensive inspection for various components in YOLOs is still lacking. Consequently, the model architecture exhibits non non-negligible computational redundancy and constrained capability, which impedes its potential for achieving high efficiency and performance. Here, we aim holistically perform model designs for YOLOs from both efficiency and accuracy perspectives.
Efficiency driven model design. The components in YOLO consist of the stem, downsampling layers, stages with basic building blocks, and the head. The stem incurs few computational cost and we thus perform efficiency driven model design for other three parts.
(1) Lighteeight classification head, The classification and regression heads usually share the same architecture in YOLOs. However, they exhibit notable disparities in computational overhead. For example, the FLOPs and parameter count of the classification head (5.95G/1.5 are 2.5x and 2.4 those of the regression head (2.34G/0.6 (.MM) in YOLOv8-S. respectively. However, after analyzing the impact of classification error and the regression error (secing Tab. we find that the regression head undertakes more significance for the performance of YOLOs. Consequently, we can reduce the overhead of classification head without worrying about hurting the performance greatly. Therefore, simply adopt lightweight architecture for the classification head, which consists of two depthwise with the kernel size of 3 3x3 followed by a 1 1x1 convolution.
separable convolutions 25 9
(2) Spatial-channe decoupled downsampling. YOLOs typically leverage regular 3x3 standard convolutions with stride of 2, achieving spatial downsampling (from H x W to 2 x W. and channel transformation (from C to 2C') simultaneously. This introduces non-negligible computational cost of O HW (²) and parameter count of (18C22). Instead, we propose to decouple the spatial reduction and channel increase operations, enabling more efficient downsampling. Specifically, we firstly leverage the pointwise convolution to modulate the channel dimension and then utilize the depthwise convolution perform spatial downsampling. This reduces the computational cost to O(HHW C HWC) and the parameter count to O + 18C'.. Meanwhile, it maximizes information retention during downsampling, leading to competitive performance with latency reduction.
(3) Runk- guided block design. YOLOs usually employ the same basic building block for all stages 29 65 e.g., the bottleneck block in YOLOv8 (2.. To thoroughly examine such homogeneous design for YOLOs, we utilize the intrinsic rank [33 [6] to analyze the redundancy of each stage. Specifically, we caleulate the numerical rank of the last convolution in the last basic block in each stage, which counts the number of singular values larger than a threshold. Fig. 3 (a) presents the results of
A lower rank implies greater redundancy, while higher rank signifies more condensed information.
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0.7
Split
1X
CIB
Split
3 DW
MHSA
2
DW
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1X
Noss
0.2
DW
6.1-
Stage 1 Stage Stage 3. Stage Stage 5 Stage Stage? Stage
(a)
(b)
(c)
Figure 3: (a) The intrinsic ranks across stages and models in YOLOv8. The stage in the backbone and neck is numbered in the order model forward process. The numerical rank r is normalized to r/C, for y-axis and its threshold is set Amaz/ / by default, where C. denotes the number of output channels and Amaz is the largest singular value. can be observed that deep stages and large models exhibit lower intrinsic rank values. (b) The compact inverted block (CIB). (c) The partial self-attention module (PSA).
YOLOv8. indicating that deep stages and large models are prone exhibit more redundancy. This observation suggests that simply applying the same block design for all stages is suboptimal for the best capacity-efficiency trade-off. To tackle this, we propose rank-guided block design scheme which aims to decrease the complexity of stages that are shown to be redundant using compact architecture design. We first present a compact inverted block (CIB) structure, which adopts the cheap depthwise convolutions for spatial mixing and cost-effective pointwise convolutions for channel mixing, as shown Fig. 33 (b). It can serve as the efficient basic building block, e.g., embedded the ELAN structure (64] 21 (Fig. (b)). Then, advocate a rank-guided block allocation strategy to achieve the best efficiency while maintaining competitive capacity. Specifically, given model, we sort its all stages based on their intrinsic ranks in ascending order. We further inspect the performance variation replacing the basic block in the leading stage with CIB. there is no performance degradation compared with the given model, proceed with the replacement of the next stage and halt the process otherwise. Consequently, we can implement adaptive compact block designs across stages and model scales, achieving higher efficiency without compromising performance. Due the page limit, we provide the details the algorithm in the appendix.
Accuracy driven model design. We further explore the for accuracy driven design, aiming to boost the performance under minimal cost.
convolution and self-attention
(I) Large-kernel convolution. Employing large-kernel depthwise convolution is an effective way to enlarge the receptive field and enhance the model's capability [10] 40 39]. However, simply leveraging them in all stages may introduce contamination in shallow features used for detecting small objects, while also introducing significant I/O overhead and latency in high-resolution stages [8]. Therefore, we propose to leverage the large-kernel depthwise convolutions in CIB within the deep stages. Specifically, we increase the kernel size of the second 3 xx3 depthwise convolution the CIB to 7x7, following [39]. Additionally, we employ the structural reparameterization technique [1] 10 59] to bring another 3 3 depthwise convolution branch to alleviate the optimization issue without inference overhead. Furthermore, as the model size increases, its receptive field naturally expands, with the benefit of using large-kernel convolutions diminishing. Therefore, we only adopt large-kernel convolution for small model scales.
(2) Partial self-attention (PSA). Self-attention [58 widely employed in various visual tasks due to its remarkable global modeling capability [38 14 76). However, it exhibits high computational complexity and memory footprint. To address this, in light of the prevalent attention head redun- dancy [69], we present an efficient partial self- attention (PSA) module design, as shown in Fig. (c). Specifically, we evenly partition the features across channels into two parts after the 1 convolution. We only feed one part into the NPSA blocks comprised of multi-head self-attention module (MHSA) and feed-forward network (FFN). Two parts are then concatenated and fused by a convolution. Besides, we follow 22 to assign the dimensions of the query and key to half of that of the value in MHSA and replace the LayerNorm [ with BatchNorm [27] for fast inference. Furthermore, PSA only placed after the Stage 4 with the lowest resolution, avoiding the excessive overhead from the

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Table 1: Comparisons with state-of-the-arts Latency is measured using official pre-trained models. Lateney/ denotes the latency in the forward process of model without post-processing. i means the results of YOLOv10 with the original one-to-many, training using NMS. All results below are without the additional advanced training techniques like knowledge distillation PGI for fair comparisons.
Model
#Param
FLOPs(()
APTM (%)
Latency(ms)
Latency' (ms)
YOLOv6-3.0-N
29
11.4
37.0
2.69
1.76
Gold-YOLO-N
60
5.6
12.1
39.6
2.92
1.82
YOLOv8-N 21
3.2
8.7
37.3
6.16
1.77
YOLOv10-N (Ours)
38.5 39.5!
1.79
YOLOv6-3.0-S
29
18.5
45.3
44.3
3.42
2.35
Gold-YOLO-S 60
46.0
3.82
YOLO-MS XS
4.5
17.4
43,4
8.23
2.80
YOLO-MS-S 8
8.1
31.2
46.2
10.12
4.83
YOLOv8-S 21
11.2
28.6
44.9
7.07
2.33
YOLOv9-S 65
26.4
46.7
RT-DETR-R18 78
20.0
60.0
46.5
4.58
4.49
VOLOv10-S (Ours)
7,2
21.6
46.3 / 46.8*
2.49
2.39
YOLOv6-3.0-M
29
34.9
85.8
49.1
5.63
4.56
Gold-YOLO-M
660
41.3
87.5
49.8
6.38
5.45
YOLO-MS g
22.2
80.2
51.0
12.41
7.30
YOLOv8-M 21
25.9
78,9
50.6
9.50
5.09
YOLOv9-M 65
20.0
76.3
51.1
T-DETR-R34
78
31.0
92.0
48.9
6.32
6.21
RI-DETR-R50m
78
36.0
100.0
51.3
6.90
6.84
YOLOv10-M (Ours)
15.4
59.1
/1.1/51.3
4.74
4.63
YOLOv6-3.0-L 29
59.6
150.7
51.8
9.02
7.90
Gold YOLO-L 60)
75.1
151.7
51.8
10.65
9.78
YOLOv9-C 665
25.3
102.1
52.5
10.57
6.13
YOLOv10-B (Ours)
19.1
92.0
52.5 52.7
5.74
5.67
YOLOv8-L 21
165.2
52.9
12.39
8.06
RT-DETR-R50 78
42.0
136.0
53.1
9.20
9.07
YOLOv10-L (Ours)
24.4
120.3
53.2 53,4
7.28
7.21
YOLOv8-X 21
68.2
257.8
53.9
16.86
12.83
RI-DETR-RIO1
78
76.0
259.0
54.3
13.71
13.58
YOLOv10-X (Ours)
29.5
160.4
54.4 54.4
10.70
10.60
quadratic computational complexity of self-attention. In this way, the global representation learning ability can be incorporated into YOLOs with low computational costs, which well enhances the model's capability and leads to improved performance.
4 Experiments
4.1
Implementation Details
We select YOLOv8 21 our baseline model, due its commendable latency-accuracy balance and its availability in various model sizes. We employ the consistent dual assignments for NMS-frea free training and perform holistic efficiency-aceurae, driven model design based on which brings our YOLOvI0 10 models. YOLOv10 has the same variants as YOLOv8, i.e., N S M X. Besides, we derive a new variant YOLOv10-B, by simply increasing the width scale factor of YOLOv10-M. We verify the proposed detector on COCO 35 under the same Moreover, the latencies of all models are tested on T4 GPU with TensorRT FP16, following 77
setting (21 65 62
Comparison with state-of-the-arts
As shown in Tab. I our YOLOv10 achieves the state-of-the-a performance and end-to-end end latency across various model scales. first compare YOLOv10 with our baseline models, i.e., YOLOv8.
On S M /L/ X five variants, our YOLOv10 achieves 1.2% 1.4% / 0.5% 0.3% / 0.5% AP improvements, with 28% 36%
44% 57% fewer parameters, 23% 24% 25% 27% 38% less caleulations, and 70% 65% / 50% 41% 37% lower latencies. Compared with other YOLOs.

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Table 2: Ablation study with YOLOv10-S and YOLOvI0-M on COCO.
# Model
NMS-free. Efficiency. Accuracy. #Param.(M) FLOPs(G) AP" (%) Latency(ms)
11.2
28.6
44.9
7.07
11.2
28.6
44.3
2.44
YOLOv10-S
6.2
20.8
44.5
2.31
7.2
21.6
46.3
2.49
25.9
78.9
50.6
9.50
25.9
78.9
50.3
5.22
YOLOvI0-M
7
14.1
58.1
50.4
4.57
8
15.4
59.1
51.1
4.74
Table 3: Dual assign.
Table 4: Matching metric.
Table 5: Efficiency. for YOLOv10-S/M.
o2m o2o Latency
0.2o
Bazo
AP
0,20
3,20 Ap
Model
#Param
FLOPs
AP"
Latency
44.9
7.07
0.5
2.0
42.7
0.25
3.0
44.3
base.
11,2/25,9
28.6/78.9 44.3/50.3 2.44/5.22
43.4
2.44
0.5
4.0
44.2
0.25
6.0
43.5
+cls.
9.9/23.2
23.5/67.7 44.2/50.2 2.39/5.07
V
44.3 2.44
0.5
6.0
44.3
1.0
6.0
43.9
3 +downs.
8.0/19.7
22.2/65.0
44.4/50.4 2.36/4.97
0.5
8.0
44.0
1.0
12.0
44.3
4 +block.
6.2/14.1
20.8/58.1
44.5/50.4 2.31/4.57
YOLOv10 also exhibits superior trade-offs between accuracy and computational cost. Specifically, for lightweight and small models, YOLOv10-N S outperforms YOLOv6-3.0N/ by 1.5 AP and 2.0 AP, with 51% 61% % fewer parameters and 41% 1% / 52% less computations, respectively. For medium models, compared with YOLOv9-C/ YOLO-MS, YOLOv10-B / M enjoys the 46% / 62% latency reduction under the same better performance, respectively. For large models, compared with Gold-YOLO-L. our YOLOv 10-L shows 68% fewer parameters and 32% lower latency, along with significant improvement of 1.4% AP. Furthermore, compared with RT-DETR, YOLŌv10 obtains significant performance and lateney improvements. Notably, YOLOv X achieves 1.8 x and 1.3x faster inference speed than RT-DETR-RI8 R101, respectively, under the similar performance. These results well demonstrate the superiority of YOLOv10 10 as the real-time end-to-end detector.
We also compare YOLOv10 with other YOLOs using the original one-to- mn--tommany training approach. We consider the performance and the latency of model forward process (Lateney/) in this situation, following 62 21 60]. As shown in Tab. I. YOLOv10 also exhibits the state-of-the-ard performance and efficiency across different model scales, indicating the effectiveness of our architectural designs.
4.3 Model Analyses
Ablation study. We present the ablation results based on YOLOv10-S and YOLOv10-M in Tab. 2 can be observed that our NMS-frre training with consistent dual assignments significantly reduces the end-to-end latency of YOL.vv 10-S by 4.63ms, while maintaining competitive performance of 44.3% AP. Moreover, efficiency driven model design leads to the reduction of 11.8 M parameters and 20.8 GFIOPs, with a considerable latency reduction of 0.65ms for VOLOvv0-M, well showing its effectiveness. Furthermore, our accuracy driven model design achieves the notable improvements of 1.8 AP and 0.7 AP for YOLOv 10-S and YOLOv 10-M,, alone with only 0.18ms and 0. 1mss latency overhead, respectively, which well demonstrates its superiority.
Analyses for NMS-free training.
Dual label assignments. We present dual label assignments for NMS-free YOLOs, which can bring both rich supervision of onn-to-many (o2m) branch during training and high efficiency of one-to-one (o2o) branch during inference. We verify its benefit based on YOLOv8-S, i.e., #1 in Tab. 2 Specifically, we introduce baselines for training with only o2m branch and only o2o branch, respectively. As shown in Tab. 3 our dual label assignments achieve the best AP-latency trade-off.
Consistent matching metric. We introduce consistent matching metric make the one-to-one head more harmonious with the one-to-many head. We verify its benefit based on YOLOv8-S, i.e., #1 in Tab. 2 under different 0o and 8o2o. As shown in Tab. the proposed consistent matching metric. i.e., 0622=F O.o2m and Bo2o=r Bo2m. can achieve the optimal performance, where 40.5 and 8.2m=6.0 in the one-to-many head [21]. Such an improvement can attributed to the reduction of the supervision gap (Eq. (2), which provides improved supervision alignment between two branches. Moreover, the proposed consistent matching metric eliminates the need for exhaustive hyper-parameter tuning, which is appealing in practical scenarios.
8

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YOLOVID-N
YOLOVIDS
YOLOVIDM
VOLOVE
10LOV10
42
Figure 4: The average cosine similarity of each anchor point's extracted features with all others.
Table 6: cls. results.
Table 7: Results of d.s.
Table 8: Results of CIB.
Table Rank-guided.
base.
+els.
Model
Ap
Latency
Model
ApPal
Latency
Stages with CIB AP
Ap
44.3
44.2
base 43.7
2.33
IRB
43.7
2.30
empty
44.4
Ap
59.9
59.9
ours
44.4
2.36
IRB-DW
44.2
2.30
44.5
Ap
w/or
64.5
64.2
44.5
2.31
8,4,
44.5
8,4,7
44.3
Performance gap compared with one-to-many training. Although achieving superior end-to-end performance under NMS-free training, we observe that there still exists the performance gap compared with the original one-to-many training using NMS, as shown in Tab. 3 and Tab. 1 Besides, we note that the gap diminishes as the model size increases. Therefore, we reasonably concludes that such a gap can be attributed to the limitations in the model capability. Notably, unlike the original one-to-many training using NMS, the NMS-free training necessitates more discriminative features for one-to-one matching. In the case of the YOLOvI0-N model, its limited capacity results in extracted features that lack sufficient discriminability, leading to a more notable performance gap of 1.0% AP. In contrast, the YOLOv10-X model, which possesses stronger capability and more discriminative features, shows no performance gap between two training strategies. In Fig. 4 visualize the average cosine similarity of each anchor point's extracted features with those all other anchor points on the COCO val set. We observe that as the model size increases, the feature similarity between anchor points exhibits downward trend, which benefits the one-to-one matching. Based on this insight, we will explore approaches to further reduce the gap and achieve higher end-to-end performance in the future work.
Analyses for efficiency driven model design. We conduct experiments to gradually incorporate the efficiency driven design elements based on YOLOv10-S/M Our baseline is the YOLOv 1000000-SMM 10-S/M model without secciency-accurace driven model design, i.e., #2/#6 in Tab. 2 As shown Tab. 5 each design component, including lightweight classification head, spatial-channel decoupled downsampling, and rank- vank-guided block design, contributes to the reduction of parameters count, FLOPs, and latency. Importantly. these improvements achieved while maintaining competitive performance.
Lighteeight classification head. We analyze the impact of category and localization errors of predictions the performance. based on the YOLOvIO-S of #1 and #2 in Tab. 3 like [7]. Specifically, we match the predictions to the instances by the one-to-one assignment. Then, we substitute the predicted category score with instance labels, resulting in Apeal w/oc with no classification errors. Similarly, we replace the predicted locations with those of instances, yiclding APeal with no regression errors. As shown in Tab. 6. Ap 01/0
w//o is much higher than Apeal revealing that eliminating the regression errors achieves greater improvement. The performance bottleneck thus lies more in the regression task. Therefore, adopting the lightweight classification head can allow higher efficiency without compromising the performance.
w/o
Spatial-channel decoupled downsampling. We decouple the downsampling operations for efficiency, where the channel dimensions are first increased by pointwise convolution (PW) and the resolution is then reduced by depthwise convolution (DW) for maximal information retention. We compare with the baseline way of spatial reduction by DW followed by channel modulation by PW, based on the YOLOv of in Tab. 5 As shown in Tab. 7 our downsampling strategy achieves the 0.7% AP improvement by enjoying less information loss during downsampling.
Compact inverted block (CIB). We introduce CIB as the compact basic building block. We verify its effectiveness based on the YOLOv10-S of #4 in the Tab. 3 Specifically, we introduce the inverted residual block [5T] (IRB) as the baseline, which achieves the suboptimal 43.7% AP, as shown in We then append 3x3 depthwise convolution (DW) after it, denoted "IRB-DW"" which
Tab. 8

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Table 10: Accuracy. for S/M. Table 11: L.k. results. Table 12: L.k. usage. Table 13: PSA results.
# Model
Ap
Latency
Model
Ap
Latency
w/o L.k.
w/ L.k.
Model
Apeal
Tatency
1 base.
44,5/50.4
2.31/4.57
k.s.=5
44.7
2.32
36.3
36.6
PSA
46.3
2.49
2 +L.k.
44.9/-
2.34/-
k.s.=7
44.9
2.34
S
44.5
44.9
Trans.
46.0
2.54
3 +PSA 46.3/51.1
2.49/4.74
k.s.=9
44.9
2.37
M
50.4
50.4
NPSA
46.3
2.49
w/o rep. 44.8
2.34
NPsA =
46.5
2.59
brings 0.5% AP improvement. Compared with "IRB-DW", our CIB further achieves 0.3% AP improvement by prepending another DW with minimal overhead, indicating its superiority.
Rank-guided block design. We introduce the rank-guided block design to adaptively integrate compact block design for improving the model efficiency. We verify its benefit based on the YOLOv10-S of #3 in the Tab. []] The stages sorted in ascending order based on the intrinsic ranks are Stage 8-4-7-3-5-1-6- 1-6-,, like in Fig. (a). As shown in Tab. | when gradually replacing the bottleneck block in each stage with the efficient CIB, we observe the performance degradation starting from Stage 7. In the Stage 8 and 4 with lower intrinsic ranks and more redundancy, we can thus adopt the efficient block design without compromising the performance. These results indicate that rank- lunk-gudedd block design serve an effective strategy for higher model efficiency.
Analyses for accuracy driven model design. We present the results of gradually integrating the accuracy driven design elements based on YOLOv10-S/M. Our baseline is the YOLOv1O 10-0-//MMM model after incorporating efficiency driven design, i.e., #3/#7 in Tab. 2 As shown in Tab. 10 the adoption of large-kernel convolution and PSA module leads to the considerable performance improvements of 0.4% AP and 1.4% AP for YOLOvI0-S under minimal latency increase of 0.03ms and 15ms, respectively. Note that large-kernel convolution is not employed for YOLOv10-M, (see Tab. T2b.
Large-kemel convolution. We first investigate the effect of different kernel sizes based on the YOLOv10-S of #2 in Tab. 10 As shown in Tab. [1] the performance improves as the kernel size increases and stagnates around the kernel size of 7 7x7, indicating the benefit of large perception field. Besides, removing the reparameterization branch during training achieves 0.1 1% AP degradation, showing its effectiveness for optimization. Moreover, we inspect the benefit of large-kernel convolution across model scales based on YOLOv10-N / S / M. As shown in Tab. [2] it brings no improvements for large models, i.e., YOLOv10-M, due to its inherent extensive receptive field. We thus only adopt large- kereel convolutions for small models, i.e., YOLOv1O-N/ S.
Partial self- self-attention (PSA). We introduce PSA to enhance the performance by incorporating the global modeling ability under minimal cost. We first verify its effectiveness based on the YOLOv 10- S of #3 in Tab. 10 Specifically, we introduce the transformer block, i.e., MHSA A followed by FFN, as the baseline, denoted as "Trans.". As shown in Tab. 13 compared with it, PSA brings 0.3% AP improvement with 0.05ms latency reduction. The performance enhancement may be attributed to the alleviation of optimization problem (68 10] in self-attention, by mitigating the redundancy in attention heads. Moreover, we investigate the impact of different NpSA. As shown in Tab. [3 increasing NPSA to 2 obtains 0.2% AP improvement with 0. Ims latency overhead. Therefore, we set NPSA to 1, by default, to enhance the model capability while maintaining high efficiency.
5 Conclusion
In this paper, we target both the post-processing and model architecture throughout the detection pipeline of YOLOs. For the post-processing, we propose the consistent dual assignments for NMS- free training, achieving efficient end-to-end detection. For the model architecture, we introduce the holistic afficieney-accurayy driven model design strategy, improving the performance-efficiency trade- off's These bring our YOLOv10, a new real-time end-to-end object detector. Extensive experiments show that YOLOv10 achieves the state-of-the-art performance and latency compared with other advanced detectors, well demonstrating its superiority.
6 Acknowledgments
This work was supported by National Natural Science Foundation of China (Nos. 61925107, 6227 1281) and Beijing Natural Science Foundation (No. L223023).
10

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15

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A Appendix
A.1
Implementation Details
Following 21 62
all YOLOv10 models are trained from scratch using the SGD optimizer for 500 epochs. The SGD momentum and weight decay are set to 0.937 and 5 10 respectively. The initial learning rate is Mosaic 2 Mixup 74] and copy-paste augmentation [18] etc., like 21 65 Tab. [resents the detailed hyper-parameters. All models are trained on NVIDIA 3090 GPUs. Besides, we increase the width scale factor of YOLOv10-M to 1.0 to obtain YOLOv10-BB For PSA, we employ it after the SPPF module [2I] and adopt the expansion factor of 2 for FFN. For CIB, we also adopt the expansion ratio of 2 for the inverted bottleneck block structure. Following 65 62). report the standard mean average precision (AP) across different object scales and IoU thresholds on the COCO dataset [35].
10 2 and decays linearly to 1 10 For data augmentation, we adopt the
Moreover, we follow [78 to establish the end-to-end speed benchmark. Since the execution time of NMS is affected by the imput, we thus measure the latency on the COCO val set with the batch size of I, like (78). We adopt the same NMS hyperparameters used by the detectors during their validation. The TensorRT effici entNMSPlugin omitted. We report the average latency across all images.
appended for post-processing and the I/O overhead
Table 14: Hyper-parameters of YOLOv10.
hyper-parameter
YO1.0V10-N//M///I//XX
epochs optimizer momentum
500
SGD
0.937
weight decay warm-up epochs warm-up momentum warm-up bias learning rate
5x 5x10
3
0.8
0.1
initial learning rate final learning rate learning rate schedule box loss gain class loss gain
10-2
10-4
linear decay
7.5
0.5
DFL loss gain
1.5
HSV saturation augmentation
0.7
HSV value augmentation
0.4
HSV hue augmentation translation augmentation
0.015
0.1
scale augmentation
0.5/0.5/0.9909990999009
mosaic augmentation
Mixup augmentation copy-pase copy-paste augmentation close mosaic epochs
0.0/0.0/0. 1/0. 1/0, 15
0.000.0/0.1/0.1/0..30
10
A.2 Details of Consistent Matching Metric
We provide the detailed derivation of consistent matching metric here.
As mentioned in the paper, suppose that the one-to-many positive samples is Ω and the one-to- one branch selects i-th prediction. We can then leverage the normalized metric [15) to obtain the classification target for task alignment learning
[ 15 65
29 [0), i.e., Lo2m. u matmal < u² m
for j E Ω and lo20. = u²
Motol. u². We can thus derive the supervision gap between two
branches by the I-Wasserstein distance [46] of the different classification targets, i.e.,
A = [1 to2o,) (1 - IIi ))toomm.)l +
Σ EREOR |1- (1 f02m,))
T(i E Q)tozm,il + Σ Eneryr tozm.k
to20,1
Ili E 1)tommm
Σnear tozm.ks
16

---

where II()) is the indicator function. We denote the classification targets of the predictions in Ω as (fi, ff..,..... Enj in descending order, with & 2 2 Ell.. We can then replace to20, with u( and obtain:
= u Ili E a)toom, + Σ REMA to2m.
= u + Σ ken la2m, - TI(i E 2ommm
(4)
u² [2]
k=1 & 2 II(i R)toomm
We further discuss the supervision gap in two scenarios, i.e.,
1. Supposing E 92, we can obtain:
A= + Σ k==1 is
(5)
Supposing E Q we denote to2m. fn and obtain:
= u" Em=
k==1 ba
10
2 fn
(6)
Due to εx 2 0, the second case lead to smaller supervision gap. Besides, decreases as fn increases, indicating that n decreases and the ranking of within Q improves. Due to in < 1, A thus achieves the minimum when in = l1, i.e., i is the best positive sample in Ω with Mo2m,i mm2m and Lo2m, u" Ma2m.
observe that A
u²
m2m
Furthermore, we prove that we can achieve the minimized supervision gap by the consistent matching
metric. We suppose azom > 0 and Boom > O, which are common in 21 65 29
[5] 70]. Similarly,
we assume 0,22 > 0 and Baz0 > O. We can obtain
> 0 and
B.2u.
0, and then
ac2m
Bo2m
derive mazo by
3 p paoie IoU 6)00
molo
9 p" auam IoU(b, b) 3)-m.
s. (p aeam loU b) 902m) IoU (122)))))
=m mm2m LoU () (12-))))
To achieve Mo2m.1 = m mm2m and mozo, = m3z0> we can make m.zo monotonically increase with mo2m by assigning (r' r1)
0, i.e.
IbU 0)0m
MoZo = m 02m
(8)
mm2m
Supposing 71 12 r, we can thus derive the consistent matching metric, i.e., 0.20 r 0.2m and 8.20 = T- Bo2m. By simply taking r = 1, we obtain ago0 = ao2m and Bo20 = 302m-
A.3 Details of Rank-Guider Block Design
We present the details of the algorithm of rank-guided block design in Algo. I Besides, to calculate the numerical rank of the convolution, we reshape its weight to the shape of (Ca, K2 C.), where C, and C, denote the number of output and input channels, and K means the kernel size, respectively.
A.4 Training Cost Analyses
In addition the inference efficiency analyses, we also investigate the training cost of our YOLOv10 models. We compare with other YOLO variants and measure the training throughput on 8 NVIDIA 3090 GPUs using the official codebases. Tab. [3 presents the comparison results based on the medium model scale. We observe that despite having 500 training epochs. YOLOv10 achieves high training throughput, making its training cost affordable. We also note that the one-te one-to-many head in the NMS-free training will introduce the extra overhead for YOLOv10. To investigate this, we measure the training cost of YOLOv10 with only the one-to-one head, which denoted as "YOLOv10-o2o" As shown in Tab. 15 YOLOv Ov100MM results in a small increase in the training time over "YOLOv 10- M-o2o",, about 18s each epoch, which affordable. fairly verify the benefit of the one-to-many head in NMS-free training, also adopt longer 550 training epochs for -YOLOv10-M-O22 which
17

---

Algorithm 1: Rank-guided block design
Input: Intrinsic ranks R for all stages S; Original Network θ; CIB fcss
Output: New network O" with CTB for certain stages.
f 0;
Gg <- 60;;;; 0; 4- e Go: ape <- AP(T((o))
// Itraninig the network; AP APievaluating the AP performance.
while S + 0 do
St argmin,es'
1 + Replace 1r Babb, 8 // Replace the block in Stage 8t of O₁ with CIB 8 ap-+1 - 1
if opt+1 2 apo then
o* 0 S\ {8t}:
10
else
11
return O":
12
end
13
end
14
return :":
Table 13: Training cost analyses on 8 NVIDIA 3090 GPUs.
Table 16: Latency with NMS.
Model
Epoch
Speed (epoch/hour)
Time (hour)
Model
Latency
YOLOv6-3.0-D
41.7
YOLOv10-N
6.19ms
YOLOv8-M
500
18.3
27.3
YOLOv10-S
7.15ms
YOLOv9-M
500
12.3
40.7
YOLOv10-M
9.03ms
Gold-YOLO-M
300
4.7
63.8
YOLOv10-B
10.04ms
YOLO-MS
300
7.1
42.3
YOLOv10-L.
11.52ms
YOLOv10-M-020
18.8
26.7
YOLOv10-X
14.67ms
YOLOv10-M
500
17.2
29.1
leads similar training time (29.3 vs. 29.1 hours) but still yields inferior performance (48.9% vs. 51.1% AP) compared with YOLOv10-M.
A.5 More Results on COCO
We measure the latency of YOLOv10 with the original one-to-many training using NMS and report the results COCO in Tab. [6 Besides, we report the detailed performance of YOLOv10, including AP and Apral at different loU thresholds, as well as Apral APpalatt Apeal
medium. and A Apeal large across different scales, in Tab. 17 We also present the comparisons with more lightweight detectors, including DAMO-YON YOLO 71. YOLOv7
[62] and DEYO 45). in Tab. T8 It shows that our YOLOv10 also achieves superior performance and efficiency trade-offs. Additionally, experiments, we follow previous works (2I] 655 to train the models for 500 epochs. We also conduct experiments to train the models for 300 epochs and present the comparison results with YOLOv6 [29], Gold- YOLO [60). and YOLO-MS (8] which adopt 300 epochs, in Tab. 19 We observe that our YOLOv10 also exhibits better performance and inference latency. We also note that despite trained for 500
epochs, YOLOv10 has less training cost compared with these models as presented in Tab. 15
A.6 Inference Efficiency Comparison on CPU
We present the speed comparison results of YOLOv10 and others on CPU (Intel Xeon Skylake, IBRS) using Open VINO in Fig. We observe that YOLOv10 also shows state-of-the-art trade-offs in terms of performance and efficiency.
A.7 More Analyses for Holistic Efficienyy-Accrrag Driven Model Design
We note that reducing the latency of YOLOvI0-S (#2 in Tab. 2 particularly challenging due to its small model scale. However, as shown in Tab. 2 our efficiency driven model design still achieves 5.3% reduction in latency without compromising performance. This provides substantial support for the further accuracy driven model design. YOLOv10-S achieves a better aatency-accural trade-off
18

---

Table 17:
Detailed performance of YOLOv10 on COCO.
Model
Ap (%)
AP (%)
AP7o (%)
(%)
AP
Aprnl
medium (%)
AP
large (%)
YOLOvIO-N
38,5
53.8
41.7
18.9
42,4
54,6
YOLOvI0-S
46.3
63.0
50.4
26.8
51.0
63.8
YOLOv10-M
51.1
68.1
55.8
33.8
56.5
67.0
YOLOv10-B
52.5
69.6
57.2
35.1
68.5
Y0LOv10-L
53.2
70.1
58.1
35.8
58.5
69.4
YOLOv10-X
54.4
71.3
59.3
37.0
59.8
70.9
Table 18: Comparisons with more lightweight detectors.
Model
(M)
FLOPs(G)
AP (%)
Latency(ms)
DEYO-tiny 45
4.0
8.0
37.6
2.01
YOLOv10-N
2.3
6.7
38.5
1.84
DAMO- YOLO-T 77
8.5
18.1
42.0
2.21
DAMO- YOLO-S
16.3
46.0
3.18
DEYO-S 445
14.0
26.0
45.8
3.34
YOLOv10-S
7.2
21.6
46.3
2.49
DAMO- YOLO-M 71
28.2
61.8
49.2
4.57
DAMO-YOLO 71
42.1
97.3
50.8
6,48
DEYO-M 45
33.0
78.0
50.7
7.14
YOLOv10-M
15.4
59.1
51.1
4.74
YOLOv7 62
36.9
104.7
$1.2
17.03
YOLOv10-B
52.5
5.74
YOLOv7-X 62
71.3
189.9
$2.9
21.45
DEYO-L 4S
$1.0
155.0
YOLOv10-L
24.4
120.3
53.2
7.28
DEYO-X 45
78.0
242.0
53.7
15.38
YOLOv10-X
29.5
160.4
54.4
10.70
with our holistic efffciency-accuragy driven model design, showing a 2.0% AP improvement with only 0.05ms latency overhead. Besides, for YOLOv 10-M (#6 in Tab. 2 which has a larger model scale and more redundancy. our efficiency driven model design results in a considerable 12.5% latency reduction, shown in Tab. 2 When combined with accuracy driven model design, observe notable 0.8% AP improvement for YOLOv10-M, along with a favorable latency reduction of 0.48ms. These results well demonstrate the effectiveness of our design strategy across different model seales.
Table 19: Performance comparisons under 300 training epochs.
Model
#Param.
FLOPs(G)
AP)a((5)
Latency(ms)
YOL00663.0-N 29
4.7
11.4
37.0
2.69
Gold-YOL.O-N 60
5.6
12.1
39.6
2.92
YOLO0v10NN (Ours)
2.3
6.7
37.7
1.84
YOLOv6-3.0-S (29
18.5
45.3
44.3
3.42
Gold-YOLO-S
(60
21.5
46.0
45.4
3.82
YOLO-MS-XS
S
4.5
17.4
43.4
YOLOv10-S (Ours)
7.2
21.6
45.6
2.49
YOLOv6-3.0-M
29
34.9
85.8
49.1
5.63
Gold-YOLO-M
660
41.3
87.5
49.8
6.38
YOLOv10-M (Ours)
15.4
59.1
50.3
4.74
YOLOv6-3.0-L 29
59.6
150.7
51.8
9.02
Gold-YOLO-L
60
75.1
151.7
51.8
10.65
YOLO-MS g
22.2
80.2
51.0
12.41
YOLOv10-B (Ours)
19.1
92.0
51.6
5.74
YOLOv10-L (Ours)
24.4
120.3
52.4
7.28
YOL.0v10-X (Ours)
29.5
160.4
53.6
10.70
19

---

55.0
52.5
50.0
COCO AP (%)
47.5
YOLOv6-v3.0
YOLOV8
YOLOV9
42.5
YOLO-MS
Gol--YOLO
RI-DETR
37.5
YOLOvID (Ours)
100
200
300
400
500
Latency CPU (ms)
Figure 5: Performance and efficiency comparisons CPU.
roffic light 0.86
troffic light 0.87
traffic.
affic
0.60
Figure 6: Visualization results under complex and challenging scenarios.
A.8 Visualization Results
Fig. presents the visualization results of our YOLOv10 in the complex and challenging scenarios. It can be observed that YOLOv10 can achieve precise detection under various difficult conditions, such as low light, rotation, etc. It also demonstrates strong capability in detecting diverse and densely packed objects, such as bottle, cup, and person. These results indicate superior performance.
A.9 Contribution, Limitation, and Broader Impact
Contribution. summary, our contributions are three folds as follows:
1. We present a novel consistent dual assignments strategy for NMS-free YOLOs. A dual label assignments way designed to provide rich supervision by one-to-many branch during training and high efficiency by one-to-one branch during inference. Besides, to ensure the harmonious supervision between two branches, we innovatively propose the consistent matching metric, which can well reduce the theoretical supervision gap and lead improved performance.
20

---

2. We propose a holistic sunciinncy-acuuran driven model design strategy for the model architecture of YOLOs. We present novel lightweight classification head, spatial-channel decoupled down- sampling, and rank-guided block design, which greatly reduce the computational redundancy and achieve high efficiency. We further introduce the large-kernel convolution and innovative partial self-attention module, which effectively enhance the performance under low cost.
3. Based the above approaches, introduce YOLOv10, detector. Extensive experiments demonstrate that our YOLOv10 achieves the state-of-the-art performance and efficiency trade-off's compared with other advanced detectors.
real-time end-to-end object
Limitation. Due to the limited computational resources, we not investigate the pretraining of YOLOvI0 on large-scale datasets, e.g., Objects365 [52]. Besides, although can achieve competitive end-to-end performance using the one-to-one head under NMS-frec training, there still exists performance gap compared with the original one-to-many training using NMS, especially noticeable in small models. For example, in YOLOv10-N and YOLOv10-S, the performance of one-to one-to-many training with NMS outperforms that of NMS-free free training by 1.0% AP and 0.5% AP, respectively. We will explore ways to further reduce the gap and achieve higher performance for YOLOv10 in the future work.
Broader impact. The YOLOs can be widely applied in various real-world applications, including medical image analyses and autonomous driving, etc. We hope that our YOLOv 0 can assist in these fields and improve the efficiency. However, we acknowledge the potential for malicious use of our models. We will make every effort to prevent this.
21

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Question: Does the paper specify all the training and test details (c.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary understand the results?
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Reviews:
[{"review_id": "U3UeKdTO0g", "reviewer": "Reviewer_vpxY", "summary": "The paper presents YOLOv10, an advancement in the YOLO series for real-time object detection. The authors have focused on eliminating the need for Non-Maximum Suppression (NMS) and optimizing the model architecture for efficiency and accuracy. The paper includes a novel training strategy and architectural enhancements, demonstrating improved performance on the COCO dataset.", "questions": "1. Could you report more details on the training cost compared to other YOLOs, such as total training epochs and throughput( with dual label assignments training).\n2.  Given that previous iterations of the YOLO series have typically been trained for 300 epochs, would the authors be able to present the training outcomes for YOLOv10 at this epoch count?", "limitations": "This paper has discussed the limitations in Appendix.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The introduction of consistent dual assignments for NMS-free training is an innovative solution that potentially improves the end-to-end deployment efficiency of object detectors.\n2. The paper presents a comprehensive strategy for optimizing model components, which is a significant advancement over prior works that focused on isolated aspects of model design.\n3. The paper is backed by extensive empirical evidence, showing improvements in both speed and accuracy over previous models.", "weaknesses": "1.  While the paper introduces a new training strategy, the innovations in the model's architectural design are not as pronounced. A more detailed exposition of the creative process and rationale behind the architectural choices would strengthen the paper's contribution to the field.\n2. The paper acknowledges a 1% Average Precision (AP) difference when comparing the YOLOv10-N model trained with NMS-free to the original one-to-many training with NMS. It is better to provide further performance analysis of NMS-free training.\n3. Latency of YOLOv10 with the original one-to-many training using NMS should be reported.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your valuable feedback and insightful comments. Thank you for liking the new training strategy, comprehensive model optimization strategy, and extensive experiments. We provide the response to each comment in the point-to-point manner below. Please feel free to contact us if you have further concerns.\n\n**Q1: \"While the paper introduces a new training strategy, the innovations in the model's architectural design are not as pronounced. A more detailed exposition of the creative process and rationale behind the architectural choices would strengthen the paper's contribution to the field.\"**\n\n**A1**: Thank you for liking our NMS-free training strategy. Like ConvNeXt [1] and MobileLLM [2], our architectural choices are motivated by a comprehensive inspection for various components, systematically considering both efficiency and accuracy aspects. While the architectural designs may appear less pronounced compared with the proposed training strategy, they are not equipped without any rationale. For example, our lightweight classification head design is driven by reducing the computational redundancy in the classification task relative to the regression task. Our rank-guided block design is motivated by adopting the compact block structure adaptively based on the redundancy indicated by intrinsic ranks. We will make them more detailed and comprehensive in the revision. Besides, we believe that benefiting from the NMS-free training strategy, there is greater flexibility in the architectural designs, which can inspire further research on the YOLO architecture. Therefore, our model design strategies can serve as an initial exploration that paves the way for more advanced designs in the future.\n\n**Q2: \"It is better to provide further performance analysis of NMS-free training.\"**\n\n**A2**: Thanks. In Tab.1 in the paper, we observe that the performance gap between NMS-free training and the original one-to-many training diminishes as the model size increases. Therefore, we can reasonably conclude that such a gap can be attributed to the limitations in the model capability. Notably, unlike the original one-to-many training using NMS, the NMS-free training necessitates more discriminative features for one-to-one matching. In the case of the YOLOv10-N model, its limited capacity results in extracted features that lack sufficient discriminability, leading to a more notable performance gap. In contrast, the YOLOv10-X model, which possesses stronger capability and more discriminative features, shows no performance gap between two training strategies. In Fig.1 in the global response pdf, we visualize the average cosine similarity of each anchor point's extracted features with those of all other anchor points on the COCO validation set. We observe that as the model size increases, the feature similarity between anchor points exhibits a downward trend, which benefits the one-to-one matching. We will incorporate these results in the revision.\n\n**Q3: \"Latency of YOLOv10 with the original one-to-many training using NMS should be reported.\"**\n\n**A3**: Thanks. We measure the latency of YOLOv10 with the original one-to-many training using NMS and report the results below. \n\n|Model|Latency|\n|---------|-------|\n|YOLOv10-N|6.19ms|\n|YOLOv10-S|7.15ms|\n|YOLOv10-M|9.03ms|\n|YOLOv10-B|10.04ms|\n|YOLOv10-L|11.52ms|\n|YOLOv10-X|14.67ms|\n\n**Q4: \"More details on the training cost compared to other YOLOs, such as total training epochs and throughput (with dual label assignments training).\"**\n\n**A4**: Thanks.  We measure the training throughput on 8 NVIDIA 3090 GPUs using the official codebases and present the comparison results based on the medium variant below. We observe that despite having 500 training epochs, YOLOv10 achieves a high training throughput, making its training cost affordable. \n\n|Model|Epochs|Throughput (epochs/hour)|Total Time (hour)|\n|-------------|------|------------------------|-----------------|\n|YOLOv6-3.0-M|300|7.2|41.7|\n|YOLOv8-M|500|18.3|27.3|\n|YOLOv9-M|500|12.3|40.7|\n|Gold-YOLO-M|300|4.7|63.8|\n|YOLO-MS|300|7.1|42.3|\n|**YOLOv10-M**|500|17.2|29.1|\n\n**Q5: \"Training outcomes for YOLOv10 at 300 epochs.\"**\n\n**A5**: Thanks. In experiments, we follow previous works [3,4] to train the models for 500 epochs. Here, we conduct experiments to train the models for 300 epochs and present the comparison results with YOLOv6[5], Gold-YOLO[6], and YOLO-MS[7] which adopt 300 epochs as below. We observe that YOLOv10 also achieves superior performance and efficiency. Besides, we note that despite trained for 500 epochs, YOLOv10 has less training cost compared with these models as presented in **A4**.\n\n|Model|AP$^{val}$ (%)|Latency (ms)|\n|-------------|--------------|------------|\n|YOLOv6-3.0-N|37.0|2.69|\n|Gold-YOLO-N|39.6|2.92|\n|**YOLOv10-N**|37.7|1.84|\n|YOLOv6-3.0-S|44.3|3.42|\n|Gold-YOLO-S|45.4|3.82|\n|YOLO-MS-XS|43.4|8.23|\n|**YOLOv10-S**|45.6|2.49|\n|YOLOv6-3.0-M|49.1|5.63|\n|Gold-YOLO-M|49.8|6.38|\n|**YOLOv10-M**|50.3|4.74|\n|YOLOv6-3.0-L|51.8|9.02|\n|Gold-YOLO-L|51.8|10.65|\n|YOLO-MS|51.0|12.41|\n|**YOLOv10-B**|51.6|5.74|\n|**YOLOv10-L**|52.4|7.28|\n|**YOLOv10-X**|53.6|10.70|\n\n[1] A ConvNet for the 2020s, CVPR 2022.\n\n[2] MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases,  ICML 2024.\n\n[3] YOLOv8.\n\n[4] YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information, arXiv preprint 2024.\n\n[5] YOLOv6 v3.0: A Full-Scale Reloading, arXiv preprint 2023.\n\n[6] Gold-YOLO: Efficient object detector via gather-and-distribute mechanism, NeurIPS 2023.\n\n[7] YOLO-MS: YOLO-MS: rethinking multi-scale representation learning for real-time object detection, arXiv preprint 2023.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wvd3c8pHSn", "reviewer": "Reviewer_nrqj", "summary": "The authors further improve the YOLO series detector to the tenth generation, through the following two points. The first part is the post-processing improvement, and the authors propose consistent dual assignments for NMS-free training. The second part is the effective model architecture design, where the authors reconstruct the internal structure details. Comprehensive experiments are performed to validate that YOLOv10 strikes a balance between efficiency and accuracy well.", "questions": "1. This End-to-End paradigm is still based on the anchor-based dense detection paradigm, so the final prediction results have a lot of background. Usually, there are only less than 300 objects in a picture, so there will be almost 8000 invalid detections in YOLOs, which is undoubtedly a waste. In terms of efficiency, I think it is a bit inferior to the object query of the DETR framework.", "limitations": "The authors adequately addressed the limitations.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The authors propose a good lightweight detection framework and effectively push the YOLO framework to the End-to-End inference paradigm, which is undoubtedly admirable.\n2. The manuscript is explicit and well-organized.\n3. Experimental validation is sufficient and solid. The authors conduct comprehensive experiments on various metrics and show a large improvement, which is impressive. Furthermore, the ablation study is in detail.", "weaknesses": "1. The novelty of the paper is weak. First, the “dual assignments” is similar to H-DETR [1], where the hybrid matching scheme has been proposed, and is consistent with the dual assignment of LRANet [2]. Besides, the lightweight classification head and spatial-channel decoupled downsampling have been proposed by DAMO-YOLO [3] and large-kernel convolution is common in previous methods. The paper is more like a technical report.\n2. More lightweight detectors should be listed in Table 1, including DAMO-YOLO, and YOLOv7. Besides, the authors should specify at what batch size the delay is calculated.\n\n[1] Jia, Ding, et al. \"Detrs with hybrid matching.\" *Proceedings of the IEEE/CVF conference on computer vision and pattern recognition*. 2023.\n\n[2] Su, Yuchen, et al. \"LRANet: Towards Accurate and Efficient Scene Text Detection with Low-Rank Approximation Network.\" *Proceedings of the AAAI Conference on Artificial Intelligence*. Vol. 38. No. 5. 2024.\n\n[3] Xu, Xianzhe, et al. \"Damo-yolo: A report on real-time object detection design.\" *arXiv preprint arXiv:2211.15444* (2022).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your valuable feedback and insightful comments. Thank you for liking the end-to-end framework, well-organized writing, and solid experimental validation. We provide the response to each comment in the point-to-point manner below. Please feel free to contact us if you have further concerns.\n\n**Q1-1: \"The “dual assignments” is similar to H-DETR and LRANet. \"**\n\n**A1-1**: Thanks. While there are structural similarities in the dual assignments among H-DETR, LRANet, and ours, we think that the specific label assignment mechanisms employed differ between ours and the mentioned works. Specifically, LRANet employs the dense assignment and sparse assignment branches, which all belong to the one-to-many assignment, while we adopt the one-to-many and one-to-one branches. H-DETR introduces extra queries and repeats the ground truth several times to perform one-to-many matching using bipartite matching, while we leverage the prediction aware one-to-many matching with the spatial prior of anchor points. Furthermore, differently, we introduce the consistent matching metric for YOLOs, aiming to narrow the theoretical supervision gap between the one-to-many and one-to-one branches. It effectively improves the supervision alignment between two branches and results in performance enhancement without hyper-parameter tuning. We will incorporate more discussions with the mentioned works in the revision.\n\n**Q1-2: \"The lightweight classification head and spatial-channel decoupled downsampling have been proposed by DAMO-YOLO\"**\n\n**A1-2**: Thanks. While our lightweight classification head and spatial-channel decoupled downsampling have structural similarities to the ZeroHead and MobBlock used in DAMO-YOLO, we think that they are motivated by different considerations and have distinct architectural designs. Specifically, the ZeroHead in DAMO-YOLO is intended to balance the computational cost between the neck and head by using a single linear layer for both classification and regression tasks. In contrast, our approach addresses the redundancy in the classification head relative to the regression head by proposing a lightweight design for the classification task. Moreover, DAMO-YOLO applies the MobBlock only to the lightweight model through NAS, which also results in the identical structure for the basic block and downsampling module. Differently, we simplify the downsampling process for all model scales to reduce the computational redundancy, which is also decoupled from the basic block design. We understand this concern because of the similarity. However, we also introduce other new architectural designs for YOLOs including rank-guided block design and partial self-attention module, which help to achieve better performance and efficiency. \n\n**Q1-3: \"Large-kernel convolution is common in previous methods.\"**\n\n**A1-3**: Thanks. Large kernel convolution is widely adopted in previous works due to its ability to effectively increase the receptive field. In contrast to prior studies, our findings indicate that the benefits of large kernel convolution vary across different model scales of YOLOs. Specifically, we observe that it provides performance gains for small-scale models but no improvements for larger models. Consequently, we use large kernel convolutions based on the model size, achieving a better efficient-accuracy trade-off. Besides, we also introduce the new architectural designs for YOLOs, including the rank-guided block design and partial self-attention module, which bring favorable benefits.\n\n**Q1-4: \"The paper is more like a technical report.\"**\n\n**A1-4**: Thanks. We understand this concern. We think that there are some academic inspirations we want to share with the community, especially for YOLO research. Therefore, we would like to revisit our motivation, findings, and contributions here. Please refer to the **Discussion 1** in the global response for the specific content.\n\n**Q2-1: \"More lightweight detectors should be listed in Table 1, including DAMO-YOLO, and YOLOv7.\"**\n\n**A2-1**: Thanks. We present the comparisons with more lightweight detectors, including DAMO-YOLO and YOLOv7, as below. We will incorporate these results in the revision.\n\n|Model|Param. (M)|FLOPs (G)|AP$^{val}$ (%)|Latency (ms)|\n|-------------|----------|---------|--------------|------------|\n|DAMO-YOLO-T|8.5|18.1|42.0|2.21|\n|DAMO-YOLO-S|16.3|37.8|46.0|3.18|\n|**YOLOv10-S**|7.2|21.6|46.3|2.49|\n|DAMO-YOLO-M|28.2|61.8|49.2|4.57|\n|DAMO-YOLO-L|42.1|97.3|50.8|6.48|\n|**YOLOv10-M**|15.4|59.1|51.1|4.74|\n|YOLOv7|36.9|104.7|51.2|17.03|\n|**YOLOv10-B**|19.1|92.0|52.5|5.74|\n|YOLOv7-X|71.3|189.9|52.9|21.45|\n|**YOLOv10-L**|24.4|120.3|53.2|7.28|\n\n**Q2-2: \"What batch size the delay is calculated\"**\n\n**A2-2**: Thanks. We follow previous works [1,2] and adopt the batch size of 1 for measuring the delay. \n\n**Q3: \"Discussion with the DETR framework.\"**\n\n**A3**: Thanks for your insightful discussion. Considering that the YOLOs and the DETR framework are two important topics as independent techniques for modern object detection, a comprehensive comparison between them is challenging yet highly interesting and meaningful. We think that this is a trade-off in the design of detectors and they may complement each other and advance together. On the one hand, DETR enjoys fewer invalid predictions compared with the anchor-based detection paradigm by utilizing the object query design, which shows less waste. On the other hand, the anchor-based detection paradigm directly outputs predictions without introducing the transformer decoder to interact with object queries, which simplifies the deployment. Given that both paradigms have the respective following works in the community, we believe that the best of these two paradigms may complement each other to achieve higher efficiency and performance in future works.\n\n[1] DETRs Beat YOLOs on Real-time Object Detection, CVPR 2024.\n\n[2] YOLOv8.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "QGmvA9cOqR", "reviewer": "Reviewer_snfm", "summary": "This paper presents a one-stage object detector that introduces a consistent dual assignments strategy, effectively eliminating the need for NMS and significantly accelerating inference speed with minimal impact on accuracy. Additionally, the paper explores a series of model designs aimed at balancing efficiency and accuracy, enhancing overall model performance.", "questions": "See weakness. Besides, you mention that intrinsic ranks indicate redundancy, leading to the replacement of certain blocks with CIB in high redundancy stages. After these modifications, do the intrinsic ranks in these stages actually decrease?", "limitations": "The paper discusses their limitations in the appendix.", "rating": 7, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The introduction of a consistent dual assignments strategy enables the one-stage detector to operate without NMS, significantly boosting inference speed.\n2. The focus on efficiency-driven model design minimizes accuracy loss while improving the model's efficiency.\n3. The incorporation of large-kernel convolutions and partial self-attention mechanisms enhances model performance with minimal additional computational cost.", "weaknesses": "1. The paper could benefit from discussing related works on hybrid or multiple group label assignment strategies, which are prevalent in recent DETR-based detectors.\n\n2. The implementation of a one-to-many head as an auxiliary head likely increases training costs. The paper lacks comparative data on training time and costs, which should be addressed to provide a comprehensive evaluation of the model's efficiency.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your valuable feedback and insightful comments. Thank you for liking the consistent dual assignments strategy, efficiency-accuracy driven model designs, and the improved performance and speed. We provide the response to each comment in the point-to-point manner below. Please feel free to contact us if you have further concerns.\n\n**Q1: \"The paper could benefit from discussing related works on hybrid or multiple group label assignment strategies, which are prevalent in recent DETR-based detectors.\"**\n\n**A1**: Thanks for this suggestion. We will incorporate more discussion with the related works in the revision. In improving upon DETR, previous works, including H-DETR [1], Group-DETR [2], MS-DETR [3], etc., introduce one-to-many matching in conjunction with the original one-to-one matching by hybrid or multiple group label assignments, to accelerate the training convergence and improve the performance. For example, H-DETR introduces the hybrid branch/epoch/layer schemes, Group-DETR presents group-wise one-to-many assignment, and MS-DETR imposes one-to-many supervision on queries of the primary decoder. Similarly, we introduce dual label assignments including the one-to-one matching and the original one-to-many matching for YOLOs, to provide rich supervision during training and high efficiency during inference. Differently, to achieve the one-to-many matching, they usually introduce extra queries or repeat ground truths for bipartite matching, or select top several queries from the matching scores, while we adopt the prediction aware assignment that incorporates the spatial prior. Besides, we analyze the supervision gap between the two heads and present the consistent matching metric for YOLOs to reduce the theoretical supervision gap. It improves performance through better supervision alignment and eliminates the need for hyper-parameter tuning. \n\n**Q2: \"The implementation of a one-to-many head as an auxiliary head likely increases training costs. The paper lacks comparative data on training time and costs, which should be addressed to provide a comprehensive evaluation of the model's efficiency.\"**\n\n**A2**: Thanks. We measure the training cost on 8 NVIDIA 3090 GPUs and present the results with and without the auxiliary head based on YOLOv10-M as below. We also compare our training cost with other detectors using the official codebases. We observe that benefiting from the lightweight design, introducing the auxiliary head results in a small increase in the training time, about 18s each epoch, which is affordable. To fairly verify the benefit of the auxiliary head, we also adopt longer 550 training epochs for YOLOv10-M w/o auxiliary head, which leads to a similar training time (29.3 vs. 29.1 hours) but still yields inferior performance (48.9% vs. 51.1% AP) compared with the model w/ auxiliary head. Besides, we also note that YOLOv10 shows a relatively low training cost compared with others. We will incorporate more results and analyses about the training time and costs in the revision.\n\n|Model|Training Time (min/epoch)|Epochs|Total Time (hour)|\n|--------------------------------|-------------------------|------|-----------------|\n|YOLOv6-3.0-M|8.3|300|41.7|\n|YOLOv8-M|3.3|500|27.3|\n|YOLOv9-M|4.9|500|40.7|\n|Gold-YOLO-M|12.8|300|63.8|\n|YOLO-MS|8.5|300|42.3|\n|**YOLOv10-M** w/o auxiliary head|3.2|500|26.7|\n|**YOLOv10-M** w/ auxiliary head|3.5|500|29.1|\n\n**Q3: \"Besides, you mention that intrinsic ranks indicate redundancy, leading to the replacement of certain blocks with CIB in high redundancy stages. After these modifications, do the intrinsic ranks in these stages actually decrease?\"**\n\n**A3**: Thanks. We would like to clarify that a lower intrinsic rank indicates higher redundancy, and a higher rank implies more condensed parameters. For example, given a parameter matrix $W$, a low intrinsic rank suggests that it can be replaced through low-rank approximation with fewer parameters. Such an approximation is not to improve the rank of the original $W$, but to reduce its redundancy of parameters. Similarly for our work, we explore the intrinsic rank to guide the replacement with CIB in the high redundancy stages, to reduce the computational costs while maintaining the model capacity. As shown in Tab. 5 in the paper, it can result in the competitive performance but with fewer parameters and less computation. In Fig.2 in the global response pdf, we visualize the intrinsic ranks after the modifications. For the CIB block, we calculate the numerical rank of the last 1\\*1 convolution with major parameters. We observe that the resulting models with fewer parameters exhibit the similar ranks to before, thereby maintaining the competitive capacity compared with the original models, while enjoying faster inference speed. We also think that improving the intrinsic rank to obtain better effectiveness and efficiency is an interesting idea and we leave it for the future work.\n\n[1] DETRs with Hybrid Matching, CVPR 2023\n\n[2] Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignment, ICCV 2023.\n\n[3] MS-DETR: Efficient DETR Training with Mixed Supervision, CVPR 2024.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "WmaTu4VH1L", "reviewer": "Reviewer_qWaL", "summary": "This paper further advances the performance-efficiency boundary of YOLOs from both the post-processing and the model architecture. Specifically, the paper presents the consistent dual assignments for NMS-free training of YOLOs, which brings the competitive performance and low inference latency simultaneously. In addition, this paper performs engineering optimizations on components of YOLOs in terms of speed and accuracy to achieve optimal performance.", "questions": "Please answer the questions stated in the weakness.", "limitations": "See Weaknesses.", "rating": 5, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. According to results in the paper, YOLOv10 achieves the best trade-off between speed and accuracy.\n2. The idea of ​​removing NMS for YOLOs to improve is intuitive, and the advantages of NMS-free are analyzed in detail in the paper of RT-DETR[1].\n3. The  the consistent dual assignments for NMS-free training is effective and significantly improves the end-to-end inference latency of YOLOs.\n4. The holistic efficiency-accuracy driven optimizations listed in this paper is in line with the original design intention of the real-time detector.\n\n[1] Zhao, Yian, et al. \"Detrs beat yolos on real-time object detection.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.", "weaknesses": "1. The motivation of this paper is not clear, and it seems to be intended to further optimize YOLOs from an engineering perspective to achieve better results. The analysis and solutions on NMS and model architecture are almost from existing technologies in the detection field. For example, Hbrid-DETR[1] proposed the drawbacks of one-to-one matching to achieve NMS-free, and then adopted a solution combining one-to-one matching and one-to-many matching; the results of this paper are inspiring, but more like a technical report, providing a stronger baseline for the field of real-time detection.\n2. The paper claims that \"the inherent complexity in deploying DETRs impedes its ability to attain the optimal balance between accuracy and speed.\" This seems to lack the necessary evidence. RT-DETR retains most of the components of DETRs and achieves strong results, greatly improving the detection speed and accuracy of DETRs and providing multiple deployment options.\n3. If possible, please provide speed comparison results of YOLOv10 and competitors on other hardware.\n\n[1] Jia, Ding, et al. \"Detrs with hybrid matching.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your valuable feedback and insightful comments. Thank you for liking the speed-accuracy trade-offs of YOLOv10, the effectiveness of NMS-free training, and the design intention of efficiency-accuracy driven architectural optimizations. We provide the response to each comment in the point-to-point manner below. Please feel free to contact us if you have further concerns.\n\n**Q1-1: \"The motivation of this paper is not clear. The analysis and solutions on NMS and model architecture are almost from existing technologies in the detection field.\" and \" The results of this paper are inspiring, but more like a technical report.\"**\n\n**A1-1**: Thanks. We understand this concern. We think that there are some academic insights we wish to share with the community for further works on improving YOLOs. Therefore, we would like to discuss our motivation, observations, and contributions here. Our work seeks to improve YOLOs from the whole pipeline, including the post-processing and the architectural design. (1) To improve the post-processing step, similar to previous works [1], we find that the one-to-one matching, while eliminating the need for NMS, suffers from limited supervision and leads to inferior performance for YOLOs, as shown in Tab.3 in the paper. Therefore, we introduce dual label assignments including one-to-one matching and the original one-to-many matching for YOLOs to enrich the supervision during training and simplify the post-processing during inference. Furthermore, motivated by the observation that the supervision gap between two matching ways adversely affects the performance, as shown in Tab.4 and Fig.2.(b) in the paper, we propose the consistent matching metric for YOLOs to minimize the theoretical supervision gap. It brings improved supervision alignment and enhances performance without requiring hyper-parameter tuning. (2) To improve the architectural design, like previous works including ConvNeXt [2] and MobileLLM [3], we note that a systematical and comprehensive inspection for various components can further improve the efficiency and accuracy of YOLOs, which is lacking. This motivates us to holistically analyze and enhance the architectural designs. In addition to introducing existing designs, we also propose the new rank-guided block design and partial self-attention module to enhance the YOLOs. These efforts lead to favorable performance and efficiency improvements. We hope that the insights of our work can benefit the community and inspire further research. For example, we think that by simplifying the post-processing, the NMS-free training strategy leaves more room for the model itself, which may facilitate more advanced architectural optimizations and designs.\n\n**Q1-2: \"For example, Hybrid-DETR[1] proposed the drawbacks of one-to-one matching and combined one-to-one matching and one-to-many matching.\"**\n\n**A1-2**: Thanks. While both having one-to-one and one-to-many matching, we think that the specific label assignment ways differ between Hybrid-DETR [1] and ours. Specifically, to achieve one-to-many matching, Hybrid-DETR introduces extra queries and repeats ground truth multiple times for bipartite matching, whereas we adopt the prediction-aware matching with the spatial prior of anchor points. Furthermore, differently, we theoretically analyze the supervision gap between two matching ways for YOLOs and present the consistent matching metric to reduce the supervision gap. It results in better performance without hyper-parameter tuning by improving the supervision alignment. We will incorporate more discussion with the mentioned work in the revision.\n\n**Q2: \"The claim of \"the inherent complexity in deploying DETRs impedes its ability to attain the optimal balance between accuracy and speed\".\"**\n\n**A2**: Thanks. We understand your concern regarding the clarity of the statement. We think that RT-DETR effectively propels DETRs into the realm of real-time detection. On the other hand, when only considering the forward process of model, we observe that its DETR-based architecture still lags behind the YOLO series models in terms of inference efficiency for certain model scales. For example, as shown in Tab. 1 in the paper, RT-DETR-R34 and RT-DETR-R18 show the higher latency of 1.12ms and 2.16ms compared with YOLOv8-M and YOLOv8-S respectively. We will revise the statement as below for more clear presentation in the revision: \"when considering only the forward process of model during deployment, the efficiency of the DETRs still has room for improvements compared with YOLOs.\" If you have further concern regarding the revised statement, please let us know.\n\n**Q3: \"Speed comparison on other hardware.\"**\n\n**A3**: Thanks. We present the speed comparison results of YOLOv10 and others on CPU (Intel Xeon Skylake, IBRS) using OpenVINO as below. We observe that YOLOv10 also shows superior performance and efficiency trade-offs. We will incorporate more results on other hardware if available, e.g., NPU, in future works.\n\n| Model| AP$^{val}$ (%)|Latency (ms)|\n|-------------|--------------|------------|\n|YOLOv6-3.0-N|37.0|32.22|\n|Gold-YOLO-N|39.6|36.35|\n|YOLOv8-N|37.3|32.91|\n|**YOLOv10-N**|38.5|27.84|\n|YOLOv6-3.0-S|44.3|93.11|\n|Gold-YOLO-S|45.4|98.08|\n|YOLO-MS-S|46.2|113.54|\n|YOLOv8-S|44.9|79.56|\n|RT-DETR-R18|46.5|157.40|\n|**YOLOv10-S**|46.3|67.70|\n|YOLOv6-3.0-M|49.1|174.82|\n|Gold-YOLO-M|49.8|188.33|\n|YOLO-MS|51.0|214.28|\n|YOLOv8-M|50.6|172.76|\n|RT-DETR-R34|48.9|222.82|\n|**YOLOv10-M**|51.1|147.99|\n|YOLOv6-3.0-L|51.8|325.85|\n|Gold-YOLO-L|51.8|344.99|\n|YOLOv9-C|52.5|244.14|\n|**YOLOv10-B**|52.5|210.98|\n|YOLOv8-L|52.9|330.02|\n|RT-DETR-R50|53.1|332.76|\n|**YOLOv10-L**|53.2|279.59|\n|YOLOv8-X|53.9|535.23|\n|RT-DETR-R101|54.3|570.25|\n|**YOLOv10-X**|54.4|399.87|\n\n[1] DETRs with Hybrid Matching, CVPR 2023.\n\n[2] A ConvNet for the 2020s, CVPR 2022.\n\n[3] MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases,  ICML 2024.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
**Discussion 1: About the technical novelty.**

Thanks. We would like to discuss more about our motivation, findings, and contributions. 

Our motivation stems from optimizing the detection pipeline of YOLOs, including the NMS post-processing and model architectural design. (1) For the post-processing, we find that simply adopting the one-to-one matching for YOLOs results in notable performance degradation due to the limited supervision signals, as shown in Tab.3 in the paper. This motivates us to introduce dual label assignments for YOLOs to enrich the supervision during training and eliminate the need for NMS during inference simultaneously. We further observe that the supervision gap between the two branches leads to inferior performance, as shown in Fig. 2.(b) and Tab.4 in the paper. To address this, we theoretically analyze the supervision gap and present the consistent matching metric for YOLOs to improve the supervision alignment. It leads to favorable performance enhancement. (2) For the model architectural design, we note that a comprehensive inspection of various components in YOLOs from both efficiency and accuracy aspects is lacking. This motivates us to follow previous works like ConvNeXt [1] and MobileLLM [2], to systematically analyze and improve architectural designs. In addition to introducing the advanced design strategies, we also present the new rank-guided block design and partial self-attention module for YOLOs. The resulting model architecture exhibits favorable efficiency and accuracy gains. 

Besides, we think that the NMS-free training strategy leaves more room for further research in optimizing YOLO models from different aspects, including the architecture designs. The introduced architectural optimization can be regarded as an initial exploration toward efficient YOLO architecture. Therefore, we believe that our work can serve as a strong baseline and inspire further advancements. 

[1] A ConvNet for the 2020s, CVPR 2022.

[2] MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases,  ICML 2024.

Meta-review: This paper proposes YOLOv10, an advancement of the real-time YOLO series object detection methods. The reviewers have a consensus that the proposed method is effective, and the experiments are sufficient and solid, demonstrating impressive performance improvements. All reviewers acknowledged that their concerns were addressed by the authors in the rebuttal phase. The AC agrees with the reviewers and recommends Accept for this paper.
Decision: Accept (poster)