# yolov12 **Repository Path**: dengwenjie/yolov12 ## Basic Information - **Project Name**: yolov12 - **Description**: yolov12镜像 - **Primary Language**: Java - **License**: AGPL-3.0 - **Default Branch**: V1.0 - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 1 - **Forks**: 0 - **Created**: 2025-05-13 - **Last Updated**: 2026-01-16 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

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

[![arXiv](https://img.shields.io/badge/arXiv-2502.12524-b31b1b.svg)](https://arxiv.org/abs/2502.12524) [![Hugging Face Demo](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/sunsmarterjieleaf/yolov12)

[![Kaggle Notebook](https://img.shields.io/badge/Kaggle-Notebook-blue?logo=kaggle)](https://www.kaggle.com/code/jxxn03x/yolov12-on-custom-data) [![deploy](https://media.roboflow.com/deploy.svg)](https://blog.roboflow.com/use-yolov12-with-roboflow/#deploy-yolov12-models-with-roboflow) ## Updates - 2025/02/24: Some blog introductions: [ultralytics](https://docs.ultralytics.com/models/yolo12/), [LearnOpenCV](https://learnopencv.com/yolov12/), [Medium@Mert](https://medium.com/@Mert.A/how-to-use-yolov12-for-object-detection-a93b3c2592a3). Thanks to them! - 2025/02/22: [YOLOv12 TensorRT CPP Inference Repo + Google Colab Notebook Support](https://github.com/mohamedsamirx/YOLOv12-TensorRT-CPP). - 2025/02/22: [Android deploy](https://github.com/mpj1234/ncnn-yolov12-android/tree/main). [TensorRT-YOLO](https://github.com/laugh12321/TensorRT-YOLO) accelerates yolo12 inference. Thanks to them! - 2025/02/21: Try yolo12 for classification, oriented bounding boxes, pose estimation, and instance segmentation at [ultralytics](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/12). Please pay attention to this [issue](https://github.com/sunsmarterjie/yolov12/issues/29). Thanks to them! - 2025/02/20: [Any computer or edge device?](https://github.com/roboflow/inference) Support yolo12 now. - 2025/02/20: [ONNX CPP Version](https://github.com/mohamedsamirx/YOLOv12-ONNX-CPP). Train a yolov12 model on a custom dataset: [Blog](https://blog.roboflow.com/train-yolov12-model/) and [Youtube](https://www.youtube.com/watch?v=fksJmIMIfXo). [Train YOLO12 on a custom dataset step-by-step](https://youtu.be/dO8k5rgXG0M) by [Noor](https://github.com/noorkhokhar99). - 2025/02/19: [arXiv version](https://arxiv.org/abs/2502.12524) is public. [Demo](https://huggingface.co/spaces/sunsmarterjieleaf/yolov12) is available (try [Demo2](https://huggingface.co/spaces/sunsmarterjieleaf/yolov12_demo2) [Demo3](https://huggingface.co/spaces/sunsmarterjieleaf/yolov12_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.

## Main Results | Model | size
^{(pixels) | mAP^{val
50-95 | Speed
^{T4 TensorRT10
| params
^{(M) | FLOPs
^{(G) |
| :----------------------------------------------------------------------------------- | :-------------------: | :-------------------:| :------------------------------:| :-----------------:| :---------------:|
| [YOLO12n](https://github.com/sunsmarterjie/yolov12/releases/download/v1.0/yolov12n.pt) | 640 | 40.6 | 1.64 | 2.6 | 6.5 |
| [YOLO12s](https://github.com/sunsmarterjie/yolov12/releases/download/v1.0/yolov12s.pt) | 640 | 48.0 | 2.61 | 9.3 | 21.4 |
| [YOLO12m](https://github.com/sunsmarterjie/yolov12/releases/download/v1.0/yolov12m.pt) | 640 | 52.5 | 4.86 | 20.2 | 67.5 |
| [YOLO12l](https://github.com/sunsmarterjie/yolov12/releases/download/v1.0/yolov12l.pt) | 640 | 53.7 | 6.77 | 26.4 | 88.9 |
| [YOLO12x](https://github.com/sunsmarterjie/yolov12/releases/download/v1.0/yolov12x.pt) | 640 | 55.2 | 11.79 | 59.1 | 199.0 |

## 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`](https://github.com/sunsmarterjie/yolov12/releases/download/v1.0/yolov12n.pt)
[`yolov12s`](https://github.com/sunsmarterjie/yolov12/releases/download/v1.0/yolov12s.pt)
[`yolov12m`](https://github.com/sunsmarterjie/yolov12/releases/download/v1.0/yolov12m.pt)
[`yolov12l`](https://github.com/sunsmarterjie/yolov12/releases/download/v1.0/yolov12l.pt)
[`yolov12x`](https://github.com/sunsmarterjie/yolov12/releases/download/v1.0/yolov12x.pt)

```python
from ultralytics import YOLO

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

## Training
```python
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
```python
from ultralytics import YOLO

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

## Export
```python
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](https://github.com/ultralytics/ultralytics). Thanks for their excellent work!

## Citation

```BibTeX
@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}
}
```}}}}}