YOLOv12

YOLOv12: Attention-Centric Real-Time Object Detectors

[Yunjie Tian](https://sunsmarterjie.github.io/)¹, [Qixiang Ye](https://people.ucas.ac.cn/~qxye?language=en)², [David Doermann](https://cse.buffalo.edu/~doermann/)¹ ¹ University at Buffalo, SUNY, ² University of Chinese Academy of Sciences.

Comparison with popular methods in terms of latency-accuracy (left) and FLOPs-accuracy (right) trade-offs

Updates

2025/02/20: Any computer or edge device?. Support yolov12 now.
2025/02/20: ONNX CPP Version. Train a yolov12 model on a custom dataset?
2025/02/19: arXiv version is public. Demo is available.

Abstract

Enhancing the network architecture of the YOLO framework has been crucial for a long time but 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 all popular real-time object detectors in accuracy with competitive speed. For example, YOLOv12-N achieves 40.6% mAP with an inference latency of 1.64 ms on a T4 GPU, outperforming advanced YOLOv10-N / YOLOv11-N by 2.1%/1.2% 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-DETR / RT-DETRv2: YOLOv12-S beats RT-DETR-R18 / RT-DETRv2-R18 while running 42% faster, using only 36% of the computation and 45% of the parameters.

Main Results

Installation

wget https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.3/flash_attn-2.7.3+cu11torch2.2cxx11abiFALSE-cp311-cp311-linux_x86_64.whl
conda create -n yolov12 python=3.11
conda activate yolov12
pip install -r requirements.txt
pip install -e .

Validation

yolov12n yolov12s yolov12m yolov12l yolov12x

from ultralytics import YOLO

model = YOLO('yolov12{n/s/m/l/x}.pt')
model.val(data='coco.yaml', save_json=True)

Training

from ultralytics import YOLO

model = YOLO('yolov12n.yaml')

# Train the model
results = model.train(
  data='coco.yaml',
  epochs=600, 
  batch=256, 
  imgsz=640,
  scale=0.5,  # S:0.9; M:0.9; L:0.9; X:0.9
  mosaic=1.0,
  mixup=0.0,  # S:0.05; M:0.15; L:0.15; X:0.2
  copy_paste=0.1,  # S:0.15; M:0.4; L:0.5; X:0.6
  device="0,1,2,3",
)

# Evaluate model performance on the validation set
metrics = model.val()

# Perform object detection on an image
results = model("path/to/image.jpg")
results[0].show()

Prediction

from ultralytics import YOLO

model = YOLO('yolov12{n/s/m/l/x}.pt')
model.predict()

Export

from ultralytics import YOLO

model = YOLO('yolov12{n/s/m/l/x}.pt')
model.export(format="engine", half=True)  # or format="onnx"

Demo

python app.py
# Please visit http://127.0.0.1:7860

Acknowledgement

The code is based on ultralytics. Thanks for their excellent work!

Citation

@article{tian2025yolov12,
  title={YOLOv12: Attention-Centric Real-Time Object Detectors},
  author={Tian, Yunjie and Ye, Qixiang and Doermann, David},
  journal={arXiv preprint arXiv:2502.12524},
  year={2025}
}

Model	size ^(pixels)	mAP^val 50-95	Speed ^{T4 TensorRT10}	params ^(M)	FLOPs ^(G)
YOLO12n	640	40.6	1.64	2.6	6.5
YOLO12s	640	48.0	2.61	9.3	21.4
YOLO12m	640	52.5	4.86	20.2	67.5
YOLO12l	640	53.7	6.77	26.4	88.9
YOLO12x	640	55.2	11.79	59.1	199.0

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