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YOLOv12

YOLOv12: Attention-Centric Real-Time Object Detectors

Yunjie Tian1, Qixiang Ye2, David Doermann1

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

• 2025/04/15: Pretrain a YOLOv12 model with LightlyTrain, a novel framework that lets you pretrain any computer vision model on your unlabeled data, with YOLOv12 support. Here is also a Colab tutorial!
• 2025/03/18: Some guys are interested in the heatmap. See this issue.
• 2025/03/09: YOLOv12-turbo is released: a faster YOLOv12 version.
• 2025/02/24: Blogs: ultralytics, LearnOpenCV. Thanks to them!
• 2025/02/22: YOLOv12 TensorRT CPP Inference Repo + Google Colab Notebook.
• 2025/02/22: Android deploy / TensorRT-YOLO accelerates yolo12. Thanks to them!
• 2025/02/21: Try yolo12 for classification, oriented bounding boxes, pose estimation, and instance segmentation at ultralytics. Please pay attention to this issue. Thanks to them!
• 2025/02/20: Any computer or edge device? / ONNX CPP Version. Thanks to them!
• 2025/02/20: Train a yolov12 model on a custom dataset: Blog and Youtube. / Step-by-step instruction. Thanks to them!
• 2025/02/19: arXiv version is public. Demo is available (try Demo2 Demo3 if busy). AbstractEnhancing 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

Turbo (default version):

v1.0:

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} }