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- Metadata-Version: 2.4
- Name: ultralytics
- Version: 8.3.63
- Summary: Ultralytics YOLO 🚀 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification.
- Author-email: Glenn Jocher <glenn.jocher@ultralytics.com>, Jing Qiu <jing.qiu@ultralytics.com>
- Maintainer-email: Ultralytics <hello@ultralytics.com>
- License: AGPL-3.0
- Project-URL: Homepage, https://ultralytics.com
- Project-URL: Source, https://github.com/ultralytics/ultralytics
- Project-URL: Documentation, https://docs.ultralytics.com
- Project-URL: Bug Reports, https://github.com/ultralytics/ultralytics/issues
- Project-URL: Changelog, https://github.com/ultralytics/ultralytics/releases
- Keywords: machine-learning,deep-learning,computer-vision,ML,DL,AI,YOLO,YOLOv3,YOLOv5,YOLOv8,YOLOv9,YOLOv10,YOLO11,HUB,Ultralytics
- Classifier: Development Status :: 4 - Beta
- Classifier: Intended Audience :: Developers
- Classifier: Intended Audience :: Education
- Classifier: Intended Audience :: Science/Research
- Classifier: License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)
- Classifier: Programming Language :: Python :: 3
- Classifier: Programming Language :: Python :: 3.8
- Classifier: Programming Language :: Python :: 3.9
- Classifier: Programming Language :: Python :: 3.10
- Classifier: Programming Language :: Python :: 3.11
- Classifier: Programming Language :: Python :: 3.12
- Classifier: Topic :: Software Development
- Classifier: Topic :: Scientific/Engineering
- Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
- Classifier: Topic :: Scientific/Engineering :: Image Recognition
- Classifier: Operating System :: POSIX :: Linux
- Classifier: Operating System :: MacOS
- Classifier: Operating System :: Microsoft :: Windows
- Requires-Python: >=3.8
- Description-Content-Type: text/markdown
- License-File: LICENSE
- Requires-Dist: numpy>=1.23.0
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- Requires-Dist: psutil
- Requires-Dist: py-cpuinfo
- Requires-Dist: pandas>=1.1.4
- Requires-Dist: seaborn>=0.11.0
- Requires-Dist: ultralytics-thop>=2.0.0
- Provides-Extra: dev
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- Dynamic: license-file
- <div align="center">
- <h1>YOLOv12</h1>
- <h3>YOLOv12: Attention-Centric Real-Time Object Detectors</h3>
- [Yunjie Tian](https://sunsmarterjie.github.io/)<sup>1</sup>, [Qixiang Ye](https://people.ucas.ac.cn/~qxye?language=en)<sup>2</sup>, [David Doermann](https://cse.buffalo.edu/~doermann/)<sup>1</sup>
- <sup>1</sup> University at Buffalo, SUNY, <sup>2</sup> University of Chinese Academy of Sciences.
- <p align="center">
- <img src="assets/tradeoff_turbo.svg" width=90%> <br>
- Comparison with popular methods in terms of latency-accuracy (left) and FLOPs-accuracy (right) trade-offs
- </p>
- </div>
- [](https://arxiv.org/abs/2502.12524) [](https://huggingface.co/spaces/sunsmarterjieleaf/yolov12) <a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov12-object-detection-model.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> [](https://www.kaggle.com/code/jxxn03x/yolov12-on-custom-data) [](https://colab.research.google.com/github/lightly-ai/lightly-train/blob/main/examples/notebooks/yolov12.ipynb) [](https://blog.roboflow.com/use-yolov12-with-roboflow/#deploy-yolov12-models-with-roboflow) [](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!
- - 2025/02/21: 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) / [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).
- <details>
- <summary>
- <font size="+1">Abstract</font>
- </summary>
- 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.
- </details>
- ## Main Results
- **Turbo (default)**:
- | Model (det) | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed (ms) <br><sup>T4 TensorRT10<br> | params<br><sup>(M) | FLOPs<br><sup>(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<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed (ms) <br><sup>T4 TensorRT10<br> | params<br><sup>(M) | FLOPs<br><sup>(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<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed (ms) <br><sup>T4 TensorRT10<br> | params<br><sup>(M) | FLOPs<br><sup>(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<br><sup>(pixels) | Acc.<br><sup>top-1<br> | Acc.<br><sup>top-5<br> | Speed (ms) <br><sup>T4 TensorRT10<br> | params<br><sup>(M) | FLOPs<br><sup>(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 |
- </details>
- ## 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/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}
- }
- ```
|
