# YOLOv10: Real-Time End-to-End Object Detection

Ao Wang Hui Chen∗ Lihao Liu Kai Chen Zijia Lin
Jungong Han Guiguang Ding
Tsinghua University
Corresponding Author.

###### 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: https://github.com/THU-MIG/yolov10.

Figure 1: Comparisons with others in terms of latency-accuracy (left) and
size-accuracy (right) trade-offs. We measure the end-to-end latency using the
official pre-trained models.

##  1 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 [3], robot
navigation [11], and object tracking [66], _etc_. In recent years, researchers
have concentrated on devising CNN-based object detectors to achieve real-time
detection [18, 22, 43, 44, 45, 51, 12]. Among them, YOLOs have gained
increasing popularity due to their adept balance between performance and
efficiency [2, 19, 27, 19, 20, 59, 54, 64, 7, 65, 16, 27]. 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-latency boundaries.

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 performance, 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 [71]. One line to tackle this issue is to adopt the recently
introduced end-to-end DETR architectures [4, 74, 67, 28, 34, 40, 61]. For
example, RT-DETR [71] presents an efficient hybrid encoder and uncertainty-
minimal query selection, propelling DETRs into the realm of real-time
applications. Nevertheless, the inherent complexity in deploying DETRs impedes
its ability to attain the optimal balance between accuracy and speed. Another
line is to explore end-to-end detection for CNN-based detectors, which
typically leverages one-to-one assignment strategies to suppress the redundant
predictions [5, 49, 60, 73, 16]. However, they usually introduce additional
inference overhead or achieve suboptimal performance.

Furthermore, the model architecture design remains a fundamental challenge for
YOLOs, which exhibits an important impact on the accuracy and speed [45, 16,
65, 7]. To achieve more efficient and effective model architectures,
researchers have explored different design strategies. Various primary
computational units are presented for the backbone to enhance the feature
extraction ability, including DarkNet [43, 44, 45], CSPNet [2], EfficientRep
[27] and ELAN [56, 58], _etc_. For the neck, PAN [35], BiC [27], GD [54] and
RepGFPN [65], _etc_., are explored to enhance the multi-scale feature fusion.
Besides, model scaling strategies [56, 55] and re-parameterization [10, 27]
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 a
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 competitive performance with high efficiency.
Secondly, we propose the holistic efficiency-accuracy 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 to enhance
the model capability, harnessing the potential for performance improvements
under low cost.

Based on these approaches, we succeed in achieving a new family of real-time
end-to-end detectors with different model scales, _i.e_., YOLOv10-N / S / M /
B / L / X. Extensive experiments on standard benchmarks for object detection,
_i.e_., COCO [33], demonstrate that our YOLOv10 can significantly outperform
previous state-of-the-art models in terms of computation-accuracy trade-offs
across various model scales. As shown in Fig. 1, our YOLOv10-S / X are
1.8$\times$ / 1.3$\times$ faster than RT-DETR-R18 / R101, respectively, under
the similar performance. Compared with YOLOv9-C, YOLOv10-B achieves a 46%
reduction in latency with the same performance. Moreover, YOLOv10 exhibits
highly efficient parameter utilization. Our YOLOv10-L / X outperforms YOLOv8-L
/ X by 0.3 AP and 0.5 AP, with 1.8$\times$ and 2.3$\times$ smaller number of
parameters, respectively. YOLOv10-M 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 [18, 51, 43, 32, 72, 69, 30, 29, 39].
Particularly, the YOLO series [43, 44, 45, 2, 19, 27, 56, 20, 59] stand out as
the mainstream ones. YOLOv1, YOLOv2, and YOLOv3 identify the typical detection
architecture consisting of three parts, _i.e_., backbone, neck, and head [43,
44, 45]. YOLOv4 [2] and YOLOv5 [19] introduce the CSPNet [57] design to
replace DarkNet [42], coupled with data augmentation strategies, enhanced PAN,
and a greater variety of model scales, _etc_. YOLOv6 [27] presents BiC and
SimCSPSPPF for neck and backbone, respectively, with anchor-aided training and
self-distillation strategy. YOLOv7 [56] introduces E-ELAN for rich gradient
flow path and explores several trainable bag-of-freebies methods. YOLOv8 [20]
presents C2f building block for effective feature extraction and fusion. Gold-
YOLO [54] provides the advanced GD mechanism to boost the multi-scale feature
fusion capability. YOLOv9 [59] 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 a
paradigm shift from traditional pipelines, offering streamlined architectures
[48]. 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 to enhance its performance and efficiency [40, 61, 50, 28, 34].
Deformable-DETR [74] leverages multi-scale deformable attention module to
accelerate the convergence speed. DINO [67] integrates contrastive denoising,
mix query selection, and look forward twice scheme into DETRs. RT-DETR [71]
further designs the efficient hybrid encoder and proposes the uncertainty-
minimal query selection to improve both the accuracy and latency. Another line
to achieve end-to-end object detection is based CNN detectors. Learnable NMS
[23] and relation networks [25] present another network to remove duplicated
predictions for detectors. OneNet [49] and DeFCN [60] propose one-to-one
matching strategies to enable end-to-end object detection with fully
convolutional networks. FCOS${}_{\text{pss}}$ [73] introduces a positive
sample selector to choose the optimal sample for prediction.

##  3 Methodology

###  3.1 Consistent Dual Assignments for NMS-free Training

During training, YOLOs [20, 59, 27, 64] usually leverage TAL [14] to allocate
multiple positive samples 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 on
the NMS post-processing, which causes the suboptimal inference efficiency for
deployment. While previous works [49, 60, 73, 5] 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-free 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 [75]. Fortunately, this deficiency can be
compensated for by the one-to-many assignment [5]. To achieve this, we
introduce dual label assignments for YOLOs to combine the best of both
strategies. Specifically, as shown in Fig. 2.(a), we incorporate another one-
to-one head for YOLOs. It retains the identical structure and adopts the same
optimization objectives as the original one-to-many branch but leverages the
one-to-one matching to 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.

Figure 2: (a) Consistent dual assignments for NMS-free training. (b) Frequency
of one-to-one assignments in Top-1/5/10 of one-to-many results for YOLOv8-S
which employs $\alpha_{o2m}$=0.5 and $\beta_{o2m}$=6 by default [20]. For
consistency, $\alpha_{o2o}$=0.5; $\beta_{o2o}$=6. For inconsistency,
$\alpha_{o2o}$=0.5; $\beta_{o2o}$ =2.

Consistent matching metric. During assignments, both one-to-one and one-to-
many approaches leverage a metric to quantitatively assess the level of
concordance between predictions and instances. To achieve prediction aware
matching for both branches, we employ a uniform matching metric, _i.e_.,

$m(\alpha,\beta)=s\cdot p^{\alpha}\cdot\text{IoU}(\hat{b},b)^{\beta},$ (1)

where $p$ is the classification score, $\hat{b}$ 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 [20,
59, 27, 64]. $\alpha$ and $\beta$ 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
$m_{o2m}$=$m(\alpha_{o2m},\beta_{o2m})$ and
$m_{o2o}$=$m(\alpha_{o2o},\beta_{o2o})$, respectively. These metrics influence
the label assignments and supervision information for the two heads.

In dual label assignments, the 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, we 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
prediction-instance 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 IoU with
predictions as $u^{*}$, and the largest one-to-many and one-to-one matching
scores as $m_{o2m}^{*}$ and $m_{o2o}^{*}$, respectively. Suppose that one-to-
many branch yields the positive samples $\Omega$ and one-to-one branch selects
$i$-th prediction with the metric $m_{o2o,i}$=$m_{o2o}^{*}$, we can then
derive the classification target
$t_{o2m,j}$=$u^{*}\cdot\frac{m_{o2m,j}}{m_{o2m}^{*}}\leq u^{*}$ for
$j\in\Omega$ and $t_{o2o,i}$=$u^{*}\cdot\frac{m_{o2o,i}}{m_{o2o}^{*}}$=$u^{*}$
for task aligned loss as in [20, 59, 27, 64, 14]. The supervision gap between
two branches can thus be derived by the 1-Wasserstein distance [41] of
different classification objectives, _i.e_.,

$A=t_{o2o,i}-\mathbb{I}(i\in\Omega)t_{o2m,i}+\sum\nolimits_{k\in\Omega\backslash\\{i\\}}t_{o2m,k},$
(2)

We can observe that the gap decreases as $t_{o2m,i}$ increases, _i.e_., $i$
ranks higher within $\Omega$. It reaches the minimum when $t_{o2m,i}$=$u^{*}$,
_i.e_., $i$ is the best positive sample in $\Omega$, as shown in Fig. 2.(a).
To achieve this, we present the consistent matching metric, _i.e_.,
$\alpha_{o2o}$=$r\cdot\alpha_{o2m}$ and $\beta_{o2o}$=$r\cdot\beta_{o2m}$,
which implies $m_{o2o}$=$m_{o2m}^{r}$. Therefore, the best positive sample for
one-to-many head is also the best for one-to-one head. Consequently, both
heads can be optimized consistently and harmoniously. For simplicity, we take
$r$=1, by default, _i.e_., $\alpha_{o2o}$=$\alpha_{o2m}$ and
$\beta_{o2o}$=$\beta_{o2m}$. To verify the improved supervision alignment, we
count the number of one-to-one matching pairs within the top-1 / 5 / 10 of the
one-to-many results after training. As shown in Fig. 2.(b), the alignment is
improved under the consistent matching metric. For a more comprehensive
understanding of the mathematical proof, please refer to the appendix.

Figure 3: (a) The intrinsic ranks across stages and models in YOLOv8. The
stage in the backbone and neck is numbered in the order of model forward
process. The numerical rank $r$ is normalized to $r/C_{o}$ for y-axis and its
threshold is set to $\lambda_{max}/2$, by default, where $C_{o}$ denotes the
number of output channels and $\lambda_{max}$ is the largest singular value.
It 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).

###  3.2 Holistic Efficiency-Accuracy Driven Model Design

In addition to the post-processing, the model architectures of YOLOs also pose
great challenges to the efficiency-accuracy trade-offs [45, 7, 27]. Although
previous works explore various design strategies, the comprehensive inspection
for various components in YOLOs is still lacking. Consequently, the model
architecture exhibits non-negligible computational redundancy and constrained
capability, which impedes its potential for achieving high efficiency and
performance. Here, we aim to 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) Lightweight 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/1.51M) are 2.5$\times$ and 2.4$\times$
those of the regression head (2.34G/0.64M) in YOLOv8-S, respectively. However,
after analyzing the impact of classification error and the regression error
(seeing Tab. 9), 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, we simply adopt a lightweight architecture for the classification
head, which consists of two depthwise separable convolutions [24, 8] with the
kernel size of 3$\times$3 followed by a 1$\times$1 convolution.

(2) Spatial-channel decoupled downsampling. YOLOs typically leverage regular
3$\times$3 standard convolutions with stride of 2, achieving spatial
downsampling (from $H\times W$ to $\frac{H}{2}\times\frac{W}{2}$) and channel
transformation (from $C$ to $2C$) simultaneously. This introduces non-
negligible computational cost of $\mathcal{O}(\frac{9}{2}HWC^{2})$ and
parameter count of $\mathcal{O}(18C^{2})$. 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 to
perform spatial downsampling. This reduces the computational cost to
$\mathcal{O}(2HWC^{2}+\frac{9}{2}HWC)$ and the parameter count to
$\mathcal{O}(2C^{2}+18C)$. Meanwhile, it maximizes information retention
during downsampling, leading to competitive performance with latency
reduction.

(3) Rank-guided block design. YOLOs usually employ the same basic building
block for all stages [27, 59], _e.g_., the bottleneck block in YOLOv8 [20]. To
thoroughly examine such homogeneous design for YOLOs, we utilize the intrinsic
rank [31, 15] to analyze the redundancy111A lower rank implies greater
redundancy, while a higher rank signifies more condensed information. of each
stage. Specifically, we calculate 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 YOLOv8,
indicating that deep stages and large models are prone to 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 a 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 in Fig.
3.(b). It can serve as the efficient basic building block, _e.g_., embedded in
the ELAN structure [58, 20] (Fig. 3.(b)). Then, we advocate a rank-guided
block allocation strategy to achieve the best efficiency while maintaining
competitive capacity. Specifically, given a model, we sort its all stages
based on their intrinsic ranks in ascending order. We further inspect the
performance variation of replacing the basic block in the leading stage with
CIB. If there is no performance degradation compared with the given model, we
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 to the page limit, we provide the details of the algorithm in
the appendix.

Accuracy driven model design. We further explore the large-kernel convolution
and self-attention for accuracy driven design, aiming to boost the performance
under minimal cost.

(1) Large-kernel convolution. Employing large-kernel depthwise convolution is
an effective way to enlarge the receptive field and enhance the model’s
capability [9, 38, 37]. 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 [7]. 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$\times$3 depthwise convolution in the CIB to
7$\times$7, following [37]. Additionally, we employ the structural
reparameterization technique [10, 9, 53] to bring another 3$\times$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 [52] is widely employed in
various visual tasks due to its remarkable global modeling capability [36, 13,
70]. However, it exhibits high computational complexity and memory footprint.
To address this, in light of the prevalent attention head redundancy [63], we
present an efficient partial self-attention (PSA) module design, as shown in
Fig. 3.(c). Specifically, we evenly partition the features across channels
into two parts after the 1$\times$1 convolution. We only feed one part into
the $N_{\text{PSA}}$ blocks comprised of multi-head self-attention module
(MHSA) and feed-forward network (FFN). Two parts are then concatenated and
fused by a 1$\times$1 convolution. Besides, we follow [21] to assign the
dimensions of the query and key to half of that of the value in MHSA and
replace the LayerNorm [1] with BatchNorm [26] for fast inference. Furthermore,
PSA is only placed after the Stage 4 with the lowest resolution, avoiding the
excessive overhead from the 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.

Table 1: Comparisons with state-of-the-arts. Latency is measured using official pre-trained models. Latencyf denotes the latency in the forward process of model without post-processing. $\dagger$ 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 or PGI for fair comparisons. Model | #Param.(M) | FLOPs(G) |  APval(%) | Latency(ms) |  Latencyf(ms)
---|---|---|---|---|---
YOLOv6-3.0-N [27] | 4.7 | 11.4 | 37.0 | 2.69 | 1.76
Gold-YOLO-N [54] | 5.6 | 12.1 | 39.6 | 2.92 | 1.82
YOLOv8-N [20] | 3.2 | 8.7 | 37.3 | 6.16 | 1.77
YOLOv10-N (Ours) | 2.3 | 6.7 | 38.5 / 39.5† | 1.84 | 1.79
YOLOv6-3.0-S [27] | 18.5 | 45.3 | 44.3 | 3.42 | 2.35
Gold-YOLO-S [54] | 21.5 | 46.0 | 45.4 | 3.82 | 2.73
YOLO-MS-XS [7] | 4.5 | 17.4 | 43.4 | 8.23 | 2.80
YOLO-MS-S [7] | 8.1 | 31.2 | 46.2 | 10.12 | 4.83
YOLOv8-S [20] | 11.2 | 28.6 | 44.9 | 7.07 | 2.33
YOLOv9-S [59] | 7.1 | 26.4 | 46.7 | - | -
RT-DETR-R18 [71] | 20.0 | 60.0 | 46.5 | 4.58 | 4.49
YOLOv10-S (Ours) | 7.2 | 21.6 | 46.3 / 46.8† | 2.49 | 2.39
YOLOv6-3.0-M [27] | 34.9 | 85.8 | 49.1 | 5.63 | 4.56
Gold-YOLO-M [54] | 41.3 | 87.5 | 49.8 | 6.38 | 5.45
YOLO-MS [7] | 22.2 | 80.2 | 51.0 | 12.41 | 7.30
YOLOv8-M [20] | 25.9 | 78.9 | 50.6 | 9.50 | 5.09
YOLOv9-M [59] | 20.0 | 76.3 | 51.1 | - | -
RT-DETR-R34 [71] | 31.0 | 92.0 | 48.9 | 6.32 | 6.21
RT-DETR-R50m [71] | 36.0 | 100.0 | 51.3 | 6.90 | 6.84
YOLOv10-M (Ours) | 15.4 | 59.1 | 51.1 / 51.3† | 4.74 | 4.63
YOLOv6-3.0-L [27] | 59.6 | 150.7 | 51.8 | 9.02 | 7.90
Gold-YOLO-L [54] | 75.1 | 151.7 | 51.8 | 10.65 | 9.78
YOLOv9-C [59] | 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 [20] | 43.7 | 165.2 | 52.9 | 12.39 | 8.06
RT-DETR-R50 [71] | 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 [20] | 68.2 | 257.8 | 53.9 | 16.86 | 12.83
RT-DETR-R101 [71] | 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

##  4 Experiments

###  4.1 Implementation Details

We select YOLOv8 [20] as our baseline model, due to its commendable latency-
accuracy balance and its availability in various model sizes. We employ the
consistent dual assignments for NMS-free training and perform holistic
efficiency-accuracy driven model design based on it, which brings our YOLOv10
models. YOLOv10 has the same variants as YOLOv8, _i.e_., N / S / M / L / 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 [33] under
the same training-from-scratch setting [20, 59, 56]. Moreover, the latencies
of all models are tested on T4 GPU with TensorRT FP16, following [71].

###  4.2 Comparison with state-of-the-arts

As shown in Tab. 1, our YOLOv10 achieves the state-of-the-art performance and
end-to-end latency across various model scales. We first compare YOLOv10 with
our baseline models, _i.e_., YOLOv8. On N / S / M / L / X five variants, our
YOLOv10 achieves 1.2% / 1.4% / 0.5% / 0.3% / 0.5% AP improvements, with 28% /
36% / 41% / 44% / 57% fewer parameters, 23% / 24% / 25% / 27% / 38% less
calculations, and 70% / 65% / 50% / 41% / 37% lower latencies. Compared with
other YOLOs, YOLOv10 also exhibits superior trade-offs between accuracy and
computational cost. Specifically, for lightweight and small models, YOLOv10-N
/ S outperforms YOLOv6-3.0-N / S by 1.5 AP and 2.0 AP, with 51% / 61% fewer
parameters and 41% / 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 or better performance, respectively. For large
models, compared with Gold-YOLO-L, our YOLOv10-L shows 68% fewer parameters
and 32% lower latency, along with a significant improvement of 1.4% AP.
Furthermore, compared with RT-DETR, YOLOv10 obtains significant performance
and latency improvements. Notably, YOLOv10-S / X achieves 1.8$\times$ and
1.3$\times$ faster inference speed than RT-DETR-R18 / R101, respectively,
under the similar performance. These results well demonstrate the superiority
of YOLOv10 as the real-time end-to-end detector.

We also compare YOLOv10 with other YOLOs using the original one-to-many
training approach. We consider the performance and the latency of model
forward process (Latencyf) in this situation, following [56, 20, 54]. As shown
in Tab. 1, YOLOv10 also exhibits the state-of-the-art 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. It can be observed that our NMS-free training with
consistent dual assignments significantly reduces the end-to-end latency of
YOLOv10-S by 4.63ms, while maintaining competitive performance of 44.3% AP.
Moreover, our efficiency driven model design leads to the reduction of 11.8 M
parameters and 20.8 GFlOPs, with a considerable latency reduction of 0.65ms
for YOLOv10-M, well showing its effectiveness. Furthermore, our accuracy
driven model design achieves the notable improvements of 1.8 AP and 0.7 AP for
YOLOv10-S and YOLOv10-M, alone with only 0.18ms and 0.17ms latency overhead,
respectively, which well demonstrates its superiority.

Table 2: Ablation study with YOLOv10-S and YOLOv10-M on COCO. # | Model | NMS-free. | Efficiency. | Accuracy. | #Param.(M) | FLOPs(G) |  APval(%) | Latency(ms)
---|---|---|---|---|---|---|---|---
1 | YOLOv10-S |  |  |  | 11.2 | 28.6 | 44.9 | 7.07
2 | ✓ |  |  | 11.2 | 28.6 | 44.3 | 2.44
3 | ✓ | ✓ |  | 6.2 | 20.8 | 44.5 | 2.31
4 | ✓ | ✓ | ✓ | 7.2 | 21.6 | 46.3 | 2.49
5 | YOLOv10-M |  |  |  | 25.9 | 78.9 | 50.6 | 9.50
6 | ✓ |  |  | 25.9 | 78.9 | 50.3 | 5.22
7 | ✓ | ✓ |  | 14.1 | 58.1 | 50.4 | 4.57
8 | ✓ | ✓ | ✓ | 15.4 | 59.1 | 51.1 | 4.74

Table 3: Dual assign. o2m | o2o | AP | Latency
---|---|---|---
✓ |  | 44.9 | 7.07
| ✓ | 43.4 | 2.44
✓ | ✓ | 44.3 | 2.44

Table 4: Matching metric. $\alpha_{o2o}$ | $\beta_{o2o}$ |  APval | $\alpha_{o2o}$ | $\beta_{o2o}$ |  APval
---|---|---|---|---|---
0.5 | 2.0 | 42.7 | 0.25 | 3.0 | 44.3
0.5 | 4.0 | 44.2 | 0.25 | 6.0 | 43.5
0.5 | 6.0 | 44.3 | 1.0 | 6.0 | 43.9
0.5 | 8.0 | 44.0 | 1.0 | 12.0 | 44.3

Table 5: Efficiency. for YOLOv10-S/M. # | Model | #Param | FLOPs |  APval | Latency
---|---|---|---|---|---
1 | base. | 11.2/25.9 | 28.6/78.9 | 44.3/50.3 | 2.44/5.22
2 | +cls. | 9.9/23.2 | 23.5/67.7 | 44.2/50.2 | 2.39/5.07
3 | +downs. | 8.0/19.7 | 22.2/65.0 | 44.4/50.4 | 2.36/4.97
4 | +block. | 6.2/14.1 | 20.8/58.1 | 44.5/50.4 | 2.31/4.57

Analyses for NMS-free training.

  * •

Dual label assignments. We present dual label assignments for NMS-free YOLOs,
which can bring both rich supervision of one-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. 5, our dual label assignments achieve the best
AP-latency trade-off.

  * •

Consistent matching metric. We introduce consistent matching metric to 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
$\alpha_{o2o}$ and $\beta_{o2o}$. As shown in Tab. 5, the proposed consistent
matching metric, _i.e_., $\alpha_{o2o}$=$r\cdot\alpha_{o2m}$ and
$\beta_{o2o}$=$r\cdot\beta_{o2m}$, can achieve the optimal performance, where
$\alpha_{o2m}$=$0.5$ and $\beta_{o2m}$=$6.0$ in the one-to-many head [20].
Such an improvement can be 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.

Table 6: cls. results. | base. | +cls.
---|---|---
APval | 44.3 | 44.2
AP${}^{val}_{w/o\ c}$ | 59.9 | 59.9
AP${}^{val}_{w/o\ r}$ | 64.5 | 64.2

Table 7: Results of d.s. Model |  APval | Latency
---|---|---
base. | 43.7 | 2.33
ours | 44.4 | 2.36

Table 8: Results of CIB. Model |  APval | Latency
---|---|---
IRB | 43.7 | 2.30
IRB-DW | 44.2 | 2.30
ours | 44.5 | 2.31

Table 9: Rank-guided. Stages with CIB |  APval
---|---
empty | 44.4
8 | 44.5
8,4, | 44.5
8,4,7 | 44.3

Table 10: Accuracy. for S/M. # | Model |  APval | Latency
---|---|---|---
1 | base. | 44.5/50.4 | 2.31/4.57
2 | +L.k. | 44.9/- | 2.34/-
3 | +PSA | 46.3/51.1 | 2.49/4.74

Table 11: L.k. results. Model |  APval | Latency
---|---|---
k.s.=5 | 44.7 | 2.32
k.s.=7 | 44.9 | 2.34
k.s.=9 | 44.9 | 2.37
w/o rep. | 44.8 | 2.34

Table 12: L.k. usage. | w/o L.k. | w/ L.k.
---|---|---
N | 36.3 | 36.6
S | 44.5 | 44.9
M | 50.4 | 50.4

Table 13: PSA results. Model |  APval | Latency
---|---|---
PSA | 46.3 | 2.49
Trans. | 46.0 | 2.54
$N_{\text{PSA}}$ = 1 | 46.3 | 2.49
$N_{\text{PSA}}$ = 2 | 46.5 | 2.59

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 YOLOv10-S/M model without efficiency-accuracy
driven model design, _i.e_., #2/#6 in Tab. 2. As shown in Tab. 5, each design
component, including lightweight classification head, spatial-channel
decoupled downsampling, and rank-guided block design, contributes to the
reduction of parameters count, FLOPs, and latency. Importantly, these
improvements are achieved while maintaining competitive performance.

  * •

Lightweight classification head. We analyze the impact of category and
localization errors of predictions on the performance, based on the YOLOv10-S
of #1 and #2 in Tab. 5, like [6]. 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 AP${}^{val}_{w/o\ c}$ with
no classification errors. Similarly, we replace the predicted locations with
those of instances, yielding AP${}^{val}_{w/o\ r}$ with no regression errors.
As shown in Tab. 9, AP${}^{val}_{w/o\ r}$ is much higher than
AP${}^{val}_{w/o\ c}$, 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.

  * •

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 it with the
baseline way of spatial reduction by DW followed by channel modulation by PW,
based on the YOLOv10-S of #3 in Tab. 5. As shown in Tab. 9, 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. 5.
Specifically, we introduce the inverted residual block [46] (IRB) as the
baseline, which achieves the suboptimal 43.7% AP, as shown in Tab. 9. We then
append a 3$\times$3 depthwise convolution (DW) after it, denoted as “IRB-DW”,
which 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. 5. The stages
sorted in ascending order based on the intrinsic ranks are Stage
8-4-7-3-5-1-6-2, like in Fig. 3.(a). As shown in Tab. 9, 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-guided block design can serve as 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 YOLOv10-S/M model after incorporating efficiency driven
design, _i.e_., #3/#7 in Tab. 2. As shown in Tab. 13, the adoption of large-
kernel convolution and PSA module leads to the considerable performance
improvements of 0.4% AP and 1.4% AP for YOLOv10-S under minimal latency
increase of 0.03ms and 0.15ms, respectively. Note that large-kernel
convolution is not employed for YOLOv10-M (see Tab. 13).

  * •

Large-kernel convolution. We first investigate the effect of different kernel
sizes based on the YOLOv10-S of #2 in Tab. 13. As shown in Tab. 13, the
performance improves as the kernel size increases and stagnates around the
kernel size of 7$\times$7, indicating the benefit of large perception field.
Besides, removing the reparameterization branch during training achieves 0.1%
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. 13, it brings no improvements for large
models, _i.e_., YOLOv10-M, due to its inherent extensive receptive field. We
thus only adopt large-kernel convolutions for small models, _i.e_., YOLOv10-N
/ S.

  * •

Partial 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 YOLOv10-S of #3 in Tab. 13. Specifically, we
introduce the transformer block, _i.e_., MHSA 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 [62,
9] in self-attention, by mitigating the redundancy in attention heads.
Moreover, we investigate the impact of different $N_{\text{PSA}}$. As shown in
Tab. 13, increasing $N_{\text{PSA}}$ to 2 obtains 0.2% AP improvement but with
0.1ms latency overhead. Therefore, we set $N_{\text{PSA}}$ 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 efficiency-accuracy driven model design strategy, improving the
performance-efficiency trade-offs. 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.

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##  Appendix A Appendix

###  A.1 Implementation Details

Following [20, 56, 59], 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$\times$10-4, respectively. The initial learning rate is
1$\times$10-2 and it decays linearly to 1$\times$10-4. For data augmentation,
we adopt the Mosaic [2, 19], Mixup [68] and copy-paste augmentation [17],
_etc_., like [20, 59]. Tab. 14 presents the detailed hyper-parameters. All
models are trained on 8 NVIDIA 3090 GPUs. Besides, we increase the width scale
factor of YOLOv10-M to 1.0 to obtain YOLOv10-B. For PSA, we employ it after
the SPPF module [20] 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 [59, 56], we report the standard mean average precision
(AP) across different object scales and IoU thresholds on the COCO dataset
[33].

Moreover, we follow [71] to establish the end-to-end speed benchmark. Since
the execution time of NMS is affected by the input, we thus measure the
latency on the COCO val set, like [71]. We adopt the same NMS hyperparameters
used by the detectors during their validation. The TensorRT efficientNMSPlugin
is appended for post-processing and the I/O overhead is omitted. We report the
average latency across all images.

Table 14: Hyper-parameters of YOLOv10. hyper-parameter | YOLOv10-N/S/M/B/L/X
---|---
epochs | 500
optimizer | SGD
momentum | 0.937
weight decay |  5$\times$10-4
warm-up epochs | 3
warm-up momentum | 0.8
warm-up bias learning rate | 0.1
initial learning rate |  10-2
final learning rate |  10-4
learning rate schedule | linear decay
box loss gain | 7.5
class loss gain | 0.5
DFL loss gain | 1.5
HSV saturation augmentation | 0.7
HSV value augmentation | 0.4
HSV hue augmentation | 0.015
translation augmentation | 0.1
scale augmentation | 0.5/0.5/0.9/0.9/0.9/0.9
mosaic augmentation | 1.0
Mixup augmentation | 0.0/0.0/0.1/0.1/0.15/0.15
copy-paste augmentation | 0.0/0.0/0.1/0.1/0.3/0.3
close mosaic epochs | 10

###  A.2 Details of Consistent Matching Metric

We provide the detailed derivation of consistent matching metric here.

As mentioned in the paper, we suppose that the one-to-many positive samples is
$\Omega$ and the one-to-one branch selects $i$-th prediction. We can then
leverage the normalized metric [14] to obtain the classification target for
task alignment learning [20, 14, 59, 27, 64], _i.e_.,
$t_{o2m,j}=u^{*}\cdot\frac{m_{o2m,j}}{m_{o2m}^{*}}\leq u^{*}$ for $j\in\Omega$
and $t_{o2o,i}=u^{*}\cdot\frac{m_{o2o,i}}{m_{o2o}^{*}}=u^{*}$. We can thus
derive the supervision gap between two branches by the 1-Wasserstein distance
[41] of the different classification targets, _i.e_.,

$\displaystyle A$
$\displaystyle=|(1-t_{o2o,i})-(1-\mathbb{I}(i\in\Omega)t_{o2m,i})|+\sum\nolimits_{k\in\Omega\backslash\\{i\\}}|1-(1-t_{o2m,k})|$
(3)
$\displaystyle=|t_{o2o,i}-\mathbb{I}(i\in\Omega)t_{o2m,i}|+\sum\nolimits_{k\in\Omega\backslash\\{i\\}}t_{o2m,k}$
$\displaystyle=t_{o2o,i}-\mathbb{I}(i\in\Omega)t_{o2m,i}+\sum\nolimits_{k\in\Omega\backslash\\{i\\}}t_{o2m,k},$

where $\mathbb{I}(\cdot)$ is the indicator function. We denote the
classification targets of the predictions in $\Omega$ as
$\\{\hat{t}_{1},\hat{t}_{2},...,\hat{t}_{|\Omega|}\\}$ in descending order,
with $\hat{t}_{1}\geq\hat{t}_{2}\geq...\geq\hat{t}_{|\Omega|}$. We can then
replace $t_{o2o,i}$ with $u^{*}$ and obtain:

$\displaystyle A$
$\displaystyle=u^{*}-\mathbb{I}(i\in\Omega)t_{o2m,i}+\sum\nolimits_{k\in\Omega\backslash\\{i\\}}t_{o2m,k}$
(4)
$\displaystyle=u^{*}+\sum\nolimits_{k\in\Omega}t_{o2m,k}-2\cdot\mathbb{I}(i\in\Omega)t_{o2m,i}$
$\displaystyle=u^{*}+\sum\nolimits_{k=1}^{|\Omega|}\hat{t}_{k}-2\cdot\mathbb{I}(i\in\Omega)t_{o2m,i}$

We further discuss the supervision gap in two scenarios, _i.e_.,

  1. 1.

Supposing $i\not\in\Omega$, we can obtain:

$A=u^{*}+\sum\nolimits_{k=1}^{|\Omega|}\hat{t}_{k}$ (5)

  2. 2.

Supposing $i\in\Omega$, we denote $t_{o2m,i}=\hat{t}_{n}$ and obtain:

$A=u^{*}+\sum\nolimits_{k=1}^{|\Omega|}\hat{t}_{k}-2\cdot\hat{t}_{n}$ (6)

Due to $\hat{t}_{n}\geq 0$, the second case can lead to smaller supervision
gap. Besides, we can observe that $A$ decreases as $\hat{t}_{n}$ increases,
indicating that $n$ decreases and the ranking of $i$ within $\Omega$ improves.
Due to $\hat{t}_{n}\leq\hat{t}_{1}$, $A$ thus achieves the minimum when
$\hat{t}_{n}=\hat{t}_{1}$, _i.e_., $i$ is the best positive sample in $\Omega$
with $m_{o2m,i}=m_{o2m}^{*}$ and
$t_{o2m,i}=u^{*}\cdot\frac{m_{o2m,i}}{m_{o2m}^{*}}=u^{*}$.

Furthermore, we prove that we can achieve the minimized supervision gap by the
consistent matching metric. We suppose $\alpha_{o2m}>0$ and $\beta_{o2m}>0$,
which are common in [20, 59, 27, 14, 64]. Similarly, we assume
$\alpha_{o2o}>0$ and $\beta_{o2o}>0$. We can obtain
$r_{1}=\frac{\alpha_{o2o}}{\alpha_{o2m}}>0$ and
$r_{2}=\frac{\beta_{o2o}}{\beta_{o2m}}>0$, and then derive $m_{o2o}$ by

$\displaystyle m_{o2o}$ $\displaystyle=s\cdot
p^{\alpha_{o2o}}\cdot\text{IoU}(\hat{b},b)^{\beta_{o2o}}$ (7)
$\displaystyle=s\cdot
p^{r_{1}\cdot\alpha_{o2m}}\cdot\text{IoU}(\hat{b},b)^{r_{2}\cdot\beta_{o2m}}$
$\displaystyle=s\cdot(p^{\alpha_{o2m}}\cdot\text{IoU}(\hat{b},b)^{\beta_{o2m}})^{r_{1}}\cdot\text{IoU}(\hat{b},b)^{(r_{2}-r_{1})\cdot\beta_{o2m}}$
$\displaystyle=m_{o2m}^{r_{1}}\cdot\text{IoU}(\hat{b},b)^{(r_{2}-r_{1})\cdot\beta_{o2m}}$

To achieve $m_{o2m,i}=m_{o2m}^{*}$ and $m_{o2o,i}=m_{o2o}^{*}$, we can make
$m_{o2o}$ monotonically increase with $m_{o2m}$ by assigning
$(r_{2}-r_{1})=0$, _i.e_.,

$\displaystyle m_{o2o}$
$\displaystyle=m_{o2m}^{r_{1}}\cdot\text{IoU}(\hat{b},b)^{0\cdot\beta_{o2m}}$
(8) $\displaystyle=m_{o2m}^{r_{1}}$

Supposing $r_{1}=r_{2}=r$, we can thus derive the consistent matching metric,
_i.e_., $\alpha_{o2o}=r\cdot\alpha_{o2m}$ and $\beta_{o2o}=r\cdot\beta_{o2m}$.
By simply taking $r=1$, we obtain $\alpha_{o2o}=\alpha_{o2m}$ and
$\beta_{o2o}=\beta_{o2m}$.

###  A.3 Details of Rank-Guided Block Design

We present the details of the algorithm of rank-guided block design in
Algorithm 1. Besides, to calculate the numerical rank of the convolution, we
reshape its weight to the shape of ($C_{o}$, $K^{2}\times C_{i}$), where
$C_{o}$ and $C_{i}$ denote the number of output and input channels, and $K$
means the kernel size, respectively.

Input: Intrinsic ranks $R$ for all stages $S$; Original Network $\Theta$; CIB
$\theta_{cib}$;

Output: New network $\Theta^{*}$ with CIB for certain stages.

1 $t\leftarrow 0$;

2 $\Theta_{0}\leftarrow\Theta$; $\Theta^{*}\leftarrow\Theta_{0}$;

$ap_{0}\leftarrow\text{AP}(\text{T}(\Theta_{0}))$ ;

// T:training the network; AP:evaluating the AP performance.

3 while  _$S\neq\emptyset$_ do

4 $\boldsymbol{s}_{t}\leftarrow\operatorname*{argmin}_{s\in S}R$;

$\Theta_{t+1}\leftarrow\text{Replace}(\Theta_{t},\theta_{cib},\boldsymbol{s}_{t})$
;

// Replace the block in Stage $\boldsymbol{s}_{t}$ of $\Theta_{t}$ with CIB
$\theta_{cib}$.

5 $ap_{t+1}\leftarrow\text{AP}(\text{T}(\Theta_{t+1}))$;

6 if  _$ap_{t+1}\geq ap_{0}$ _ then

7 $\Theta^{*}\leftarrow\Theta_{t+1}$; $S\leftarrow
S\setminus\\{\boldsymbol{s}_{t}\\}$;

8 else

9 return _$\Theta^{*}$_ ;

10

11 end if

12

13 end while

14return _$\Theta^{*}$_ ;

Algorithm 1 Rank-guided block design

###  A.4 More Results on COCO

We report the detailed performance of YOLOv10 on COCO, including
AP${}^{val}_{50}$ and AP${}^{val}_{75}$ at different IoU thresholds, as well
as AP${}^{val}_{small}$, AP${}^{val}_{medium}$, and AP${}^{val}_{large}$
across different scales, in Tab. 15.

###  A.5 More Analyses for Holistic Efficiency-Accuracy Driven Model Design

We note that reducing the latency of YOLOv10-S (#2 in Tab. 2) is particularly
challenging due to its small model scale. However, as shown in Tab. 2, our
efficiency driven model design still achieves a 5.3% reduction in latency
without compromising performance. This provides substantial support for the
further accuracy driven model design. YOLOv10-S achieves a better latency-
accuracy trade-off with our holistic efficiency-accuracy driven model design,
showing a 2.0% AP improvement with only 0.05ms latency overhead. Besides, for
YOLOv10-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, as shown in Tab. 2. When combined with accuracy driven model
design, we observe a 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 scales.

Table 15: Detailed performance of YOLOv10 on COCO. Model |  APval(%) |  AP${}_{50}^{val}$(%) |  AP${}_{75}^{val}$(%) |  AP${}_{small}^{val}$(%) |  AP${}_{medium}^{val}$(%) |  AP${}_{large}^{val}$(%)
---|---|---|---|---|---|---
YOLOv10-N | 38.5 | 53.8 | 41.7 | 18.9 | 42.4 | 54.6
YOLOv10-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 | 57.8 | 68.5
YOLOv10-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
Figure 4: Visualization results under complex and challenging scenarios.

###  A.6 Visualization Results

Fig. 4 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 a strong capability in detecting diverse and
densely packed objects, such as bottle, cup, and person. These results
indicate its superior performance.

###  A.7 Contribution, Limitation, and Broader Impact

Contribution. In summary, our contributions are three folds as follows:

  1. 1.

We present a novel consistent dual assignments strategy for NMS-free YOLOs. A
dual label assignments way is 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 to improved performance.

  2. 2.

We propose a holistic efficiency-accuracy driven model design strategy for the
model architecture of YOLOs. We present novel lightweight classification head,
spatial-channel decoupled downsampling, 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. 3.

Based on the above approaches, we introduce YOLOv10, a new real-time end-to-
end object detector. Extensive experiments demonstrate that our YOLOv10
achieves the state-of-the-art performance and efficiency trade-offs compared
with other advanced detectors.

Limitation. Due to the limited computational resources, we do not investigate
the pretraining of YOLOv10 on large-scale datasets, _e.g_., Objects365 [47].
Besides, although we can achieve competitive end-to-end performance using the
one-to-one head under NMS-free training, there still exists a 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-many training with NMS outperforms that of NMS-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 YOLOv10 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.