YOLOv12: Attention-Centric Real-Time Object Detectors
Authors: Yunjie Tian
Qixiang Ye
David Doermann
Venue: NeurIPS 2025 poster ()
Keywords: YOLO
Real-Time Detection
Attention-Centric
Primary area: deep_learning

Abstract: Enhancing the network architecture of the YOLO framework has been crucial for a long time. Still, it has focused on CNN-based improvements despite the proven superiority of attention mechanisms in modeling capabilities. This is because attention-based models cannot match the speed of CNN-based models. This paper proposes an attention-centric YOLO framework, namely YOLOv12, that matches the speed of previous CNN-based ones while harnessing the performance benefits of attention mechanisms. YOLOv12 surpasses popular real-time object detectors in accuracy with competitive speed. For example, YOLOv12-N achieves 40.5% mAP with an inference latency of 1.62 ms on a T4 GPU, outperforming advanced YOLOv10-N / YOLO11-N by 2.0%/1.1% mAP with a comparable speed. This advantage extends to other model scales. YOLOv12 also surpasses end-to-end real-time detectors that improve DETR, such as RT-DETRv2 / RT-DETRv3: YOLOv12-X beats RT-DETRv2-R101 / RT-DETRv3-R101 while running faster with fewer computations and parameters. See more comparisons in Figure 1. Source code is available at https://github.com/sunsmarterjie/yolov12.

YOLOv12: Attention-Centric Real-Time Object Detectors
Yunjic Tian University at Buffalo umjieti@buffalo.cm edu
Qixiang Ye" UCAS qxye@ucas.co ac
David Doermann University at Buffalo doermann@buffalo edu
MS COCO MAP (5))
VDLON5-20
VOLOW-34
YOLO<>
VOLOV?
VOLOs1
YOLOVE
KE-DETR
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RT-DEIR52
BT. BT-DETRe3
42
VOLOMM
40
40
38
VOL Lo0n0
38
7.5
10.0
17.5 20.0
140
160
180
Latency (ms)
FLOPs (G)
Figure 1: Comparisons with other popular methods in terms of latency-accuracy (left) and FLOPs-accuracy (right) trade-offs.
Abstract
Enhancing the network architecture of the YOLO framework has been crucial for a long time. Still, it has focused CNN-based improvements despite the proven superiority of attention mechanisms modeling capabilities. This is because attention--aase models cannot match the speed of CNN-based models. This paper proposes an attention- the speed of previous CNN-hased ones while harnessing the performance benefits attention mechanisms. YOLOv12 surpasses popular real-time object detectors in accuracy with competitive speed. For example, YOLOv12-N achieves 40.5% mAP with an inference latency of 1.62 ms on T4 GPU, outperforming advanced YOLOv10-N, YOLO1 I-N by 2.0% / 1.. % mAP with comparable speed. This advantage extends to other model scales. YOLOv 12 also surpasses end-to-end real- time detectors that improve DETR, such RT-DETRv2 RT-DETRy3: YOLOv12- X beats DEw.D-TTRRRRE 101 RT-DETRv3-R100 while running faster with fewer computations and parameters. See more comparisons in Figure Source code available at htthh:tt///ttt httpaau....ooiiisaaaaerrree//l/o
YOLO framework, namely YOLOv12, that matches
Real-time object detection has consistently attracted significant attention due its low-latency characteristics, which provide substantial practicality [26] 31 6 19] Among them, the YOLO
has effectively established an optimal balance between latency and accuracy, thus dominating the field, Although improvements in YOLO have focused
series [1 53 52 32 35
67
on areas such as loss functions [10] 80 47 46 79 38 [54, label assignment [2] 37
68 24 81].
network architecture design has remained a critical research priority [35 64
26
67
Although
"Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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dncention-eettiie models have been proven to possess more substantial modeling capabilities, even in small models [27 21 57], most architectural designs continue focus primarily on CNNs.
The primary reason for this situation lies in the inefficiency of the attention mechanism, which comes from two main factors: quadratic computational complexity and inefficient memory access operations of the attention mechanism (the latter being the main issue addressed by FlashAttention [16 (5). As result, under a similar computational budget, CNN-based architectures outperform attention-based ones by a factor of ~ 3x 4T. which significantly limits the adoption of attention in YOLO systems.
This paper aims to tackle these challenges and establish attention-centric YOLO framework, YOLOv12. We introduce three key improvements. First, we propose a simple yet efficient Area Attention module (A2), which preserves large receptive field while efficiently reducing the compu- tational complexity of attention, thus improving speed. Moreover, A2 supports flexible input sizes to accommodate window partitioning. allowing rectangular inference in YOLO. Second, we design Residual Efficient Layer Aggregation Networks (R-ELAN) to address optimization challenges in the designed attention-based models. Building on ELAN (64), R-ELAN introduces (i) a block-level residual design with scaling techniques and (ii) an improved feature aggregation strategy. Third, we refine attention-centric architectures to better integrate with the YOLO framework. Key modifications include: incorporating FlashAttention to mitigate memory access issue; using a decoupled projection strategy to construct q. k, and v during attention computation, thus avoiding redundant feature reorganization; removing positional encoding for leaner design; reducing the MLP ratio from 4 to 1.5 to better balance attention and FFN computation; and decreasing the depth of stacked blocks to facilitate optimization.
without constraints in window attention 42 18]
Based on the designs outlined above, develop new family of real-time detectors with 5 model scales: YOLOv12-N, S, M. L, and X. We perform extensive experiments on standard object detection benchmarks following YOLOI1 [I without any additional tricks, demonstrating that YOLOv 12 provides significant improvements over previous popular models in terms of latency-accuracy and FLOPS-accuracy trade-off's across these scales, as illustrated in Figure For example, YOLOv12-N achieves 40.5% mAP, outperforming YOLOv10-P [6 by 2.0% mAP while maintaining faster inference speed, and YOLO11-N (11 by 1.1% mAP with a comparable speed. This advantage remains consistent across other scale models. Compared to RT-DETRV2-RR
(78), YOLOv12-S is comparable and 47% faster latency speed, requiring only 33% of its computations and 46% of its parameters. Compared to RT-DETRv3-R50 43 YOLOv12-L. is 0.4% mAP better and 15% faster, requiring only 61% % of its computations and 63% of its parameters.
In summary, YOLOv12 contributes by: (i) introducing an attention-centric, efficient YOLO framework that challenges CNN dominance in the series, and (ii) achieving sfate-of-the-art results with fast inference and high accuracy without relying on pre-training or additional training techniques.
Related Work
Real-time Object Detectors. The YOLO series 51 52 32 55
67 61
has
become the leading framework for real-time object detection. The early YOLO models [1 53 52
established the foundation primarily from a model design perspective. YOLOv4 33 and YOLOv5
introduced CSPNet [65], data augmentation, and multi-scale features, while YOLOvó 35 further
enhanced efficiency with BiC, SimCSPSPPE, etc. YOLOv7 664 incorporated E-ELAN 66 for
better gradient flow and various ffgebfefr while YOLOv8 (26) adopted the efficient C2f block for feature extraction. Recent versions. YOLOv9 677, introduced GELAN for architectural
optimization and PGI for training improvements, YOLOv10 61 applied NMS-free training with dual
assignments, and YOLOv11 (31] optimized speed and accuracy with C3K2 (a variant of GELAN) further enhanced
and lightweight depthwise separable convolutions. Other developments 773 62 75
detection capabilities through improved backbones and head designs Beyond YOLO, RT-DETR (78) improved end-to-end detectors
36 for real-time use through an efficient encoder and encertainty-minime query selection, with RT-DETRy2 43 and RT-DETRv3 69 further refinement
[9] 83
45
with bag- ffeeeebis... Recent follow-ups [50 29 continue to show compromising results.
Efficient Vision Transformers. Reducing the computational cost global self-attention is the key to effectively applying vision transformers to downstream tasks. PVT [7] tackles this with multi- resolution stages and downsampling. Swin Transformer 42 restricts self-attention to local windows and shifts them to connect non-overlapping regions. Other approaches, like axial self- attention (28

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(Ours)
input
Criss-cross attention
Swin attention
CSwin attention
Area attention
Rect: V
Reci: X
Rect: X
Rect: v/
to:
Figure 2: Comparison of the representative local attention mechanisms with our area attention. Area attention computes local attention over sequentially placed tokens without requiring input size constraints, enabling default distortion-free rectangular inference (Recf) in YOLO. Area attention enjoys efficient implementation, requiring only flatten and reshape operations.
compute attention within horizontal and vertical windows. CSWin Transformer 18] extends this with self-attention in cross-shaped windows. Furthermore, methods
and criss-cross attention
30),
such as 14 76 establish local-global relations, enhancing efficiency while reducing the reliance on global self-attention. Fast-iTPN [57] accelerates downstream task inference with token migration and gathering. Some methods [56 70 33 use linear attention to reduce complexity, but vision mamba models [82 4| still struggle to achieve real-time speeds [41]. FlashAttention [16] [5] addresses
high-bandwidth memory bottlenecks by optimizing I/O and reducing memory access for efficiency.
2
Approach
2.1
Efficiency Analysis
The attention mechanism, while highly effective in capturing global dependencies and facilitating tasks such as natural language processing [7] and computer vision [23] 42], is inherently slower than CNNs. Two primary factors contribute to this speed discrepancy.
Complexity. First, the computational complexity of the attention operation scales quadratically with the input sequence length L. Specifically, for an input sequence with length L and feature dimension d, the computation of the attention matrix requires O(L'd) operations since each token attends to every other token. contrast, the complexity of convolution operations in CNNs scales linearly with respect to the spatial or temporal dimension, i.e., O(kLd), where & is the kernel size and is typically much smaller than As result, eeffattention becomes computationally prohibitive, especially for significant inputs such as high-resolution images or long sequences.
Moreover, attention-based vision transformers often suffer from speed overhead due to their complex designs (e.g., window partitioning reverse in some window attentions [42 [8] and additional
modules (e.g., positional encoding). leading to slower performance compared to CNNs (41). In contrast, the design modules in this paper use simple and efficient operations to implement attention, maximizing speed and efficiency.
Computation. Second, the memory access patterns in attention computations are less efficient than in CNNs [6 [5]. In self-attention, intermediate maps such the attention map (QK?) ) and the softmax map (L X L) must be transferred from high-speed GPU SRAM to high- gigh-bandwidth GPU memory (HBM) for further computation. The read/write speed of SRAM is more than 10 times faster, leading to significant memory access overhead and increased wall clock time.
2.2 Area Attention
A straightforward way to reduce the computational cost of vanilla attention is to use the linear attention mechanism [56 70), which reduces the complexity from quadratic to linear. For a visual feature o with dimensions (n, h,d), where is the number of tokens, is the number of heads, and d is the head size, linear attention reduces the complexity from hdd to 2nhuf. cutting the computational cost as n d. However, linear attention faces issues such global dependency degradation (34).
3

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transition
transition split
transition
transition
split....
split
conV
cont
cont
con
transition
x
conty
block
conv
CoIV
Mock
A2
block
black
A2
francition
block,
transition
concatenation
coneatenation
concatenation
concatenation
(a) CSPNet
(b) ELAN
(c) C,K,
(d) R-ELLAN
Figure 3: The architecture comparison with popular modules including (a): CSPNet 665 (b) ELAN [66). (c) C3K2 (a case of GELAN) 67 31, and (d) the proposed R-ELAN (residual efficient
layer aggregation networks).
instability [2].
and distribution sensitivity [2]. Furthermore, the low-rank bottleneck provides limited speed improvements when applied to YOLO.
13
alternative approach effectively reduce complexity is the local attention mechanism (e.g.. criss-cross attention [30). axial attention (28). and window attention [42 [8), as shown in Figure 2 which transforms global attention into local, thus reducing computational costs. However, partitioning the feature map into windows can introduce overhead or reduce the receptive field, impacting both speed and accuracy. Furthermore, for window attentions, such as Swin 42 and CSwin [18], they also suffer from additional constraints on input size to accommodate window partitioning. As shown Figure 2 they cannot handle specific rectangular dimensions (native resolutions) commonly seen in the inference of YOLO (e.g., 672 x 608) and must resize the input to window-friees frienddly sizes (e.g., 640x x 640), which somehow introduces image distortion and thereby degrades detection performance.
In this study, we propose simple yet efficient area attention module (A2). The comparison of A2 with other local attentions is illustrated in Figure 2 first perform flatten operation to obtain a one-dimension then evenly divided into / segments (areas) (so each area contains ! sequentially placed tokens). Local attention is applied independently within each area (a simple reshape operation enables the transformation to the original size). This only requires simple flatten and a reshape operation. leading to efficient implementation and faster speed. We empirically set the default value of / to 4 (or 8), thus reducing the receptive field to of the original size while maintaining large receptive field. More importantly, A2 eliminates feature size constraints while only requiring the total token count (H x W) to be divisible by This design inherently supports rectangular inference (see Figure ( the standard evaluation protocol in YOLO, thus achieving seamless compatibility with the YOLO framework. (See more details about A2 and architecture design in the Appendix.)
Specifically, for feature of size (H, W), we
feature with H x W tokens, which is
Regarding the memory access issue, we integrate FlashAttention [6 15) into area attention to solve it. FlashAttention already an infrastructure for many large language models 66
40 and diffusion
models 55 49 4. and can provide similar benefits to YOLO.
2.3 Residual Efficient Layer Aggregation Networks
Efficient layer aggregation networks (ELAN) 64 are designed to improve feature aggregation. As shown in Figure (b), ELAN splits the output of transition layer (a convolution 1 x 1, processes each split through multiple modules, and then concatenates the outputs before applying another transition layer to align the dimensions. However, noted in [64], this design can lead to instability. We argue that this architecture causes gradient blocking and lacks residual connections between input and output. In addition, incorporating the attention mechanism into the network introduces additional optimization challenges. Empirically, L- and X-scale models either fail to converge or remain unstable,
when using Adam or AdamW optimizers.
To address this issue, we propose residual efficient layer aggregation networks (R-ELAN), as shown in Figure (d). We introduce residual shortcut from input to output throughout the block, with scaling factor (defaulting to 0.01). This design inspired by the layer scaling [59] used in deep vision transformers. However, applying layer scaling for each attention area does not solve the optimization

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Table I: Comparison with popular real-time object detectors. All results obtained using 640 x 640 inputs. f: pre-trained models are required.
FLOPs
#Param. (M)
A APa
APa (%)
AP (%)
Latency
Method
50:95 %
(G)
(ms)
YOLOv8-N 26
8.7
3.2
37.4
52.6
40.5
1.77
YOLOvI0-N 66
6.7
2.3
38.5
53.8
41.7
1.84
YOLO1I-N 31
6.5
2.6
39.4
55.3
42.8
1.5
YOLOv12-N (Ours)
6.0
2.6
40.5
56.4
43.8
1.62
YOLOv8-S 26
28.6
11.2
45.0
61.8
48.7
2.33
RT-DETRv2-R18
44
60.0
20.0
47.9
64.9
4.58
YOLOv9-S 66
26.4
7.1
46.8
63.4
50.7
YOLOv10-S 61
21.6
7.2
46.3
63.0
50.4
2.49
YOLO11-S 31
21.5
9.4
46.9
63.9
50.6
2.5
YOLOv12-S (Ours)
19.5
9.1
47.8
64.9
51.3
2.44
YOLOv8-M 26
78.9
25.9
50.3
67.2
54.7
5.09
RT-DETRv2-RS5 R34
44
100.0
36.0
49.9
67.5
6.32
RT-DETRv3-RRE
48
60.0
20.0
48.7
4.58
RT-DETRv3-R341
669
100.0
36.0
50.1
67.5
6.32
YOLÓv9-M [67
76.3
20.0
51.4
68.1
56.1
YOLOv10-M 61
59.1
15.4
51.1
68.1
55.8
4.74
YOLO11-M 3]
68.0
20.1
51.5
68.5
55.7
4.7
YOLOv12-M (Ours)
59.9
19.7
52.5
70.0
57.0
4.30
YOLOv8-L 26
165.2
43.7
53.0
69.8
57.7
8.06
RT-DETRv2-R50
44
136.0
42.0
53.4
71.6
6.90
RT-DETRRv3-R50
669
136.0
42.0
53.4
6.90
YOLOv9-C (67]
102.1
25.3
53.0
70.2
57.8
VOLOvI0-B (61
92.0
19.1
52.5
69.6
57.2
5.74
YOLOvI0-L 61
120.3
24.4
53.2
70.1
58.1
7.28
YOLO11-L 31
86.9
25.3
53.3
70.1
58.2
6.2
D-FINE-L' [50]
91
54.0
71.6
58.4
8.07
YOLOv12-L (Ours)
82.6
26.6
53.8
71.1
58.7
5.89
YOLOv8-X 26
257.8
68.2
54.0
71.0
58.8
12.83
RT-DETRv2-RIO1
259.0
76.0
54.3
72.8
13.5
RT-DETRv3-RI01
69
259.0
76.0
54.6
13.5
YOLOv10-X 61
160.4
29.5
54.4
71.3
59.3
10.70
YOL011-X [3I
194.9
56.9
54.6
71.6
59.5
11.3
D-FINE-Xt (50]
202
62
55.8
73.7
60.2
12.89
YOLOv12-X (Ours)
184.9
59.5
55.4
72.6
60.4
10.47
challenge and introduces latency. This highlights that the convergence issue is not solcly due to the attention mechanism but also the ELAN structure, validating the rationale behind R- R-ELAN design.
We also design a new feature aggregation approach, shown in Figure 3 (d). In the original ELAN, the input is passed through transition layer, splitting it into two parts. Subsequent blocks process one part, and both parts are concatenated to produce the output. In contrast, our design uses transition layer adjust channel dimensions and produce single feature map. This map is processed through subsequent blocks, followed by concatenation, forming a bottleneck structure. This method retains the original feature integration capability while reducing computational cost, parameters, and memory usage.
Architectural Improvements
Many attention-base vision transformers use plain-style architectures
5% 2
22
while we
retain the hicrarchical structure of previous YOLO versions [5]]
53
52
35
64
26
67
6
31

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and we will demonstrate its necessity. We simplify the architecture depth by removing the stacking of three blocks the final stage of the backbone that are used most frequently in recent YOLO versions 226 67 61 31, retaining only a single block. In addition, we retain the first two blocks, remove the third block, and replace all C3K2 blocks with R-ELAN blocks in the backbone. We used convolutions with the 2 and 4 groups in the second and third blocks, respectively.
Several default configurations of the vanilla attention mechanism have also been modified for better alignment with the YOLO system. These include adjusting the MLP ratio from 4 to 1.5 (or 2 for N / / M scale models), removing positional encoding, and adding a separable convolution (7 x 7) (position perceiver) to enhance the ability of area attention to perceive positional information. The effectiveness of these changes will be demonstrated in Section 3.5
Previous
versions
(egg.
Table 2: Comparison of YOLOv12 with previous versions in speed (GPU, CPU) and memory usage. CUDA results measured on T4/ RTX 3080 GPUs, with inference latency (ms) reported for FP32 and FP16. Memory usage (Mem.) is measured with the TensorRT model (bs 1). All results obtained using the same hardware.
YOLOv10 66 and YOLOv11 (31) adopt a coupled projection strategy for constructing 9. k, and v during attention calculation, where a single convolutional layer jointly projects the input and then splits the output into the three components. However, when used alongside the position perceiver, this coupling results in redundant reorganization of the v features, degrading inference effi- ciency. To mitigate this, we introduce a decoupled projection strategy that computes v separately from q and k. This design eliminates unnecessary processing on v. leading to an inference speedup of approximately 10%.
FLOPs Mem. (G)
CUDA
Model
CPU
mAP
(G)
FP32
FP16
YOLOv9-T 67
8.2
0.16
4.0/2.3
2.4/1.5
40.1
YOLOv10-N 66
6.7
0.19
3.0/1.6
1.7/1.0
32.4
38.5
YOLO11-N 31
6.5
0.20
3.0/1.6
1.5/0.9
32.5
39,5
YOLOv12-N
6.0
0.15
3.0/1.6
1.6/1.0
38.7
40.5
YOLOv9-S 67
26.4
0.19
7.3/3.7
3.3/1.9
85.6
46.8
YOLOv10-S 61]
21.6
0.23
6.3/2.6
2.6/1.3
70.1
46.3
YOLO11-S 31
21.5
0.23
6.4/2.7
2.4/1.3
72.1
46.9
YOLOv12-S
19.5
0.18
6.5/2.7
2.5/1.3
83.3
47.8
YOLOvv-M 67
76.3
0.24
16.7/6.3
5.6/2.7
186.5
51.4
YOLOv10-M 61
59.1
0.29
13.5/5.3
4.8/2.4
161.5
51.1
YOLO11-M 31
68.0
0.30
16.2/5.4
4.4/2.2
189.9
51.5
3 Experiment
YOLOv12-M
59.9
0.24
14.9/5.2
4.3/2.1
213.2
52.5
YOLOv9-C 67
102.1
0.29
20.9/7.4
6.2/2.8
272.6
53.0
3.1 Experimental Setup
YOLOv10-L 61
120.3
0.30
23.8/8.0
7.3/3.4
294.8
53.2
YOLO11-L 31
86.9
0.32
20.4/6.9
5.9/2.9
237.7
53.3
We validate YOLOv12 on the MS-
YOLOv12-L
82.6
0.27
20.6/6.9
6.0/2.9
299.5
53.8
COCO 2017 dataset (39] The
YOLOx9-E 67
189.0
0.69
48.6/15.5
15.5/6.5 499.7
55.6
YOLOv12 family consists of 5 variants: YOLOv12-N, YOLOv12- S. YOLOv12-M, YOLOv12-L, and YOLOv12-X, All models are trained
YOLOv10-X 60
160.4
0.40
35.0/10.7
10.4/4.5
410.1
54.4
YOLOII-X [1]
194,9
0.45
40.4/13.5
10.5/4,9 484.0
54.6
YOLOv12-X
184.9
0.37
40.6/13.6 10.5/4.9 524.6
55.4
for 600 epochs using the SGD optimizer with an initial learning rate of 0.01, consistent with YOLO11 [3I] A linear learning rate decay schedule adopted, with linear warm-up for the first three epochs. Following the methodology in (78), the latencies of all models are tested on a T4 GPU using TensorRT FP16. See the Appendix for more details and results.
3.2 Comparison with State-of-the-arts
We compare YOLOv12 with other detectors Table [ us- ing performance data from their official reports (some achieve higher speed in our reproduc- tion, see Table 2). N-scale: YOLOv12-N exceeds YOLOv8- N 677. YOLOv10-N [61), and
MNN mACUU
MS COCO
DETS
YOLOH 31 by up to 3.1% mAp maintaining a similar or lower computational cost with 1.62 ms/image latency. S-scale: YOLOv12-S (19.5G FLOPs, 9.IM params) achieves 47.8 mAP
Parameter (M)
CPU OXXX (am))
Figure 4: Eccuracy-parameter//e latency trade-offs for Y0L0v12.

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at 2.44 ms/image, outperforming YOLOv9-S [67, YOLOv10-S [6]] and YOLOI1-S BI]. Com-
pared to RT-DETRv2-RI8 44. offers better speed with lower computational cost. M-scale:
YOLOv12-M (59.9G FI F. 19.7M params) achieves 52.5 mAP at 4.30 ms/image, outperforming
YOLOv9-M [67]. YOLOv10 61,, YOLO11 [1) and RT-DETRV2-R3. 44 RT-DETRY3-R34 [69]
L-scale: 10LOv12-L. exceeds YOLOv10-I. [61] with 37.7G fewer FLOPs and YOLO11-L [31
by 0.5% mAP while maintaining a similar efficiency. also outperforms RT-DETRv2-RS0 [44]
(RT-DETRY3-R5) [69] with fewer FLOPs, parameters, and faster speed. X-scale: YOLOv12-X
exceeds YOLOv 10-X [6]] and YOLO11-X (3 by up to 1.0% mAP with similar efficiency and surpasses ST-DETRv2-RR parameters, and 22.4% faster speed.
[43 RT-DETRRV R101 [69 with 28.6% fewer FLOPs, 21.7% fewer
Table 3: Ablation on the proposed area attention. Com-
3.3 Speed, Memory, and Efficiency
pared to other methods (cross-cross (CC), Swin, and CSwin attentions), area attention helps YOLOv 12-N/S/X models ob- tain better accuracy and run faster on GPU (CUDA) and CPU. CUDA results measured T4/RTX 3080. All results
Table 2 compares the inference speed
of YOLOv9 [67]. YOLOv10 [6]].
YOLO11 [3I. and YOLOv12 on T4 and RTX 3080 GPUs using FP32 and FP16 precision, as well as CPU (Intel Core 17-10700K @ 3.80GHz) speed and peak memory usage. All results obtained on the same hardware to ensure fairness, The results show that YOLOv12 significantly outper- forms YOLOv9 in inference speed, while matching the performance of YOLOv10 and YOLOvI1. YOLOv12 requires less peak memory than the other models.
use the same hardware and exclude FlashAttention [6 [5].
Atten. Type
Mem. (G)
CUDA
(ms)
Model
CPU (ms)
Paaf 50:95
FP32
FP16
Global
0.21
6.1/2.7
3.1/1.6
66.3
41.7
CC
0.17
4.7/2.3
2.4/1.4
72.9
39.5
Swin
0.16
4.5/2.2
2.2/1.3
39.6
40.2
CSwin
0.16
4.5/2.2
2.2/1.3
39.9
40.3
A2
0.15
4.2/2.0
2.0/1.2
38.7
40.5
Global
0.30
12.5/4.8
5.2/2.4
144.3
49.0
CC
0.20
9.5/3.9
3.8/2.1
90.6
46.6
S
Swin
0.18
9.2/3.8
3.3/1.9
83.4
47.4
CSwin
0.18
9.3/3.8
3.3/1.9
85.6
47.5
Figure T compares the speed and effi- ciency of YOLOv12. We present the accuracy-paramettr trade-off, demon- strating that it outperforms popular X
A2
0.18
8.7/3.5
3.1/1.8
83.3
47.8
Global
0.68
74.2/31.4
26.3/10.8
913.0
56.2
CC
0.40
56.2/23.6
16.6/8.0
572.3
54.7
Swin
0.38
53.4/21.2
15.3/7.6
538.2
55.2
methods, including YOLOv10, which has significantly fewer parameters.
CSwin
0.38
53.5/21.4
15.4/7.6
553.6
55.2
A2
0.37
52.5/21.0
14.7/7.1
524.6
55.4
This underscores YOLOv12's ability to achieve higher accuracy with fewer parameters. We also in- clude the accuracy-latena trade-off for YOLOv12 on CPU (Intel Core i7- @ 3.80GHz). As shown, YOLOv12 delivers superior performance and establishes superior boundaries, demonstrating greater efficiency across various metrics.
Table 4: Ablation on the proposed residual efficient layer aggregation networks (R-ELAN). RA: Proposed feature integration; RS: Residual block; SL: Scaling factor for resid- uals; CS: Convergence status.
3.4 Ablation Studies
We perform ablation experiments to assess the effectiveness of area atten- tion (A2) and E-ELAN in Table 3 and Table respectively.
Model scale RA RS
FLOPs
Param Mem. (G)
SL
CS
mAP
Lat.
(G)
(M)
X
6.5
2.8
0.20
40.6
1.70
Area Attention. In Table uations are performed YOLOv12- N/S/X models, measuring the GPU (CUDA) and CPU inference speed. CUDA results are obtained using iden- tical T4 and RTX 3080 hardware to L
3 eval-
6.0
2.6
0.15
40.4
1.62
0.01
6.0
2.6
0.15
40.5
1.62
X
X
X
V
X
X
X
0.01
87.7
27.2
0.31
53.9
6.15
ensure fairness, while CPU perfor- mance is measured on an Intel Core 17-10700K @ 3. 3.80GHz. Memory us- age (Mem.) measured with the Ten- sorRT model (bs 1). We compare the performance with other local atten- tions and adjust the number of chan-
0.1
82.6
26.6
0.27
53.7
5.89
0.01
82.6
26.6
0.27
53.8
5.89
X
XXX
X
X
0.01
197.5
60.8
0.42
55.4
10.47
0.1
184.9
59.5
0.37
55.2
10.47
0.01
184.9
59.5
0.37
55.4
10.47

---

Table 5: Diagnostic studies. To space, only show the factor(s) to be diagnosed in each subtable. The default parameters are (unless otherwise specified) training for 600 epochs from scratch,
using the YOLOv12-N model. All latency (Lat.) results are tested on T4 GPU
Model
Lat. DP
Lat.
Method
mAP
Lat.
Ep.
mAP (N)
mAP (S)
kernel
mAP
Lat.
N
1.70
1.62
N/A
38.2
1.60
300
39.2
47.0
3
40.1
1.57
S
2.70
2.44
ST
40.1
1.60
500
40.3
47.6
5
40.4
1.60
M
4.88
4.30
ST
39.8
1.68
600
40.5
47.8
40.5
1.62
6.56
5.89
41.1
48.1
40.6
1.73
Ours
40.5
1.62
X
11.39
10.47
(c) Training Epoch
(d) Position Perceiver
(b) Hierarchical Design
Decoupled Projection
Pos.
mAP
Lat.
Model
mAP
Lat.
Ratio (L)
mAP
Lat.
FA
Lat. (N)
Lat. (S)
RPE
40.2
1.77
YOLOv10
38.5
1.68
1.5
53.8
5.89
x
2.00
3.12
APE
40.3
1.67
YOLO11
39.4
1.49
2.0
53.3
5.79
1.62
2.45
4.0
53.1
5.73
N/A 40.5
1.62
Y0LOv12
40.5
1.62
(h) FlashAttention
(g) MLP Ratio
(e) Position Embedding
(i) FA for V10/11
nels to ensure that all models have similar parameters and FLOPs. The results demonstrate significant efficiency and speedup with area attention. For example, with FP32 on RTX 3080, YOLOv12-N achieves a 1.2ms inference (RTX 3080) with a 40.5 mAP, surpassing the criss-cross (CC) (3), Swin [42), and CSwin 18] attentions, with only 0.15G inference mem- ory usage. In particular, the Swin and CSwin attentions require a resize operation (to 640 x 640) during inference, which can cause a performance drop Performance gain consistently observed across different models and hardware configurations. We do not use FlashAttention [16 15) in this experiment because would significantly reduce the speed difference.
R-ELAN. In Table 4 evaluations are performed on HOLOv12-N/L/2 models, revealing two key findings: (i) For smaller models such as YOLOv 12-N,, residual connections provide performance gain with negligible extra cost. For larger models (YOLOv12-L/X)), residual connections are crucial for stable training. with minimal scaling factor (0.01) for convergence. (ii) The proposed feature integration method reduces the complexity of the model (FLOPs and parameters) and the memory cost while maintaining accuracy, with only a slight (even without) performance drop.
3.5 Diagnosis & Visualization
We diagnose YOLOv12 designs in Tables 5a to
5h using YOLOv12-N trained from scratch for 600
epochs, unless otherwise stated.
We compare the Decoupled Projection (DP) strategy to construct q, k, and y with previous methods (Lat. ). DP consistently improves speed by around 10% in all scale models, as it avoids costly reorganization of the v feature during the A2 implementation, leading to better efficiency.
Decoupled Projection: Table 5a
Unlike other detection systems, such Mask R-CNN [27 2 where plain vision transformers produce substantial results, YOLOv12 behaves differently. Using a plain vision transformer (N/A) causes a significant performance drop, achieving only 38.2% mAP. A moderate adjustment, such as omitting the first (Sj) or fourth stage (S,) while maintaining similar FLOPs, results in a slight performance degradation of 0.4% mAP and 0.7% mAP, respectively. Consistent with previous YOLO models, the hierarchical design remains the most effective, providing the best performance in YOLOv12.
Hierarchical Design: Table 5b
Training Epochs: Table We examine how varying the number of training epochs impacts performance (training from scratch). Although some existing YOLO detectors achieve optimal results after roughly 500 training epochs [26 67 61), YOLOv12 requires a more extended training
II we resize the image to 640 x 640 for A2 inference, the speed remains unaffected, but the performance drops by about 0.2 in general, highlighting the importance of rectangular inference.
8

---

8
P
STOP
STOP
STOP HOLLI
STOP WHETEE
WHEITI
input
YOLOv10
YOLO+11
YOLOx12
input
YOLO.10
YOLOv1
YOLO.12
Figure 5: Comparison of heat maps between YOLOv10 YOLOv12. YOLO012 exhibits clearer object perception than YOLOv10 and YOLO11. All results use X-scale models. Zoom in to compare details.
61,
YOLO11 31 and the proposed
period (about 600/800 epochs) to achieve peak performance, keeping the same configuration used in YOLO11 (31]
the attention mechanism, we apply separable convolution with large kernel to the attention value adding its output to v@ attn. This component, called the Position Perceiver, preserves the original positions of image pixels, helping the attention mechanism perceive positional information. While similar to the PSA module [61]. expand the convolution kernel, improving performance without affecting speed. Increasing the kernel size enhances perfor- mance but slows processing, with a significant slowdown at 9 9. Therefore, set the default kernel size to x 7.
Position Perceiver: Tables 5d
We examine the impact of common positional embeddings (RPE and APE) on performance. Interestingly, the best results are achieved without any positional embedding, leading to cleaner architecture and faster inference.
Position Embedding: Tables 5e.
This table uses the FltehAttention (FA) for YOLOv10 and YOLO11, including a few attention blocks. It can be seen that with FA, they also benefit from speed improve- ments. FA should serve as future infrastructure for the YOLO framework, much like in large language models.
FA for V10/11: Tables 5f
MLP Ratio: Tables 5g In vanilla attention, the MLP ratio within the attention module is typically set to 4.0. However, YOLOv12 behaves differently. The table shows that varying the MLP ratio affects model size,
adjust the feature dimensions for consisteney. In particular, YOLOv12 performs better with an MLP ratio of 1.5, shifting the computational load toward the attention mechanism and emphasizing the importance of area attention.
This table demonstrates the role of FlashAttention in YOLOv 12, showing 0.38ms speedup for YOLOv12-N and 0.67 ms for YOLOv12- 12-S without additional costs.
FlashAttention: Tables 5h
Visualization: Heat Map Comparison. Figure 5 compares the heat maps of YOLOv 12 with YOLOv10 [61 and YOLO11 1
BI]. These heat maps, extracted from the third stage of the backbones of X-scale models, highlight the activated regions, reflecting the model's object perception capability. As illustrated, YOLOv12 shows more defined object contours and better foreground activation than YOLOv10 and YOLO1 indicating improved perception. We explain that this improvement comes from the area attention mechanism, which captures a broader context and enables more precise foreground activation with its larger receptive field than CNN with larger receptive field than CNN. We believe that this characteristic gives YOLOv12 12 a performance advantage.

---

4 Conclusion
This study introduces YOLOv12, which integrates an attention-centicc design into the YOLO frame- work, achieving sfate-oftthe-ar aar aatency-accurace trade-off. We propose novel network that uses area attention to reduce computational complexity and R-ELAN to enhance feature aggregation for efficient inference. Key refinements to the vanilla attention mechanism further align it with YOLO's real-time constraints while maintaining high-speed performance. By combining area attention, R- ELAN, and architectural optimizations, YOLOv12 significantly improves accuracy and efficiency. Comprehensive ablation studies validate these innovations. This work challenges CNN-dominated YOLO designs and advances prrention-bbsedd real-time object detection.
Limitations. YOLOv 12 requires FlashAttention [5], which currently supports Turing, Ampere, Ada Lovelace, or Hopper GPUs (e.g., T4, Quadro RTX series, RTX20 series, RTX30 series, RTX40 series, RTX A5000/6000, A30/40, A100, H100, etc.).
5 Acknowledgment
This work was supported by National Natural Science Foundation of China (NSFC) under Grant 62225208 and 62450046 and CAS Project for CAS Project for Young Scientists Basic Research under Grant No. YSB--117.
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[73] Xianzhe Xu, Yiqi Jiang, Weihua Chen, Yilun Huang, Yuan Zhang, and Xiuyu Sun. Damo-yolo: A report on real-time object detection design. arXiv preprint arX1v:221.11544, 2022.
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A report real-time object detection design. arXiv preprint 12Xi::221111544: 2022.
[75] Zhiqiang Yang, Qiu Guan, Keer Zhao, Jianmin Yang. Xinli Xu, Haixia Long, and Ying Tang. Multi-branch auxiliary fusion yolo with re-parameterizattoon heterogeneous convolutional for accurate object detection. arXiv preprini arXiv:2407.04338 2024.
[76] Qihang Yu, Yingda Xia, Yutong Bai, Yongyi Lu, Alan L Yuille, and Wei Shen. Glance-and-gaze vision transformer. Advances in Neural Information Processing Systems, 34:12992-13003, 2021.
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[78] Yian Zhao, Wenyu Lv, Shangliang Xu, Jinman Wei, Guanzhong Wang, Qingqing Dang, Yi Liu, and Jie Chen. Detrs beat yolos on real-time object detection. In Proceedings of the IEEF/CVF Conference Computer Vision and Pattern Recognition, pages 16965-16974, 2024.
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Table Hyperparameters for training the YOLOv12 family on COCO 39
Hyperparameters
N/S/M/L/X-Scale
Training Configuration
Epochs
600
Optimizer
SGD
Momentum
0.937
Batch size
32x 8
Weight decay
5 x 10
Warm-up epochs
3
Warm-up momentum
0.8
Warm-up bias learning rate
0.0
Initial learning rate
10-2
Final learning rate
10-4
Learning rate schedule
Linear decay
Loss Parameters
Box loss gain
7.5
Class loss gain
0.5
DFL loss gain
1.5
Augmentation Parameters
HSV saturation augmentation
0.7
HSV value augmentation
0.4
HSV hue augmentation
0.015
Translation augmentation
0.1
Scale augmentation
0..90.9/0.9/0.9//..
Mosaic augmentation
1.0
Mixup augmentation
0.0/0.05/0.15/00155022
Copy-paste augmentation
0.1/0.15/0.4/0.5/006
Close mosaic epochs
10
[79] Zhaohui Zheng, Ping Wang, Wei Liu, Jinze Li, Rongguang Ye, and Dongwei Ren. Distance-iou loss: Faster and better learning for bounding box regression. Pruceedings of the AAAI conference artificial intelligence, volume 34, pages 12993-13000, 2020.
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[83] Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai. Deformable detr: Deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159. 2020.
A More Details
Architecture Details, We present the detailed configuration of the overall YOLOv12 network architecture in Table For the backbone, we stack only eight blocks. Except for the convolution layers of downsampling 3 3, the remaining components are two A2 blocks configured with 4 and 1 areas, respectively. We apply grouped convolutions to the downsampling layers with IDs and 2 to save computations, using two groups (g 2) and four groups (g = 4), respectively. For Neck.
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Table 7: Detailed performance of YOLOv12 on COCO.
APa 50.95 (%)
AP
AP (%)
AP
mmall (%
AP
(%
A P
(%)
medium
YOLOv12-N
40.5
56.6
43.7
20.2
45.2
58.2
YOLOv12-S
47.8
64.9
51.5
29.7
53.0
65.3
YOLOv12-M
52.5
69.6
57.0
35.7
58.2
68.9
YOLOv12-L
53.8
71.0
58.6
36.9
59.4
71.0
YOLOv12-X
55.4
72.5
60.3
38.9
60.8
70.9
we follow the design of YOLOv11, replacing only the first three C3K2 modules with A2 blocks. However, do not enable attention in these blocks (A2 False); instead, retain only the feature integration design of R-ELAN. The Head design remains unchanged from YOLOv11.
To implement area attention in the A2 block, we normalization layer to construct the projections for qk and v. Similarly, we use convolution with Batch Normalization layers for the output projection and position perceiver, which facilitates optimization. For the MLP module, we stack two consecutive x convolution layers with a non-lineat activation layer (SiLU function) between them.
convolutional layer 1 with batch
Table 8: Network configurations of YOLOv12.
ID
Module
Route
A2
Num-area
Dims.
Depth
Size
Stride
BackBone
0
Conv
64
3
Conv
0
128
I
2 (g 2)
2
C3K2
1
256
2
Conv
256
I
2 (g = 4)
4
C3K2
3
True
512
2
Conv
4
512
6
A2 block
5
True
4
512
4
7
Conv
6
1024
3
A2 block
True
1024
Neck
Up
8
1024
10
Concat
9.
1024
11
A2 block
10
False
512
12
Up
11
512
2
13
Concat
12, 4
512
14
A2 block
13
False
256
2
15
Conv
14
256
16
Concat
15, 11
256
17
A2 block
16
False
512
18
Conv
17
512
3
19
Concat
18, 8
512
1
20
C3K2
19
1024
2
Head
21
Predict
14, 17,20
Training Details. All YOLOv12 12 models are trained using the default SGD optimizer for 600 epochs. Following previous works [64 26 67 61), the SGD momentum and weight decay are set to 0.937
and 5 x 10-4, respectively. The initial learning rate is set to x 10 and decays linearly to 1 throughout the training process. Data augmentations, including Mosaic [5] 64), Mixup [3]. and
copy-paste augmentation [77], are applied enhance training. Following YOLOvII I [31, we adopt
the Albumentations library [8] Detailed hyperparameters are presented in Table 6 The N/S/M
models are trained on 4x NVIDIA A6000 GPUs and the I/XX models are trained on 8x NVIDIA
A800 GPUs. Following established conventions [26]
67 61
31, report the standard mean average
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precision (mAP) on different object scales and IoU thresholds. In addition, we report the average latency all images.
Result Details, We report more details of the YOLOv12 results Table including A AP 75 small:
Ap 50:95
50
APral Aprel
APral
mediam?
large'
Table 9: Comparative analysis inference speed across different GPUs (RTX 3080, RTX A5000, and RTX A6000). Inference latency: milliseconds (ms) for FP32 and FP16 precision.
FLOPs
Model
Scale
RTX 3080
A5000
A6000
(G)
T
8.2
2.4/1.5
2.4/1.5
2.3/1.5
S
26.4
3.7/1.9
3.3/1.9
3.3/1.8
YOLOv9
67
M
76.3
6.3/2.7
5.4/2.4
5.1/2.4
102.1
7.4/2.8
6.4/2.6
6.0/2.6
E
189.0
15.5/6.5
14.0/6.1
12.9/5.7
1.6/1.0
1.6/1.0
1.6/1.0
21.6
2.8/1.3
2.4/1.3
2.4/1.2
YOLOv10
59.1
5.3/2.4
4.3/2.3
4.2/2.1
92.0
6.7/2.8
5.4/2.5
5.1/2.6
X
160.4
10.7/4.5
7.2/3.5
6.8/3.2
IASS
INN
6.5
1.6/0.9
1.6/0.9
1.5/0.9
2.7/1.3
2.3/1.3
2.3/1.3
YOLOv 31
M
68.0
5,4/2.2
4.4/2.1
4.3/2.0
86.9
6.9/2.9
5.7/2.6
5.6/2.5
X
194.9
13.5/4.9
10.4/4.5
8.9/3.9
N
6.0
1.6/1.0
1.6/1.0
1.6/1.0
S
19.5
2.7/1.3
2.3/1.3
2.3/1.3
YOLOv12
M
KURL
59.9
5.2/2.1
4.4/2.1
4.3/2.1
82.6
6.9/2.9
5.8/2.6
5.7/2.5
184.9
13.6/4.9
10.7/4.6
9.5/4.0
Table 10: Comparison of YOLOv12 series with more lightweight or stronger detectors including DEYO [48). DAMO-YOLO (74,, and recent D-FINE [50].
Model
#Param. (M)
FLOPs (G)
Apral
Latency (ms)
50:95
DEYO-tiny 48
4.0
8.0
37.6
2.01
YOLOv12-N (Ours)
2.6
6.0
40.5
1.62
774
8.5
18.1
42.0
2.21
HAMO-YOLO-S 74
16.3
37.8
46.0
3.18
DEYO-S 48
14.0
26.0
45.8
3.34
YOLOv12-S (Ours)
9.1
19.5
47.8
2.44
DAMO- YOLO-M 77
28.2
61.8
49.2
4.57
DAMO-YOLO-L 74
42.1
97.3
50.8
6.48
DEYO-M 48
33.0
78.0
50.7
7.14
YOL0v12-M (Ours)
19.7
59.9
52.5
4.30
YOLOv7 64
36.9
104.7
51.2
17.03
D-FINE-L 50
31
91
54.0
8.07
YOLOv12-L. (Ours)
26.6
82.6
53.8
5.89
D-FINE-X X 50
62
202
55.8
12.89
XOLOv12-X (Ours)
59.5
184.9
55.4
10.47
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B More Comparisons
Latency Comparison on Various GPUs. Table presents a comparative analysis of inference speed across different GPUs, evaluating YOLOv9 167. YOLOv10 [1]. YOLOv11 [31] and our YOLOv12 on RTX 3080, RTX A5000, and RTX A6000 with FP32 and FPI6 precision. To ensure consistency, all results are obtained on the same hardware, and YOLOv9 [67] and YOLOv10 [6]] are evaluated using the integrated codebase of Ultralytics [3I. Across all tested models, FP16 inference is significantly faster than FP32, often reducing latency by more than 50%. The inference speed generally improves as we move from RTX 3080 to A6000. N-Scale: YOLOv12-N achieves similar latencies (1.6 ms for FP32 and 1.0-1.1 ms for FP16). matching slightly outperforming their YOLOv 10 and YOLOv counterparts. S-Scale: YOLOv12-4 12-S maintains lower latency than YOLOv9-S S and YOLOv10-S while achieving superior FLOPs efficiency. M/L-Scale: YOLOv 2:0012-- 12-M and YOLOv12-L demonstrate competitive speed, with FP16 latencies close to their counterparts, while offering improved accuracy. X-Scale: VOLOvv1-XX achieves 13.6 ms (FP32) and 4.9 ms (FP16) on RTX 3080, matching YOLOv1 1-X and YOLOv10-X in efficiency.
Comparison with Other Detectors. Table presents comparison with other lightweight and state- of-the-art real-time object detectors, such as YOLO 74), YOLOv7 64 DEYO 48]. and D-FINE (50) N-scale: VOLOv12-N achieves 40.5% mAP, surpassing DEYO-tiny while requiring lower computational cost (6.0G FLOOPs vs. 8.0G) and achieving faster inference (1.62ms vs. ms). S-scale: YOLOv12-S (9. IM parameters, 19.5G FLOPs) achieves 47.8% mAP, outperforming DAMO- YOLO-S and DEYO-S with a better balance between accuracy and efficiency. M-scale: YOLOv12-M (19.7M parameters, 59.9G FLOPs) achieves 52.5% mAP, outperforming DAMO-YOLO-M and DEYO-M while being more efficient. L-scale: YOLOv12-L. achiivess achieves 53.8% mAP with 82.6G FLOPs, surpassing DAMO-YOLO-L and YOLOv7 while maintaining faster inference speed. X-scale: YOLOv12-X (59.5M parameters, 184.9G FLOPs) achieves 55.4% mAP, surpassing D-FINE-X while being more computationally efficient and faster.
C Contributions & Broader Impact
Contributions. This work effectively incorporates invention-centric architectures as the core backbone of the YOLO system, achieving saateoffthhe-a art performance. To this end, we make three key contributions:
1. Area Attention Module (A2). We propose a simple yet efficient Area Attention module that maintains a large receptive field while reducing the computational complexity of attention, significantly improving inference speed.
2. Residual Efficient Layer Aggregation Networks (R-ELAN). To address optimization challenges in attention-based models, especially scale, we design R-ELAN. It builds on ELAN 64 and introduces: (i) a block-level residual design with scaling techniques and (fi) improved feature aggregation strategy.
3. Optimization for YOLO Integration. We refine attention-centric architectures to better integrate with the YOLO framework. Key modifications include: incorporating FlashAt- tention to mitigate memory access issue; using decoupled projection strategy construct q. k, and v during attention computation, thus avoiding redundant feature reorganization; removing positional encoding for leaner design: reducing the MLP ratio from 4 to 1.5 to better balance attention and FFN computation; and decreasing the depth of stacked blocks to facilitate optimization.
Broader Impact. This study breaks the dominance of CNN architectures in YOLO systems by utilizing the proposed attention mechanism to achieve one the most advanced YOLO object detectors. It opens up new follow-up research directions, such as transferring other successful techniques from attention mechanisms to further enhance this framework, unlocking greater potential.
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Reviews:
[{"review_id": "dMA5iOxPEf", "reviewer": "Reviewer_EVR3", "summary": "The paper proposes YOLOv12, an attention-centric real-time object detector that for the first time replaces the CNN-backbone tradition of the YOLO family with a pure attention design while matching—or surpassing—CNN speed.\n Key technical contributions are:\n- Area Attention (A²): a simple “segment-wise” local self-attention that only needs one flatten → reshape pair. It preserves a large receptive field, supports any rectangular input (no window divisibility constraints), and runs faster than criss-cross, Swin or CSwin alternatives .\n- R-ELAN backbone block: adds scaled residual shortcuts and a lighter aggregation strategy, stabilising training of deep attention stacks while cutting parameters/FLOPs .\n- YOLO-specific refinements: FlashAttention integration, decoupled Q/K vs V projection (≈10 % speed-up) , removal of positional encodings, reduced MLP ratio (1.5) and shallower stages.", "questions": "1. A² Hyper-parameter l. You default to l = 4 or 8; how sensitive is accuracy and speed to this choice? A short plot would clarify robustness.\n2. In Table 4, the authors show an ablation study in R-ELAN. Can you give another ablation study about using these efficient layer aggregation methods in previous models, like YOLOv11 for example?\n3. In table 5, the authors show diagnostic studies about multiple methods. Can you use these methods in previous models and compare with your model? A table would be appreciated.", "limitations": "Partially. The paper explicitly notes the FlashAttention hardware requirement , but societal impact is left as “NA”. I recommend adding a paragraph on misuse risks (e.g., mass surveillance) and dataset bias.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n1. Extensive evidence: state-of-the-art trade-offs vs. previous baselines; ablations isolate each component. Code-free tricks avoided, training from scratch.\n2. Architecture diagrams, tables, diagnostic studies are easy to follow; checklist answers clarify compute and ethics.\n3. Breaks the long-standing CNN dominance in YOLO by showing attention can equal latency; A² may benefit any dense detector needing rectangular inference.\n4. A²’s extremely light implementation and decoupled-projection trick are novel; R-ELAN is a non-trivial improvement over ELAN.\nWeaknesses\n1. Speed advantage partly hinges on FlashAttention; without it, the gap shrinks (Table 5h)—thus impact depends on hardware adoption.\n2. Use many known ingredients (FlashAttention, residual scaling); novelty is incremental relative to Swin/CSwin in spirit, though well-executed.\\\n3. Lack of theoretical analysis of A²'s superiority.", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate the reviewer's thoughtful feedback and support for our work. Below, we address the reviewer's concerns point by point. We hope our responses address all of your concerns, and we welcome any further comments.\n\n***Q1: Speed advantage partly hinges on FlashAttention.***\n\n***A1:*** Thank you for your insightful comment. We agree that FlashAttention contributes to the speed improvements observed in YOLOv12. However, we would like to emphasize that FlashAttention is increasingly becoming a standard infrastructure-level technique, with growing support across modern GPU hardware. In addition, YOLOv12 incorporates several other key design innovations to enhance efficiency, such as area attention and the decoupled projection of q/k/v, which also play significant roles in accelerating the model. Finally, as shown in Figure 4 (right), YOLOv12 maintains strong performance even on CPU devices without relying on FlashAttention.\n\n***Q2: Use many known ingredients and novelty is incremental relative to Swin/CSwin in spirit, though well-executed.***\n\n***A2:*** Thank you for this insightful comment. We agree that YOLOv12 adopts several well-established techniques. However, we would like to highlight that YOLOv12 also introduces multiple original and novel designs that play a crucial role in its performance. For example, the proposed area attention mechanism does not rely on fixed window patterns; instead, it employs a simple combination of flatten and reshape operations. More importantly, area attention naturally supports rectangular inference—a capability not available in Swin or CSWin. The comparison and advantages of area attention are clearly demonstrated in Table 3. Furthermore, the decoupled projection of q/k/v is a novel and elegant design that improves inference speed by 10% without compromising accuracy. We believe these innovations contribute meaningful novelty to the field and work synergistically to enable YOLOv12’s strong performance.\n\n\n\n\n***Q3: Lack of theoretical analysis of A2's superiority.***\n\n***A3:*** Thank you for your insightful comment. We agree that a formal theoretical analysis of the proposed area attention (A2) module would further strengthen our work. While our current focus is on empirical performance and practical efficiency, A2 is built upon clear design motivations that offer both conceptual novelty and practical benefits. Below, we outline its theoretical foundations and practical strengths:\n\n1. **Receptive Field Control without Window Design.**\n   Unlike traditional local attention mechanisms (e.g., Swin, CSWin), which rely on fixed-size window partitioning, A2 avoids rigid window structures. It flattens a feature map of size \\$(H, W)\\$ into a sequence of $H \\times W$ tokens and partitions them into $l$ equal-length segments. This strategy provides an elegant way to constrain the receptive field to approximately $\\frac{1}{l}$ of the global context—achieving a similar effect to local attention—while remaining fully resolution-agnostic.\n2. **Compatibility with Rectangular Inference.**\n   A2 only requires that the total number of tokens be divisible by $l$, without imposing any constraints on the spatial shape or aspect ratio of the input. This makes A2 inherently compatible with rectangular inference, which is standard in the YOLO evaluation protocol. In contrast, window-based methods often struggle under such conditions due to their reliance on fixed window configurations. A2's flexibility in this regard represents a practical advantage not offered by existing local attention designs.\n3. **Implementation Simplicity and Efficiency.**\n   The A2 module consists of two simple tensor operations—flatten and ​reshape​—making it highly efficient and hardware-friendly. This simplicity translates into faster inference and facilitates seamless integration with real-time frameworks such as YOLO. Additionally, the use of decoupled projection of q/k/v reduces unnecessary feature reorganization in theory, further contributing to improved computational efficiency.\n\nWe hope this explanation clarifies the motivation behind A2 and highlights its practical and conceptual merits, even in the absence of a formal theoretical analysis.\n\n\n\n\n***Q4: How sensitive is accuracy and speed to the choice of $l$?***\n\n***A4:*** Thank you for this valuable comment. We agree that the choice of the hyperparameter $l$ in A2 deserves clarification. During the rebuttal period, we conducted ablation studies using various values of $l$ (2, 4, 8, 16) in the third stage of the YOLOv12-n model. Specifically, these models achieve mAPs of 40.8%, 40.6%, 40.4%, and 40.0%, with corresponding inference times of 1.74 ms, 1.63 ms, 1.61 ms, and 1.58 ms on a T4 GPU, respectively. We will include a plot illustrating this trend in the appendix of the next version to better demonstrate the trade-off between speed and accuracy across different values of $l$.\n\n\n\n\n***Q5: Use R-ELAN on YOLO11.***\n\n***A5:*** Good suggestion! During the rebuttal period, we conducted ablation studies using the R-ELAN module in YOLO11-n and YOLO11-s models, and obtained the following results. The YOLO11-n/s models equipped with R-ELAN consistently achieved faster inference speeds and delivered comparable or even improved performance, while requiring fewer parameters and computational resources. These findings demonstrate the effectiveness and transferability of the R-ELAN design.\n\n| **Model** | **R-ELAN** | **FLOPs (G)** | **Param (M)** | **mAP** | **Lat.** |\n| --------------- | ---------------- | ------------------- | -------------------- | --------------- | ---------------- |\n| N              | ✗               | 6.5                 | 2.6                 | 39.4          | 1.54           |\n| N              | ✓               | 6.3                 | 2.5                 | 39.4          | **1.49**           |\n| S               | ✗               | 21.5                | 9.4                 | 46.9          | 2.53           |\n| S               | ✓               | 21.2                | 9.1                 | 47.0          | **2.47**           |\n\n\n\n\n***Q6: Diagnostic studies using previous models and compare with YOLOv12.***\n\n***A6:*** Good suggestion! Following your advice, we conducted additional experiments on the YOLOv10 and YOLO11 models (using n-scale variants) to validate the effectiveness of our proposed methods. Specifically, we performed diagnostic studies on several key components, including: (a) decoupled projection of q/k/v, (b) the position perceiver, (c) position embedding, and (d) FlashAttention (already reported in Table 5.f). The results are summarized below, from which we draw the following conclusions:\n\n1. Decoupled projection is applicable to both YOLOv10 and YOLO11, and demonstrates speed improvements even though these models employ only a minimal amount of attention operations.\n2. The proposed large-kernel position perceiver consistently improves model performance.\n3. Position embedding does not improve performance and instead negatively impacts speed, which aligns with our findings in YOLOv12.\n4. FlashAttention enhances the performance of YOLOv10 and YOLO11, achieving inference speeds comparable to YOLOv12.\n\n*Decoupled Projection*\n\n| Model   | Lat. (-DP) | Lat. | mAP |\n|---------|------------|------|-----|\n| YOLOv10 | 1.84       | 1.78 | 38.5 |\n| YOLO11  | 1.54       | 1.51 | 39.5 |\n| YOLOv12 | 1.71       | 1.63 | 40.6 |\n\n*Position Perceiver (PP)*\n\n| Model   | Lat./mAP (-PP) | Lat./mAP (+PP) |\n|---------|---------------|---------------|\n| YOLOv10 | 1.84 / 38.5   | 1.89 / 38.7   |\n| YOLO11  | 1.54 / 39.4   | 1.60 / 39.7   |\n| YOLOv12 | 1.57 / 40.3   | 1.63 / 40.6   |\n\n*Position Embedding*\n\n| Model          | Lat. | mAP |\n|----------------|------|-----|\n| YOLOv10 + RPE | 1.90 | 38.2 |\n| YOLOv10 + APE | 1.86 | 38.5 |\n| YOLO11 + RPE  | 1.64 | 39.2 |\n| YOLO11 + APE  | 1.57 | 39.4 |\n| YOLOv12       | 1.63 | 40.6 |\n\n*FlashAttention for YOLO-v10/11*\n\n| Model   | mAP | Lat. |\n|---------|-----|------|\n| YOLOv10 | 38.5 | 1.68 |\n| YOLO11  | 39.4 | 1.49 |\n| YOLOv12 | 40.6 | 1.63 |\n\n***Q7: Societal impact: adding a paragraph on misuse risks.***\n\n***A7:*** Thank you for raising this important point regarding societal impact. While our work focuses on real-time object detection with YOLOv12, primarily aimed at advancing efficient and accurate computer vision techniques, we acknowledge that such technologies could potentially be misused—for example, in mass surveillance or privacy-invading applications. In the next revision, we will include a dedicated paragraph discussing these potential misuse risks, as well as the importance of addressing dataset biases to promote fairness and responsible deployment. Furthermore, we emphasize that usage permissions should be strictly restricted in sensitive contexts to prevent abuse. We believe it is essential to raise awareness of the ethical implications of AI technologies alongside technical advancements."}, {"review_id": "458sEsVKVg", "reviewer": "Reviewer_h5Ez", "summary": "This paper proposes YOLOv12, an \"attention centric\" real-time object detection framework aimed at combining the expressive power of attention mechanisms with the efficiency of CNN architecture. The author introduces three improvements: (1) a local attention mechanism called Area Attention, which claims to reduce computational complexity while maintaining a long receptive field; (2) An improved feature aggregation structure R-ELAN for solving optimization instability problems; (3) Multiple attention structural modifications. Experiments have shown that it outperforms YOLOv10/11 and RT-DETR series on COCO datasets, achieving better trade-off in latency and accuracy.", "questions": "1. Please more clearly distinguish the essential difference between Area Attention and Local/Block Attention mechanism. At present, the essence is to divide the tokens into local groups for self-attention, which is a slight change of the existing methods.\n2. Can we test the generalization ability of A2 in non-YOLO frameworks such as anchor-free detector or cascade head? Otherwise its generality is in doubt.\n3. Is there a theoretical explanation for why the residual scaling + simplified attention combination provides stable training? Only empirical analysis is available.", "limitations": "Refer to Weaknesses and Questions.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. Strong actual performance: YOLOv12 significantly outperforms YOLOv10/11 in all model scales, especially in terms of latency/accuracy and FLOPs/mAP dimensions.\n2. The engineering optimization is relatively comprehensive: combining multiple components such as FlashAttention, structural simplification, and decoupled projection, it demonstrates excellent system design capabilities.\n3. Area Attention design is simple: Compared with window based attention, it supports any input resolution and is deployment friendly, especially suitable for common non square inputs in object detection. \n4. Experimental adequacy: Detailed ablation experiments and speed evaluations are provided, covering GPU/CPU, FP16/FP32, and TensorRT environments.\n\n\nWeaknesses:\n1. The description of Area Attention in Figure 2 is not clear enough, and the intended meaning is not clear enough.\n2. Suggest increasing the visualization comparison of receptive field results to enhance the reliability of the proposed method.\n3. The essence of Area Attention is the local attention of token segmentation, which is similar to the early sliding window/local block attention or chunk-based attention, and there is no fundamental breakthrough in theory or form.\n4. The structural adjustment of R-ELAN is a fine-tuning of ELAN, which is heuristic and lacks evaluation of sufficient theoretical analysis or generalization ability.\n5. No significant improvement in modeling the nature of Transformers:\n6. The authors claim \"attention-centric\" but mainly approximate attention to obtain CNN-like efficiency, which is essentially more like attention-inspired CNNS.\n7. Despite removing positional encoding, location awareness is externalized to large-kernel conv, which is a common alternative in Transformers and not novel.", "was_revised": true, "final_justification": "Most of my concerns have been addressed, and I tend to keep my rating.", "rebuttal": "We appreciate the reviewer’s constructive comments. Below, we address each of the reviewer’s concerns point by point. We hope our responses address all concerns, and we welcome any further feedback.\n\n\n\n***Q1: The description of area attention is not clear enough, and the intended meaning is not clear enough.***\n\n***A1:*** Thank you for your valuable feedback. We acknowledge that the current Figure 2 may not clearly convey the internal mechanism and advantages of the area attention (A2). As suggested, we will improve both the visual explanation and the accompanying textual description to clarify the intended meaning. Specifically, we plan to:\n\n1. Add a diagram that illustrates the flatten → reshape → attention process step by step, to better demonstrate how A2 operates without relying on spatial windowing.\n2. Clarify in the figure legend that ✓ / ✗ indicates support for rectangular inference, and explain key visual elements such as attention coverage.\n3. Include a brief note explaining why window-based methods (e.g., Swin, CSWin) are incompatible with rectangular input sizes due to fixed window alignment, whereas A2 only requires the total token count to be divisible by $l$.\n\nWe believe these changes will significantly enhance clarity and better communicate the core ideas of A2. We welcome any further suggestions.\n\n\n\n\n***Q2: Suggestion on visualization comparison of receptive field results.***\n\n***A2:*** Thank you for your insightful suggestion. We agree that comparing the receptive fields across different attention mechanisms is a valuable way to enhance the interpretability and reliability of our method. In the revised version, we will include a new figure that visualizes the attention maps and receptive field regions of the proposed A2 module. This comparison will illustrate how A2 enables large receptive fields at various values of $l$, particularly for non-square input resolutions, where other methods often struggle due to rigid window partitioning. We believe this visualization will further strengthen the empirical evidence for the effectiveness of A2 and help better convey its advantages.\n\n\n\n\n***Q3: Area attention is similar to the window / local block / chunk-based attentions?***\n\n***A3:*** Thanks for this thoughtful comment. We acknowledge that area attention (A2) shares conceptual similarities with prior local attention mechanisms. However, A2 introduces several key distinctions that, in our view, constitute a meaningful contribution in both design and practical effectiveness. Specifically:\n\n1. Unlike fixed-size or fixed-shape designs typically used for image processing, A2 supports rectangular feature maps, allowing better adaptability to flexible input resolutions.\n2. A2 introduces a decoupled projection that avoids redundant feature reorganization, enabling faster inference without compromising performance.\n3. ​Empirically​, A2 consistently improves both accuracy and speed (Table 3), which fits well with the YOLO system.\n\nWe believe that the novel design, efficient implementation, and demonstrated empirical effectiveness of A2 distinguish it from existing methods and justify its contribution.\n\n\n\n\n***Q4: Theoretical analysis or generalization ability of R-ELAN.***\n\n***A4:*** Thank you for this thoughtful comment. While R-ELAN builds upon ELAN, we emphasize that its design is not merely a heuristic fine-tuning, but rather a targeted architectural solution addressing specific limitations in ELAN—namely, the absence of residual connections, gradient blocking, and instability in deeper models with attention modules. We have conducted the following analysis and incorporated corresponding additions:\n\n1. ​To demonstrate the generalization capability of R-ELAN​, we conducted ablation studies using R-ELAN in YOLO11-n/s models. The results show that models equipped with R-ELAN consistently achieve faster inference speeds and deliver comparable or even improved performance, while using fewer parameters and FLOPs. This supports the effectiveness and generalization of the R-ELAN design.\n2. The introduction of residual connections and a scaling strategy effectively addresses the instability issues observed in ELAN-L and ELAN-X variants, particularly when integrated with attention modules. In addition, R-ELAN rethinks the feature aggregation strategy by avoiding concatenation of all intermediate outputs. This reduces redundancy and improves both parameter and memory efficiency—contributing to better convergence behavior and enhanced deployment practicality.\n\nWe will highlight these aspects in the revised version, including the YOLO11 results, to clarify that R-ELAN is a general, transferable, and well-motivated architectural improvement.\n\n|Model|R-ELAN|FLOPs (G)|Param (M)|mAP|Lat.|\n|--|--|--|--|--|--|\n|N|✗|6.5|2.6|39.4|1.54|\n|N|✓|6.3|2.5|39.4|1.49|\n|S|✗|21.5|9.4|46.9|2.53|\n|S|✓|21.2|9.1|47.0|2.47|\n\n\n\n\n***Q5: No significant improvement in modeling the nature of transformers.***\n\n***A5:*** Thanks for pointing it out. We understand your concern regarding the depth of our contribution in relation to the fundamental modeling nature of Transformers. We respectfully clarify that area attention (A2) addresses several critical limitations of prior efficient attention mechanisms. Specifically, A2 supports native resolutions, freeing it from the limitations of window-based designs and enabling seamless integration with the YOLO framework. A2 introduces efficient operations; for example, it relies solely on flatten and reshape, allowing it to run efficiently. A2 also introduces a novel and elegant decoupled projection of q/k/v, which accelerates the framework by 10% without sacrificing accuracy. We believe these innovations empower YOLOv12 with a distinct advantage and differentiate it from other approaches.\n\n\n\n\n\n***Q6: Attention-centric is essentially more like attention-inspired CNNs.***\n\n***A6:*** We respectfully clarify that area attention (A2) is fundamentally an attention mechanism, not CNN-like. Like standard attention, A2 dynamically aggregates features based on token-to-token affinity within each area, while CNNs use static convolutional kernels. A2 retains the core attention paradigm of query-key-value projection and softmax normalization, only limiting computation spatially. Unlike CNNs’ fixed local receptive fields, A2’s adaptive affinity scores enable long-range dependencies across areas via stacked layers, maintaining Transformer behavior. We optimize attention for efficiency but do not approximate it with convolution. A2’s reshape-only partitioning avoids CNN priors, preserving attention’s dynamic nature.\n\n\n\n\n\n***Q7: Removing positional encoding is a common alternative in transformer and not novel.***\n\n***A7:*** Thanks for pointing it out. We acknowledge that the use of large-kernel convolution to externalize location awareness, in lieu of position encoding, has been explored in prior transformer variants. However, in our design, it serves as a minor architectural improvement rather than a core intellectual contribution. We note that this modification is well adapted to the area attention design, striking an excellent balance between accuracy and speed. As a result, it makes a valuable contribution to the overall performance of the framework.\n\n\n\n\n***Q8: More clearly distinguish the essential difference between area attention and local / block attentions.***\n\n***A8:*** Thanks for the suggestion! We clarify the essential distinctions between area attention and conventional local/block attention in the following three aspects. 1) Local/block attention requires a fixed window design to reduce complexity, while area attention uses flattened 1D segmentations on feature maps that are more flexible and support rectangular inference (important for the YOLO system). 2) Area attention is more efficient due to its elegant designs, such as decoupled projection and the requirement of only a flatten and a reshape operation, which is important for the efficiency of YOLOv12. 3) Area attention also showcases more powerful ability, as shown in Table 3.\n\n\n\n\n***Q9: Test A2 in non-YOLO framework.***\n\n***A9:*** Thank you for your suggestion. Following your advice, during the rebuttal, we replaced the third and fourth stages of the backbone module in the RT-DETRv2-S model with our A2 blocks. We adjusted the dimensions to ensure that the computational cost and parameter count remained approximately the same. Experimental results show that A2 achieved 48.4 mAP and 233 FPS, surpassing the original RT-DETRv2's 47.9 mAP and 217 FPS. This demonstrates the generalization ability of A2. We will include these results in the next version of the paper.\n\n\n\n***Q10: A theoretical explanation for why the residual scaling + simplified attention combination provides stable training.***\n\n***A10:*** Thank you for this constructive suggestion. The training stability primarily stems from the residual scaling mechanism, which addresses gradient pathologies in deep networks. Here is the theoretical rationale. In standard residual connections, the output is\n\n$$\ny = x + \\mathcal{F}(x).\n$$\n\nWhen stacking attention blocks, gradient norms accumulate through the identity term:\n\n$$\n\\frac{\\partial\\mathcal{L}}{\\partial x}=\\frac{\\partial\\mathcal{L}}{\\partial y}\\cdot\\left(1+\\frac{\\partial\\mathcal{F}(x)}{\\partial x}\\right).\n$$\n\nThis can cause exponential gradient growth in deep chains. In R-ELAN's innovation, we scale residual connections by factor $0<\\lambda <1$:\n\n$$\ny = x + \\lambda \\cdot \\mathcal{F}(x).\n$$\n\nSo that the gradient becomes:\n\n$$\n\\frac{\\partial\\mathcal{L}}{\\partial x}=\\frac{\\partial\\mathcal{L}}{\\partial y}\\cdot\\left(1 +\\lambda \\cdot\\frac{\\partial \\mathcal{F}(x)}{\\partial x}\\right).\n$$\n\nThe $\\lambda$ term acts as a gradient dampener, constraining the maximum gradient norm to $O(1+\\lambda)$ rather than $O(2^L)$ for $L$ layers. With residual scaling, $\\lambda$ suppresses large gradient from attention, enabling safe integration. Thus, stability is enabled."}, {"review_id": "Qx72Dgygtw", "reviewer": "Reviewer_XQeS", "summary": "This paper presents YOLOv12, the latest version of the YOLO series, which significantly improves the accuracy of the YOLO family while maintaining comparable or even faster inference speed using an attention centric design. The contributions of YOLOv12 are multifaceted. For example, it introduces the Area Attention (A2) module, designs the Residual Efficient Layer Aggregation Networks (R-ELAN), and incorporates several technical optimizations, such as an innovative decoupled projection strategy for constructing Q, K, and V for attention. These innovations are original and, together, enable YOLOv12 to achieve a dominant latency-accuracy trade-off.\n\nI believe this paper to be of high quality and clearly above the NeurIPS acceptance bar. I strongly recommend its acceptance.", "questions": "1. Will YOLOv12 support other tasks? It appears to be an innovation on architecture, which suggests that YOLOv12 could easily replace task heads to enable training for different tasks. If YOLOv12 can support more tasks, it would greatly expand its applicability and impact.\n2. In line 169, the authors mention setting the MLP ratio to 2 for the N/S/M-scale models, and to 1.5 for the L/X-scale models. Why is there a difference in these settings?", "limitations": "The authors note that YOLOv12 relies on Flash Attention (FA), which limits its deployment on some devices. However, FA is likely to become core infrastructure in deep learning, with growing support across platforms. Thus, in my opinion, using FA in YOLO is a sensible and forward-looking choice.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:    \n1. This paper achieves strong latency-accuracy and FLOPs-accuracy trade-offs, outperforming previous YOLO models. It does not use any pre-trained weights, unlike DETR-based methods, which makes the performance even more impressive.\n2. The proposed Area Attention is efficient and novel. It reduces attention complexity with just a simple flatten and reshape to build areas, avoiding previous complex designs. In addition, it also supports rectangular inference in YOLO, which prior models like Swin and CSWin do not—a clever and practical advantage.\n3. The proposed R-ELAN is innovative and effectively mitigates the optimization challenges caused by attention in the YOLO framework. It offers a simple and generalizable solution to a common issue when adding attention to lightweight models.\n4. Other designs, such as the decoupled projection strategy, also demonstrate innovation and effectively improve inference speed. Such engineering advances are particularly valuable for the YOLO series. The integration of Flash Attention is also interesting and timely—FA is likely to become a core infrastructure in deep learning, and adopting it in YOLO is a sensible and forward-looking decision.\n5. This paper provides extensive ablation and diagnostic experiments on important designs, along with visualizations like those in Figure 5 to demonstrate YOLOv12’s advantages. The appendix also includes detailed information, such as architectural specifics.\n\nWeaknesses:      \n1. I understand that DETR-based methods use pre-trained models, but YOLOv12’s performance is weaker than these methods—for example, D-FINE-X outperforms YOLOv12-X in accuracy (55.8 mAP vs. 55.5 mAP).\n2. In Figure 3, CSPNet, ELAN, and R-ELAN are names of architectures or methods, while C3K2 refers to a module in YOLOv11. I believe these are not aligned consistently, and I suggest revising this for clarity.\n3. I suggest the authors explain why the n-scale model of YOLO11 is faster than YOLOv12, while models of other scales are slower than YOLOv12.", "was_revised": true, "final_justification": "I appreciate the authors' detailed response. The authors' rebuttal has taken care of my potential worries. This work makes a good contribution. My score remains unchanged.", "rebuttal": "We appreciate this reviewer’s support for our work and the valuable comments provided in the review. We respond to your every concern and hope we have addressed them well. Your further comments are welcomed.\n\n***Q1: Lower accuracy compared to D-FINE-X.***\n\n**A1**: We thank the reviewer for this observation. While it is true that D-FINE-X slightly outperforms YOLOv12-X in terms of mAP (55.8 vs. 55.5), our choice of YOLOv12 is deliberate and motivated by its suitability for real-time, resource-constrained applications. Unlike DETR-based methods, which typically rely on computationally expensive pretraining and large transformer backbones, YOLOv12 offers a more efficient alternative, achieving competitive accuracy with lower inference time (12.89 ms vs. 10.53 ms) and fewer computational resources. Additionally, it is worth noting that even without pretraining, YOLOv12 outperforms D-FINE on models of other scales.\n\n***Q2: Clarify C3K2 for aligned consistently.***\n\n**A2**: Thanks for this suggestion. We agree that C3K2 is the name of a module rather than a method. Following your suggestion, we will replace C3K2 with GELAN for consistency.\n\n\n\n***Q3: Why yolo11-n faster than yolov12-n while other scales not?***\n\n**A3:** Thanks for this careful observation. The fact that the n-scale YOLO11 model runs faster than YOLOv12, while the opposite is true for other scales, can be attributed to the extremely lightweight nature of the n-scale models, which have the smallest number of parameters and FLOPs. In this setting, the simpler architecture of YOLO11 incurs less overhead, resulting in slightly faster inference. As the model size increases (e.g., s, m, l, x), the architectural improvements in YOLOv12—such as better feature aggregation and a more efficient backbone—allow it to better utilize computational resources, leading to faster inference overall. As suggested, we will explain this in the revised paper.\n\n\n\n***Q4: Support other tasks?***\n\n**A4:** Good question. Our YOLOv12 is indeed designed to support other tasks beyond object detection. For example, our experiments show that YOLOv12 can be easily adapted for instance segmentation and achieves state-of-the-art results. Specifically, YOLOv12-n/s/m/l/x achieve mAP50–95 (box) scores of ​39.9% / 47.5% / 52.4% / 54.0% / 55.2%, respectively, outperforming all corresponding versions of YOLO11.\n\n\n\n***Q5: MLP ratio setting.***\n\n**A5:** This design is primarily intended to balance the parameter count and computational cost of YOLOv12, ensuring a fair comparison with previous versions such as YOLO11. By aligning the number of parameters and FLOPs of YOLOv12 with those of the corresponding YOLO11 versions, we enable a more equitable and meaningful evaluation."}, {"review_id": "KQ088ystEd", "reviewer": "Reviewer_B1qM", "summary": "This paper introduces YOLOv12, which integrates an attention-centric design into the YOLO framework. By combining area attention, R-ELAN, and architectural optimizations, YOLOv12 significantly improves accuracy and efficiency. Comprehensive experiments validate the effectiveness of the methods.", "questions": "1. Why are the networks compared between the left and right sub-graphs in Figure 4 inconsistent?\n2. What training set is used  in the experiments? Is the experimental comparison fair?\n3. I am confused about the proposed area attention. Why do others not support distortion-free rectangular inference but area attention does?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths\n1. This paper proposes an attentioncentric YOLO framework that matches the speed of previous CNN-based ones while harnessing the performance benefits of attention mechanisms\n2. The experiments and ablation studies are sufficient.\n3. The proposed method seems simple and effective.\n\nWeaknesses\n1. As a YOLO series, it should be open sourced early.\n2. Table 1 should indicate which models are based on CNN and which models are based on Attention.\n3. It is recommended to add necessary code snippets to make it easier to understand area attention and R-ELAN.", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate the reviewer’s thoughtful review and positive comments about our study. In the following sections, we address the reviewer’s every concern and hope our responses address your concerns well. Further comments are welcomed.\n\n***Q1：Open source the code early.***\n\n**A1:** ​Thank you for your valuable comment. YOLOv12 will definitely be open-sourced, including all code and model weights, ensuring that researchers and practitioners can easily access and build upon our work. We will also provide the code link in the final version.\n\n***Q2: Table 1 should clarify CNN-based and Attention-based models.***\n\n**A2:** Thanks for your suggestion. We will indicate this in the final version. Specifically, among the YOLO series models, we are the only ones centered on attention, which strengthens the novelty of YOLOv12.\n\n***Q3: Code snippets of area attention and R-ELAN.***\n\n**A3:** Thank you for your suggestion. We provide the pseudocodes of area attention and R-ELAN below. We will include them in the appendix and update them in the next version.\n\n**Area attention:**\n\n```\nFUNCTION forward(x):\n    // Input shape: [B, C, H, W]\n    B, C, H, W = x.shape\n    N = H * W  // Total spatial positions\n    // Projections\n    qk = self.qk(x)          // Shape: [B, 2*all_head_dim, H, W]\n    v = self.v(x)            // Shape: [B, all_head_dim, H, W]\n    pp = self.pp(v)          // Positional perceiver\n\n    // Flatten to token sequence\n    qk = flatten(qk)  // [B, N, 2*all_head_dim]\n    v = flatten(v)    // [B, N, all_head_dim]\n\n    // Area-based reorganization\n    IF area > 1:\n        qk = reshape(qk, [B*area, N//area, 2*all_head_dim])\n        v = reshape(v, [B*area, N//area, all_head_dim])\n        UPDATE B = B * area, N = N // area\n\n    // Split query/key\n    q, k = split(qk, [all_head_dim, all_head_dim], dim=2)\n\n    // Attention computation\n    x = flash_attention(q, k, v)  // Output shape: [B, N, all_head_dim]\n\n    // Reverse area reorganization\n    IF area > 1:\n        x = reshape(x, [B//area, N*area, all_head_dim])\n        UPDATE B = B // area, N = N * area\n\n    // Spatial restoration\n    x = reshape(x, [B, H, W, all_head_dim])\n    x = permute(x, [0, 3, 1, 2])  // [B, all_head_dim, H, W]\n\n    // Final projection\n    RETURN self.proj(x + pp)\n```\n\n**R-ELAN:**\n\n```\nFUNCTION forward(x):\n    // Input shape: [B, C_in, H, W]\n\n    // Transition convolution\n    x_transition = transition(x)\n\n    current_input_for_block = x_transition\n\n    // Loop through the A2 blocks\n    FOR i FROM 1 TO num_A2_blocks: \n        // Store the input to the current A2 block before applying the block\n        input_to_current_block = current_input_for_block\n\n        // Apply the A2 block\n        A2_block_out = self.A2_blocks[i-1](current_input_for_block)\n\n        // Apply scaling to the input of the current block\n        scaled_input = self.scaling_layers[i-1](input_to_current_block) \n\n        // Add the scaled input to the block's output\n        current_input_for_block = A2_block_out + scaled_input\n\n    // After the loop, current_input_for_block holds the output of the last A2 block\n    last_A2_output = current_input_for_block\n\n    // with the output of the transition block.\n    concatenated_feature = concatenate([last_A2_output, x_transition], dim=CHANNEL_DIM)\n\n    // Final scaling and addition\n    scaled_concatenation = self.scaling_final(concatenated_feature) // Applying scaling\n    output = x + scaled_concatenation // Element-wise addition (residual connection)\n\n    RETURN output\n```\n\n***Q4: Sub-graphs inconsistent in Figure 4.***\n\n**A4:** Thank you for your careful observation. The main reason DETR-based methods are not included in the right figure is that these models have much slower CPU speeds compared to the YOLO series, which weakens the comparison between YOLOv12 and other YOLO models. To better compare models within the YOLO series, we did not include the DETR-based methods. We will clarify this in the revised version.\n\n***Q5: Fair training settings and comparison?***\n\n**A5:** As we have emphasized, the training configuration of YOLOv12 is exactly the same as that of YOLO11, which ensures a fair comparison in the experiments.\n\n***Q6: Why area attention support rectangular inference?***\n\n**A6:** Good question! The main reason area attention supports rectangular inference is that it does not rely on window design. Instead, it flattens the features into a one-dimensional vector and then uses reshape operations to split them into different areas. For example, suppose an image feature has a size of (4, 5). It is difficult to divide it into windows because windows of size (2, 2) or (3, 3) are both unsuitable. However, for area attention, we first flatten it into a one-dimensional vector with dimension (1, 20), then evenly reshape it into 4 or 5 parts to perform self-attention, thus supporting such rectangular inference. Our area attention is very simple and efficient to execute, which ensures its speed advantage."}]

Author rebuttal:

Meta-review: This paper introduces YOLOv12, a novel, attention-centric architecture for real-time object detection that impressively matches the inference speed of prior CNN-based YOLO models while leveraging the performance benefits of attention. The core contributions, including the novel Area Attention mechanism that uniquely supports rectangular inference and an improved R-ELAN backbone for training stability, are well-motivated and empirically validated. The primary strengths of this work, as highlighted by all reviewers, are its state-of-the-art performance, achieving a superior accuracy-latency trade-off, and the thoroughness of its experimental validation and ablation studies. Initial weaknesses pointed out by reviewers included concerns about the novelty of some components, a dependency on FlashAttention, and the lack of theoretical justification for certain design choices. However, the authors provided an exemplary rebuttal, conducting new experiments that demonstrated the generalizability of their methods to other architectures (like YOLOv11 and RT-DETRv2), offering clear theoretical motivations, and clarifying all points of confusion. Given the paper's significant practical contributions to the widely-used YOLO family, its strong empirical results, and the authors' comprehensive engagement with the review process, AC recommends accept.
Decision: Accept (poster)