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
2025/06/17: Use this repo for YOLOv12 instead of ultralytics. Their implementation is inefficient, requires more memory, and has unstable training, which are fixed here!
2025/06/04: YOLOv12's instance segmentation models are released, see code.
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).
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.
Turbo (default):
| Model (det) | size (pixels) | mAPval 50-95 | Speed (ms) T4 TensorRT10 | params (M) | FLOPs (G) |
|---|---|---|---|---|---|
| YOLO12n | 640 | 40.4 | 1.60 | 2.5 | 6.0 |
| YOLO12s | 640 | 47.6 | 2.42 | 9.1 | 19.4 |
| YOLO12m | 640 | 52.5 | 4.27 | 19.6 | 59.8 |
| YOLO12l | 640 | 53.8 | 5.83 | 26.5 | 82.4 |
| YOLO12x | 640 | 55.4 | 10.38 | 59.3 | 184.6 |
v1.0:
| Model (det) | size (pixels) | mAPval 50-95 | Speed (ms) 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 |
| Model (seg) | size (pixels) | mAPbox 50-95 | mAPmask 50-95 | Speed (ms) T4 TensorRT10 | params (M) | FLOPs (B) |
|---|---|---|---|---|---|---|
| YOLOv12n-seg | 640 | 39.9 | 32.8 | 1.84 | 2.8 | 9.9 |
| YOLOv12s-seg | 640 | 47.5 | 38.6 | 2.84 | 9.8 | 33.4 |
| YOLOv12m-seg | 640 | 52.4 | 42.3 | 6.27 | 21.9 | 115.1 |
| YOLOv12l-seg | 640 | 54.0 | 43.2 | 7.61 | 28.8 | 137.7 |
| YOLOv12x-seg | 640 | 55.2 | 44.2 | 15.43 | 64.5 | 308.7 |
wget https://.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 .
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)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()from ultralytics import YOLO
model = YOLO('yolov12{n/s/m/l/x}.pt')
model.predict()from ultralytics import YOLO
model = YOLO('yolov12{n/s/m/l/x}.pt')
model.export(format="engine", half=True) # or format="onnx"python app.py
# Please visit http://127.0.0.1:7860
The code is based on ultralytics. Thanks for their excellent work!
@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}
}