# yolov12 **Repository Path**: monkeycc/yolov12 ## Basic Information - **Project Name**: yolov12 - **Description**: https://github.com/sunsmarterjie/yolov12 - **Primary Language**: Python - **License**: AGPL-3.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 1 - **Forks**: 0 - **Created**: 2025-07-11 - **Last Updated**: 2026-03-13 ## 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) Open In Colab [![Kaggle Notebook](https://img.shields.io/badge/Kaggle-Notebook-blue?logo=kaggle)](https://www.kaggle.com/code/jxxn03x/yolov12-on-custom-data) [![LightlyTrain Notebook](https://img.shields.io/badge/LightlyTrain-Notebook-blue?)](https://colab.research.google.com/github/lightly-ai/lightly-train/blob/main/examples/notebooks/yolov12.ipynb) [![deploy](https://media.roboflow.com/deploy.svg)](https://blog.roboflow.com/use-yolov12-with-roboflow/#deploy-yolov12-models-with-roboflow) [![Openbayes](https://img.shields.io/static/v1?label=Demo&message=OpenBayes%E8%B4%9D%E5%BC%8F%E8%AE%A1%E7%AE%97&color=green)](https://openbayes.com/console/public/tutorials/A4ac4xNrUCQ) ## Updates - 2025/06/17: **Use this repo for YOLOv12 instead of [ultralytics](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/12). Their implementation is inefficient, requires more memory, and has unstable training, which are fixed here!** - 2025/07/01: YOLOv12's **classification** models are released, see [code](https://github.com/sunsmarterjie/yolov12/tree/Cls). - 2025/06/04: YOLOv12's **instance segmentation** models are released, see [code](https://github.com/sunsmarterjie/yolov12/tree/Seg). - 2025/04/15: Pretrain a YOLOv12 model with [LightlyTrain](https://docs.lightly.ai/train/stable/index.html), a novel framework that lets you pretrain any computer vision model on your unlabeled data, with [YOLOv12 support](https://docs.lightly.ai/train/stable/models/yolov12.html). Here is also a [Colab tutorial](https://colab.research.google.com/github/lightly-ai/lightly-train/blob/main/examples/notebooks/yolov12.ipynb)! - 2025/03/18: Some guys are interested in the heatmap. See this [issue](https://github.com/sunsmarterjie/yolov12/issues/74). - 2025/03/09: **YOLOv12-turbo** is released: a faster YOLOv12 version. - 2025/02/24: Blogs: [ultralytics](https://docs.ultralytics.com/models/yolo12/), [LearnOpenCV](https://learnopencv.com/yolov12/). Thanks to them! - 2025/02/22: [YOLOv12 TensorRT CPP Inference Repo + Google Colab Notebook](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. Thanks to them! - 2025/02/20: [Any computer or edge device?](https://github.com/roboflow/inference) / [ONNX CPP Version](https://github.com/mohamedsamirx/YOLOv12-ONNX-CPP). Thanks to them! - 2025/02/20: 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). / [Step-by-step instruction](https://youtu.be/dO8k5rgXG0M). Thanks to them! - 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 **Turbo (default)**: | Model (det) | size
(pixels) | mAPval
50-95 | Speed (ms)
T4 TensorRT10
| params
(M) | FLOPs
(G) | | :----------------------------------------------------------------------------------- | :-------------------: | :-------------------:| :------------------------------:| :-----------------:| :---------------:| | [YOLO12n](https://github.com/sunsmarterjie/yolov12/releases/download/turbo/yolov12n.pt) | 640 | 40.4 | 1.60 | 2.5 | 6.0 | | [YOLO12s](https://github.com/sunsmarterjie/yolov12/releases/download/turbo/yolov12s.pt) | 640 | 47.6 | 2.42 | 9.1 | 19.4 | | [YOLO12m](https://github.com/sunsmarterjie/yolov12/releases/download/turbo/yolov12m.pt) | 640 | 52.5 | 4.27 | 19.6 | 59.8 | | [YOLO12l](https://github.com/sunsmarterjie/yolov12/releases/download/turbo/yolov12l.pt) | 640 | 53.8 | 5.83 | 26.5 | 82.4 | | [YOLO12x](https://github.com/sunsmarterjie/yolov12/releases/download/turbo/yolov12x.pt) | 640 | 55.4 | 10.38 | 59.3 | 184.6 | [**v1.0**](https://github.com/sunsmarterjie/yolov12/tree/V1.0): | Model (det) | size
(pixels) | mAPval
50-95 | Speed (ms)
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 | [**Instance segmentation**](https://github.com/sunsmarterjie/yolov12/tree/Seg): | Model (seg) | size
(pixels) | mAPbox
50-95 | mAPmask
50-95 | Speed (ms)
T4 TensorRT10
| params
(M) | FLOPs
(G) | | :------------------------------------------------------------------------------------| :--------------------: | :-------------------: | :---------------------: | :--------------------------------:| :------------------: | :-----------------: | | [YOLOv12n-seg](https://github.com/sunsmarterjie/yolov12/releases/download/seg/yolov12n-seg.pt) | 640 | 39.9 | 32.8 | 1.84 | 2.8 | 9.9 | | [YOLOv12s-seg](https://github.com/sunsmarterjie/yolov12/releases/download/seg/yolov12s-seg.pt) | 640 | 47.5 | 38.6 | 2.84 | 9.8 | 33.4 | | [YOLOv12m-seg](https://github.com/sunsmarterjie/yolov12/releases/download/seg/yolov12m-seg.pt) | 640 | 52.4 | 42.3 | 6.27 | 21.9 | 115.1 | | [YOLOv12l-seg](https://github.com/sunsmarterjie/yolov12/releases/download/seg/yolov12l-seg.pt) | 640 | 54.0 | 43.2 | 7.61 | 28.8 | 137.7 | | [YOLOv12x-seg](https://github.com/sunsmarterjie/yolov12/releases/download/seg/yolov12x-seg.pt) | 640 | 55.2 | 44.2 | 15.43 | 64.5 | 308.7 | [**Classification**](https://github.com/sunsmarterjie/yolov12/tree/Cls): | Model (cls) | size
(pixels) | Acc.
top-1
| Acc.
top-5
| Speed (ms)
T4 TensorRT10
| params
(M) | FLOPs
(G) | | :----------------------------------------------------------------------------------------| :-------------------: | :------------: | :------------: | :-------------------------------------:| :----------------: | :---------------: | | [YOLOv12n-cls](https://github.com/sunsmarterjie/yolov12/releases/download/cls/yolov12n-cls.pt) | 224 | 71.7 | 90.5 | 1.27 | 2.9 | 0.5 | | [YOLOv12s-cls](https://github.com/sunsmarterjie/yolov12/releases/download/cls/yolov12s-cls.pt) | 224 | 76.4 | 93.3 | 1.52 | 7.2 | 1.5 | | [YOLOv12m-cls](https://github.com/sunsmarterjie/yolov12/releases/download/cls/yolov12m-cls.pt) | 224 | 78.8 | 94.4 | 2.03 | 12.7 | 4.5 | | [YOLOv12l-cls](https://github.com/sunsmarterjie/yolov12/releases/download/cls/yolov12l-cls.pt) | 224 | 79.5 | 94.5 | 2.73 | 16.8 | 6.2 | | [YOLOv12x-cls](https://github.com/sunsmarterjie/yolov12/releases/download/cls/yolov12x-cls.pt) | 224 | 80.1 | 95.3 | 3.64 | 35.5 | 13.7 | ## Installation ``` conda create -n yolov12 python=3.11 supervision flash-attn conda activate yolov12 git clone https://github.com/sunsmarterjie/yolov12 && cd yolov12 pip install -r requirements.txt pip install -e . ``` ## Validation [`yolov12n`](https://github.com/sunsmarterjie/yolov12/releases/download/turbo/yolov12n.pt) [`yolov12s`](https://github.com/sunsmarterjie/yolov12/releases/download/turbo/yolov12s.pt) [`yolov12m`](https://github.com/sunsmarterjie/yolov12/releases/download/turbo/yolov12m.pt) [`yolov12l`](https://github.com/sunsmarterjie/yolov12/releases/download/turbo/yolov12l.pt) [`yolov12x`](https://github.com/sunsmarterjie/yolov12/releases/download/turbo/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} } ```