Title: YOLOv12: Attention-Centric Real-Time Object Detectors
Venue: NeurIPS 2025
Accepted: yes
Keywords: YOLO

Real-Time Detection

Attention-Centric
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.


Peer reviews:
comments: 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.

STRENGTHS AND WEAKNESSES:
Strengths
1. This paper proposes an attentioncentric YOLO framework that matches the speed of previous CNN-based ones while harnessing the performance benefits of attention mechanisms
2. The experiments and ablation studies are sufficient.
3. The proposed method seems simple and effective.

Weaknesses
1. As a YOLO series, it should be open sourced early.
2. Table 1 should indicate which models are based on CNN and which models are based on Attention.
3. It is recommended to add necessary code snippets to make it easier to understand area attention and R-ELAN.

QUESTIONS:
1. Why are the networks compared between the left and right sub-graphs in Figure 4 inconsistent?
2. What training set is used  in the experiments? Is the experimental comparison fair?
3. I am confused about the proposed area attention. Why do others not support distortion-free rectangular inference but area attention does?

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 3
DATE: 2025-06-19

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:    \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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nRefer to Weaknesses and Questions.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nPartially. 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}