@ -234,14 +234,14 @@ We love your input! YOLOv5 and YOLOv8 would not be possible without help from ou
## <divalign="center">License</div>
YOLOv8 is available under two different licenses:
Ultralytics offers two licensing options to accommodate diverse use cases:
- **AGPL-3.0 License**: See [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for details.
- **Enterprise License**: Provides greater flexibility for commercial product development without the open-source requirements of AGPL-3.0. Typical use cases are embedding Ultralytics software and AI models in commercial products and applications. Request an Enterprise License at [Ultralytics Licensing](https://ultralytics.com/license).
- **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/licenses/) open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for more details.
- **Enterprise License**: Designed for commercial use, this license permits seamless integration of Ultralytics software and AI models into commercial goods and services, bypassing the open-source requirements of AGPL-3.0. If your scenario involves embedding our solutions into a commercial offering, reach out through [Ultralytics Licensing](https://ultralytics.com/license).
## <divalign="center">Contact</div>
For YOLOv8 bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues), and join our [Discord](https://discord.gg/2wNGbc6g9X) community for questions and discussions!
For Ultralytics bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues), and join our [Discord](https://discord.gg/2wNGbc6g9X) community for questions and discussions!
@ -23,4 +23,4 @@ In addition to our Snyk scans, we also use GitHub's [CodeQL](https://docs.github
If you suspect or discover a security vulnerability in any of our repositories, please let us know immediately. You can reach out to us directly via our [contact form](https://ultralytics.com/contact) or via [security@ultralytics.com](mailto:security@ultralytics.com). Our security team will investigate and respond as soon as possible.
We appreciate your help in keeping all Ultralytics open-source projects secure and safe for everyone.
We appreciate your help in keeping all Ultralytics open-source projects secure and safe for everyone.
@ -74,4 +74,4 @@ If you use the Caltech-101 dataset in your research or development work, please
}
```
We would like to acknowledge Li Fei-Fei, Rob Fergus, and Pietro Perona for creating and maintaining the Caltech-101 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the Caltech-101 dataset and its creators, visit the [Caltech-101 dataset website](https://data.caltech.edu/records/mzrjq-6wc02).
We would like to acknowledge Li Fei-Fei, Rob Fergus, and Pietro Perona for creating and maintaining the Caltech-101 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the Caltech-101 dataset and its creators, visit the [Caltech-101 dataset website](https://data.caltech.edu/records/mzrjq-6wc02).
@ -71,4 +71,4 @@ If you use the Caltech-256 dataset in your research or development work, please
We would like to acknowledge Gregory Griffin, Alex Holub, and Pietro Perona for creating and maintaining the Caltech-256 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the
Caltech-256 dataset and its creators, visit the [Caltech-256 dataset website](https://data.caltech.edu/records/nyy15-4j048).
Caltech-256 dataset and its creators, visit the [Caltech-256 dataset website](https://data.caltech.edu/records/nyy15-4j048).
@ -37,10 +37,10 @@ To train a YOLO model on the CIFAR-10 dataset for 100 epochs with an image size
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data='cifar10', epochs=100, imgsz=32)
```
@ -73,4 +73,4 @@ If you use the CIFAR-10 dataset in your research or development work, please cit
}
```
We would like to acknowledge Alex Krizhevsky for creating and maintaining the CIFAR-10 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the CIFAR-10 dataset and its creator, visit the [CIFAR-10 dataset website](https://www.cs.toronto.edu/~kriz/cifar.html).
We would like to acknowledge Alex Krizhevsky for creating and maintaining the CIFAR-10 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the CIFAR-10 dataset and its creator, visit the [CIFAR-10 dataset website](https://www.cs.toronto.edu/~kriz/cifar.html).
@ -73,4 +73,4 @@ If you use the CIFAR-100 dataset in your research or development work, please ci
}
```
We would like to acknowledge Alex Krizhevsky for creating and maintaining the CIFAR-100 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the CIFAR-100 dataset and its creator, visit the [CIFAR-100 dataset website](https://www.cs.toronto.edu/~kriz/cifar.html).
We would like to acknowledge Alex Krizhevsky for creating and maintaining the CIFAR-100 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the CIFAR-100 dataset and its creator, visit the [CIFAR-100 dataset website](https://www.cs.toronto.edu/~kriz/cifar.html).
@ -76,4 +76,4 @@ The example showcases the variety and complexity of the images in the Fashion-MN
## Acknowledgments
If you use the Fashion-MNIST dataset in your research or development work, please acknowledge the dataset by linking to the [GitHub repository](https://github.com/zalandoresearch/fashion-mnist). This dataset was made available by Zalando Research.
If you use the Fashion-MNIST dataset in your research or development work, please acknowledge the dataset by linking to the [GitHub repository](https://github.com/zalandoresearch/fashion-mnist). This dataset was made available by Zalando Research.
@ -76,4 +76,4 @@ If you use the ImageNet dataset in your research or development work, please cit
}
```
We would like to acknowledge the ImageNet team, led by Olga Russakovsky, Jia Deng, and Li Fei-Fei, for creating and maintaining the ImageNet dataset as a valuable resource for the machine learning and computer vision research community. For more information about the ImageNet dataset and its creators, visit the [ImageNet website](https://www.image-net.org/).
We would like to acknowledge the ImageNet team, led by Olga Russakovsky, Jia Deng, and Li Fei-Fei, for creating and maintaining the ImageNet dataset as a valuable resource for the machine learning and computer vision research community. For more information about the ImageNet dataset and its creators, visit the [ImageNet website](https://www.image-net.org/).
@ -71,4 +71,4 @@ If you use the ImageNet10 dataset in your research or development work, please c
}
```
We would like to acknowledge the ImageNet team, led by Olga Russakovsky, Jia Deng, and Li Fei-Fei, for creating and maintaining the ImageNet dataset. The ImageNet10 dataset, while a compact subset, is a valuable resource for quick testing and debugging in the machine learning and computer vision research community. For more information about the ImageNet dataset and its creators, visit the [ImageNet website](https://www.image-net.org/).
We would like to acknowledge the ImageNet team, led by Olga Russakovsky, Jia Deng, and Li Fei-Fei, for creating and maintaining the ImageNet dataset. The ImageNet10 dataset, while a compact subset, is a valuable resource for quick testing and debugging in the machine learning and computer vision research community. For more information about the ImageNet dataset and its creators, visit the [ImageNet website](https://www.image-net.org/).
@ -110,4 +110,4 @@ These smaller versions of the dataset allow for rapid iterations during the deve
## Citations and Acknowledgments
If you use the ImageNette dataset in your research or development work, please acknowledge it appropriately. For more information about the ImageNette dataset, visit the [ImageNette dataset GitHub page](https://github.com/fastai/imagenette).
If you use the ImageNette dataset in your research or development work, please acknowledge it appropriately. For more information about the ImageNette dataset, visit the [ImageNette dataset GitHub page](https://github.com/fastai/imagenette).
@ -81,4 +81,4 @@ The example showcases the subtle differences and similarities among the differen
If you use the ImageWoof dataset in your research or development work, please make sure to acknowledge the creators of the dataset by linking to the [official dataset repository](https://github.com/fastai/imagenette). As of my knowledge cutoff in September 2021, there is no official publication specifically about ImageWoof for citation.
We would like to acknowledge the FastAI team for creating and maintaining the ImageWoof dataset as a valuable resource for the machine learning and computer vision research community. For more information about the ImageWoof dataset, visit the [ImageWoof dataset repository](https://github.com/fastai/imagenette).
We would like to acknowledge the FastAI team for creating and maintaining the ImageWoof dataset as a valuable resource for the machine learning and computer vision research community. For more information about the ImageWoof dataset, visit the [ImageWoof dataset repository](https://github.com/fastai/imagenette).
@ -117,4 +117,4 @@ Ultralytics supports the following datasets with automatic download:
### Adding your own dataset
If you have your own dataset and would like to use it for training classification models with Ultralytics, ensure that it follows the format specified above under "Dataset format" and then point your `data` argument to the dataset directory.
If you have your own dataset and would like to use it for training classification models with Ultralytics, ensure that it follows the format specified above under "Dataset format" and then point your `data` argument to the dataset directory.
@ -40,10 +40,10 @@ To train a CNN model on the MNIST dataset for 100 epochs with an image size of 3
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data='mnist', epochs=100, imgsz=32)
```
@ -79,4 +79,4 @@ research or development work, please cite the following paper:
}
```
We would like to acknowledge Yann LeCun, Corinna Cortes, and Christopher J.C. Burges for creating and maintaining the MNIST dataset as a valuable resource for the machine learning and computer vision research community. For more information about the MNIST dataset and its creators, visit the [MNIST dataset website](http://yann.lecun.com/exdb/mnist/).
We would like to acknowledge Yann LeCun, Corinna Cortes, and Christopher J.C. Burges for creating and maintaining the MNIST dataset as a valuable resource for the machine learning and computer vision research community. For more information about the MNIST dataset and its creators, visit the [MNIST dataset website](http://yann.lecun.com/exdb/mnist/).
@ -86,4 +86,4 @@ If you use the Argoverse dataset in your research or development work, please ci
}
```
We would like to acknowledge Argo AI for creating and maintaining the Argoverse dataset as a valuable resource for the autonomous driving research community. For more information about the Argoverse dataset and its creators, visit the [Argoverse dataset website](https://www.argoverse.org/).
We would like to acknowledge Argo AI for creating and maintaining the Argoverse dataset as a valuable resource for the autonomous driving research community. For more information about the Argoverse dataset and its creators, visit the [Argoverse dataset website](https://www.argoverse.org/).
@ -78,7 +78,7 @@ If you use the COCO dataset in your research or development work, please cite th
```bibtex
@misc{lin2015microsoft,
title={Microsoft COCO: Common Objects in Context},
title={Microsoft COCO: Common Objects in Context},
author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
year={2015},
eprint={1405.0312},
@ -87,4 +87,4 @@ If you use the COCO dataset in your research or development work, please cite th
}
```
We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
@ -68,7 +68,7 @@ If you use the COCO dataset in your research or development work, please cite th
```bibtex
@misc{lin2015microsoft,
title={Microsoft COCO: Common Objects in Context},
title={Microsoft COCO: Common Objects in Context},
author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
year={2015},
eprint={1405.0312},
@ -77,4 +77,4 @@ If you use the COCO dataset in your research or development work, please cite th
}
```
We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
@ -84,4 +84,4 @@ If you use the Global Wheat Head Dataset in your research or development work, p
}
```
We would like to acknowledge the researchers and institutions that contributed to the creation and maintenance of the Global Wheat Head Dataset as a valuable resource for the plant phenotyping and crop management research community. For more information about the dataset and its creators, visit the [Global Wheat Head Dataset website](http://www.global-wheat.com/).
We would like to acknowledge the researchers and institutions that contributed to the creation and maintenance of the Global Wheat Head Dataset as a valuable resource for the plant phenotyping and crop management research community. For more information about the dataset and its creators, visit the [Global Wheat Head Dataset website](http://www.global-wheat.com/).
This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format.
Remember to double-check if the dataset you want to use is compatible with your model and follows the necessary format conventions. Properly formatted datasets are crucial for training successful object detection models.
Remember to double-check if the dataset you want to use is compatible with your model and follows the necessary format conventions. Properly formatted datasets are crucial for training successful object detection models.
@ -85,4 +85,4 @@ If you use the Objects365 dataset in your research or development work, please c
}
```
We would like to acknowledge the team of researchers who created and maintain the Objects365 dataset as a valuable resource for the computer vision research community. For more information about the Objects365 dataset and its creators, visit the [Objects365 dataset website](https://www.objects365.org/).
We would like to acknowledge the team of researchers who created and maintain the Objects365 dataset as a valuable resource for the computer vision research community. For more information about the Objects365 dataset and its creators, visit the [Objects365 dataset website](https://www.objects365.org/).
@ -86,4 +86,4 @@ If you use the SKU-110k dataset in your research or development work, please cit
}
```
We would like to acknowledge Eran Goldman et al. for creating and maintaining the SKU-110k dataset as a valuable resource for the computer vision research community. For more information about the SKU-110k dataset and its creators, visit the [SKU-110k dataset GitHub repository](https://github.com/eg4000/SKU110K_CVPR19).
We would like to acknowledge Eran Goldman et al. for creating and maintaining the SKU-110k dataset as a valuable resource for the computer vision research community. For more information about the SKU-110k dataset and its creators, visit the [SKU-110k dataset GitHub repository](https://github.com/eg4000/SKU110K_CVPR19).
@ -76,8 +76,8 @@ If you use the VisDrone dataset in your research or development work, please cit
```bibtex
@ARTICLE{9573394,
author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={Detection and Tracking Meet Drones Challenge},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={Detection and Tracking Meet Drones Challenge},
year={2021},
volume={},
number={},
@ -85,4 +85,4 @@ If you use the VisDrone dataset in your research or development work, please cit
doi={10.1109/TPAMI.2021.3119563}}
```
We would like to acknowledge the AISKYEYE team at the Lab of Machine Learning and Data Mining, Tianjin University, China, for creating and maintaining the VisDrone dataset as a valuable resource for the drone-based computer vision research community. For more information about the VisDrone dataset and its creators, visit the [VisDrone Dataset GitHub repository](https://github.com/VisDrone/VisDrone-Dataset).
We would like to acknowledge the AISKYEYE team at the Lab of Machine Learning and Data Mining, Tianjin University, China, for creating and maintaining the VisDrone dataset as a valuable resource for the drone-based computer vision research community. For more information about the VisDrone dataset and its creators, visit the [VisDrone Dataset GitHub repository](https://github.com/VisDrone/VisDrone-Dataset).
author={Mark Everingham and Luc Van Gool and Christopher K. I. Williams and John Winn and Andrew Zisserman},
year={2010},
eprint={0909.5206},
@ -88,4 +88,4 @@ If you use the VOC dataset in your research or development work, please cite the
}
```
We would like to acknowledge the PASCAL VOC Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the VOC dataset and its creators, visit the [PASCAL VOC dataset website](http://host.robots.ox.ac.uk/pascal/VOC/).
We would like to acknowledge the PASCAL VOC Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the VOC dataset and its creators, visit the [PASCAL VOC dataset website](http://host.robots.ox.ac.uk/pascal/VOC/).
@ -81,7 +81,7 @@ If you use the xView dataset in your research or development work, please cite t
```bibtex
@misc{lam2018xview,
title={xView: Objects in Context in Overhead Imagery},
title={xView: Objects in Context in Overhead Imagery},
author={Darius Lam and Richard Kuzma and Kevin McGee and Samuel Dooley and Michael Laielli and Matthew Klaric and Yaroslav Bulatov and Brendan McCord},
year={2018},
eprint={1802.07856},
@ -90,4 +90,4 @@ If you use the xView dataset in your research or development work, please cite t
}
```
We would like to acknowledge the [Defense Innovation Unit](https://www.diu.mil/) (DIU) and the creators of the xView dataset for their valuable contribution to the computer vision research community. For more information about the xView dataset and its creators, visit the [xView dataset website](http://xviewdataset.org/).
We would like to acknowledge the [Defense Innovation Unit](https://www.diu.mil/) (DIU) and the creators of the xView dataset for their valuable contribution to the computer vision research community. For more information about the xView dataset and its creators, visit the [xView dataset website](http://xviewdataset.org/).
@ -56,4 +56,4 @@ Image classification is a computer vision task that involves categorizing an ima
Multi-object tracking is a computer vision technique that involves detecting and tracking multiple objects over time in a video sequence.
* [Argoverse](detect/argoverse.md): A dataset containing 3D tracking and motion forecasting data from urban environments with rich annotations for multi-object tracking tasks.
* [VisDrone](detect/visdrone.md): A dataset containing object detection and multi-object tracking data from drone-captured imagery with over 10K images and video sequences.
* [VisDrone](detect/visdrone.md): A dataset containing object detection and multi-object tracking data from drone-captured imagery with over 10K images and video sequences.
@ -79,7 +79,7 @@ If you use the COCO-Pose dataset in your research or development work, please ci
```bibtex
@misc{lin2015microsoft,
title={Microsoft COCO: Common Objects in Context},
title={Microsoft COCO: Common Objects in Context},
author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
year={2015},
eprint={1405.0312},
@ -88,4 +88,4 @@ If you use the COCO-Pose dataset in your research or development work, please ci
}
```
We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO-Pose dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO-Pose dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
@ -68,7 +68,7 @@ If you use the COCO dataset in your research or development work, please cite th
```bibtex
@misc{lin2015microsoft,
title={Microsoft COCO: Common Objects in Context},
title={Microsoft COCO: Common Objects in Context},
author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
year={2015},
eprint={1405.0312},
@ -77,4 +77,4 @@ If you use the COCO dataset in your research or development work, please cite th
}
```
We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format. The `use_keypoints` parameter specifies whether to include keypoints (for pose estimation) in the converted labels.
This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format. The `use_keypoints` parameter specifies whether to include keypoints (for pose estimation) in the converted labels.
@ -78,7 +78,7 @@ If you use the COCO-Seg dataset in your research or development work, please cit
```bibtex
@misc{lin2015microsoft,
title={Microsoft COCO: Common Objects in Context},
title={Microsoft COCO: Common Objects in Context},
author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
year={2015},
eprint={1405.0312},
@ -87,4 +87,4 @@ If you use the COCO-Seg dataset in your research or development work, please cit
}
```
We extend our thanks to the COCO Consortium for creating and maintaining this invaluable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
We extend our thanks to the COCO Consortium for creating and maintaining this invaluable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
@ -68,7 +68,7 @@ If you use the COCO dataset in your research or development work, please cite th
```bibtex
@misc{lin2015microsoft,
title={Microsoft COCO: Common Objects in Context},
title={Microsoft COCO: Common Objects in Context},
author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
year={2015},
eprint={1405.0312},
@ -77,4 +77,4 @@ If you use the COCO dataset in your research or development work, please cite th
}
```
We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
The `auto_annotate` function takes the path to your images, along with optional arguments for specifying the pre-trained detection and [SAM segmentation models](https://docs.ultralytics.com/models/sam), the device to run the models on, and the output directory for saving the annotated results.
By leveraging the power of pre-trained models, auto-annotation can significantly reduce the time and effort required for creating high-quality segmentation datasets. This feature is particularly useful for researchers and developers working with large image collections, as it allows them to focus on model development and evaluation rather than manual annotation.
By leveraging the power of pre-trained models, auto-annotation can significantly reduce the time and effort required for creating high-quality segmentation datasets. This feature is particularly useful for researchers and developers working with large image collections, as it allows them to focus on model development and evaluation rather than manual annotation.
@ -32,4 +32,4 @@ If you notice a test failing, it would be a great help if you could report it th
Remember, a successful CI test does not mean that everything is perfect. It is always recommended to manually review the code before deployment or merging changes.
@ -36,4 +36,4 @@ Improving the accuracy of a YOLO model may involve several strategies, such as:
Remember that there's often a trade-off between accuracy and inference speed, so finding the right balance is crucial for your specific application.
If you have any more questions or need assistance, don't hesitate to consult the Ultralytics documentation or reach out to the community through GitHub Issues or the official discussion forum.
If you have any more questions or need assistance, don't hesitate to consult the Ultralytics documentation or reach out to the community through GitHub Issues or the official discussion forum.
Before your pull request can be merged, all GitHub Actions Continuous Integration (CI) tests must pass. These tests include linting, unit tests, and other checks to ensure that your changes meet the quality standards of the project. Make sure to review the output of the GitHub Actions and fix any issues
Before your pull request can be merged, all GitHub Actions Continuous Integration (CI) tests must pass. These tests include linting, unit tests, and other checks to ensure that your changes meet the quality standards of the project. Make sure to review the output of the GitHub Actions and fix any issues
@ -34,4 +34,4 @@ At Ultralytics, we recognize that the long-term success of our company relies no
This policy reflects our commitment to minimizing our environmental footprint, ensuring the safety and well-being of our employees, and continuously improving our performance.
Please remember that the implementation of an effective EHS policy requires the involvement and commitment of everyone working at or with Ultralytics. We encourage you to take personal responsibility for your safety and the safety of others, and to take care of the environment in which we live and work.
Please remember that the implementation of an effective EHS policy requires the involvement and commitment of everyone working at or with Ultralytics. We encourage you to take personal responsibility for your safety and the safety of others, and to take care of the environment in which we live and work.
@ -15,4 +15,4 @@ Welcome to the Ultralytics Help page! We are committed to providing you with com
- [Environmental, Health and Safety (EHS) Policy](environmental-health-safety.md): Explore Ultralytics' dedicated approach towards maintaining a sustainable, safe, and healthy work environment for all our stakeholders.
- [Security Policy](../SECURITY.md): Understand our security practices and how to report security vulnerabilities responsibly.
We highly recommend going through these guides to make the most of your collaboration with the Ultralytics community. Our goal is to maintain a welcoming and supportive environment for all users and contributors. If you need further assistance, don't hesitate to reach out to us through GitHub Issues or the official discussion forum. Happy coding!
We highly recommend going through these guides to make the most of your collaboration with the Ultralytics community. Our goal is to maintain a welcoming and supportive environment for all users and contributors. If you need further assistance, don't hesitate to reach out to us through GitHub Issues or the official discussion forum. Happy coding!
@ -75,4 +75,4 @@ RuntimeError: Expected input[1, 0, 640, 640] to have 3 channels, but got 0 chann
In this example, the MRE demonstrates the issue with a minimal amount of code, uses a public model ('yolov8n.pt'), includes all necessary dependencies, and provides a clear description of the problem along with the error message.
By following these guidelines, you'll help the maintainers and contributors of Ultralytics YOLO repositories to understand and resolve your issue more efficiently.
By following these guidelines, you'll help the maintainers and contributors of Ultralytics YOLO repositories to understand and resolve your issue more efficiently.
@ -63,4 +63,4 @@ To get started with the Ultralytics Android App, follow these steps:
6. Explore the app's settings to adjust the detection threshold, enable or disable specific object classes, and more.
With the Ultralytics Android App, you now have the power of real-time object detection using YOLO models right at your fingertips. Enjoy exploring the app's features and optimizing its settings to suit your specific use cases.
With the Ultralytics Android App, you now have the power of real-time object detection using YOLO models right at your fingertips. Enjoy exploring the app's features and optimizing its settings to suit your specific use cases.
@ -49,4 +49,4 @@ Welcome to the Ultralytics HUB App! We are excited to introduce this powerful mo
- [**iOS**](./ios.md): Learn about YOLO CoreML models accelerated on Apple's Neural Engine for iPhones and iPads.
- [**Android**](./android.md): Explore TFLite acceleration on Android mobile devices.
Get started today by downloading the Ultralytics HUB App on your mobile device and unlock the potential of YOLOv5 and YOLOv8 models on-the-go. Don't forget to check out our comprehensive [HUB Docs](../) for more information on training, deploying, and using your custom models with the Ultralytics HUB platform.
Get started today by downloading the Ultralytics HUB App on your mobile device and unlock the potential of YOLOv5 and YOLOv8 models on-the-go. Don't forget to check out our comprehensive [HUB Docs](../) for more information on training, deploying, and using your custom models with the Ultralytics HUB platform.
@ -53,4 +53,4 @@ To get started with the Ultralytics iOS App, follow these steps:
6. Explore the app's settings to adjust the detection threshold, enable or disable specific object classes, and more.
With the Ultralytics iOS App, you can now leverage the power of YOLO models for real-time object detection on your iPhone or iPad, powered by the Apple Neural Engine and optimized with FP16 or INT8 quantization.
With the Ultralytics iOS App, you can now leverage the power of YOLO models for real-time object detection on your iPhone or iPad, powered by the Apple Neural Engine and optimized with FP16 or INT8 quantization.
@ -156,4 +156,4 @@ Navigate to the Dataset page of the dataset you want to delete, open the dataset
If you change your mind, you can restore the dataset from the [Trash](https://hub.ultralytics.com/trash) page.
@ -39,4 +39,4 @@ We hope that the resources here will help you get the most out of HUB. Please br
- [**Ultralytics HUB App**](./app/index.md). Learn about the Ultralytics App for iOS and Android, which allows you to run models directly on your mobile device.
* [**iOS**](./app/ios.md). Learn about YOLO CoreML models accelerated on Apple's Neural Engine on iPhones and iPads.
* [**Android**](./app/android.md). Explore TFLite acceleration on mobile devices.
- [**Inference API**](./inference_api.md). Understand how to use the Inference API for running your trained models in the cloud to generate predictions.
- [**Inference API**](./inference_api.md). Understand how to use the Inference API for running your trained models in the cloud to generate predictions.
@ -210,4 +210,4 @@ Navigate to the Model page of the model you want to delete, open the model actio
If you change your mind, you can restore the model from the [Trash](https://hub.ultralytics.com/trash) page.
@ -166,4 +166,4 @@ Navigate to the Project page of the project where the model you want to mode is
Select the project you want to transfer the model to and click **Save**.
@ -48,4 +48,4 @@ Ultralytics YOLO repositories like YOLOv3, YOLOv5, or YOLOv8 are available under
- **AGPL-3.0 License**: See [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for details.
- **Enterprise License**: Provides greater flexibility for commercial product development without the open-source requirements of AGPL-3.0. Typical use cases are embedding Ultralytics software and AI models in commercial products and applications. Request an Enterprise License at [Ultralytics Licensing](https://ultralytics.com/license).
Please note our licensing approach ensures that any enhancements made to our open-source projects are shared back to the community. We firmly believe in the principles of open source, and we are committed to ensuring that our work can be used and improved upon in a manner that benefits everyone.
Please note our licensing approach ensures that any enhancements made to our open-source projects are shared back to the community. We firmly believe in the principles of open source, and we are committed to ensuring that our work can be used and improved upon in a manner that benefits everyone.
@ -166,4 +166,4 @@ We would like to acknowledge the FastSAM authors for their significant contribut
}
```
The original FastSAM paper can be found on [arXiv](https://arxiv.org/abs/2306.12156). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/CASIA-IVA-Lab/FastSAM). We appreciate their efforts in advancing the field and making their work accessible to the broader community.
The original FastSAM paper can be found on [arXiv](https://arxiv.org/abs/2306.12156). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/CASIA-IVA-Lab/FastSAM). We appreciate their efforts in advancing the field and making their work accessible to the broader community.
@ -71,4 +71,4 @@ If you use Baidu's RT-DETR in your research or development work, please cite the
We would like to acknowledge Baidu and the [PaddlePaddle](https://github.com/PaddlePaddle/PaddleDetection) team for creating and maintaining this valuable resource for the computer vision community. Their contribution to the field with the development of the Vision Transformers-based real-time object detector, RT-DETR, is greatly appreciated.
@ -86,7 +86,7 @@ The Segment Anything Model can be employed for a multitude of downstream tasks t
This way you can set image once and run prompts inference multiple times without running image encoder multiple times.
=== "Prompt inference"
```python
from ultralytics.models.sam import Predictor as SAMPredictor
@ -106,7 +106,7 @@ The Segment Anything Model can be employed for a multitude of downstream tasks t
Segment everything with additional args.
=== "Segment everything"
```python
from ultralytics.models.sam import Predictor as SAMPredictor
@ -207,7 +207,7 @@ If you find SAM useful in your research or development work, please consider cit
```bibtex
@misc{kirillov2023segment,
title={Segment Anything},
title={Segment Anything},
author={Alexander Kirillov and Eric Mintun and Nikhila Ravi and Hanzi Mao and Chloe Rolland and Laura Gustafson and Tete Xiao and Spencer Whitehead and Alexander C. Berg and Wan-Yen Lo and Piotr Dollár and Ross Girshick},
year={2023},
eprint={2304.02643},
@ -218,4 +218,4 @@ If you find SAM useful in your research or development work, please consider cit
We would like to express our gratitude to Meta AI for creating and maintaining this valuable resource for the computer vision community.
*keywords: Segment Anything, Segment Anything Model, SAM, Meta SAM, image segmentation, promptable segmentation, zero-shot performance, SA-1B dataset, advanced architecture, auto-annotation, Ultralytics, pre-trained models, SAM base, SAM large, instance segmentation, computer vision, AI, artificial intelligence, machine learning, data annotation, segmentation masks, detection model, YOLO detection model, bibtex, Meta AI.*
*keywords: Segment Anything, Segment Anything Model, SAM, Meta SAM, image segmentation, promptable segmentation, zero-shot performance, SA-1B dataset, advanced architecture, auto-annotation, Ultralytics, pre-trained models, SAM base, SAM large, instance segmentation, computer vision, AI, artificial intelligence, machine learning, data annotation, segmentation masks, detection model, YOLO detection model, bibtex, Meta AI.*
@ -106,4 +106,4 @@ If you employ YOLO-NAS in your research or development work, please cite SuperGr
We express our gratitude to Deci AI's [SuperGradients](https://github.com/Deci-AI/super-gradients/) team for their efforts in creating and maintaining this valuable resource for the computer vision community. We believe YOLO-NAS, with its innovative architecture and superior object detection capabilities, will become a critical tool for developers and researchers alike.
@ -55,7 +55,7 @@ We would like to acknowledge the YOLOv4 authors for their significant contributi
```bibtex
@misc{bochkovskiy2020yolov4,
title={YOLOv4: Optimal Speed and Accuracy of Object Detection},
title={YOLOv4: Optimal Speed and Accuracy of Object Detection},
author={Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao},
year={2020},
eprint={2004.10934},
@ -64,4 +64,4 @@ We would like to acknowledge the YOLOv4 authors for their significant contributi
}
```
The original YOLOv4 paper can be found on [arXiv](https://arxiv.org/pdf/2004.10934.pdf). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/AlexeyAB/darknet). We appreciate their efforts in advancing the field and making their work accessible to the broader community.
The original YOLOv4 paper can be found on [arXiv](https://arxiv.org/pdf/2004.10934.pdf). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/AlexeyAB/darknet). We appreciate their efforts in advancing the field and making their work accessible to the broader community.
@ -70,7 +70,7 @@ We would like to acknowledge the authors for their significant contributions in
```bibtex
@misc{li2023yolov6,
title={YOLOv6 v3.0: A Full-Scale Reloading},
title={YOLOv6 v3.0: A Full-Scale Reloading},
author={Chuyi Li and Lulu Li and Yifei Geng and Hongliang Jiang and Meng Cheng and Bo Zhang and Zaidan Ke and Xiaoming Xu and Xiangxiang Chu},
year={2023},
eprint={2301.05586},
@ -79,4 +79,4 @@ We would like to acknowledge the authors for their significant contributions in
}
```
The original YOLOv6 paper can be found on [arXiv](https://arxiv.org/abs/2301.05586). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/meituan/YOLOv6). We appreciate their efforts in advancing the field and making their work accessible to the broader community.
The original YOLOv6 paper can be found on [arXiv](https://arxiv.org/abs/2301.05586). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/meituan/YOLOv6). We appreciate their efforts in advancing the field and making their work accessible to the broader community.
@ -58,4 +58,4 @@ We would like to acknowledge the YOLOv7 authors for their significant contributi
}
```
The original YOLOv7 paper can be found on [arXiv](https://arxiv.org/pdf/2207.02696.pdf). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/WongKinYiu/yolov7). We appreciate their efforts in advancing the field and making their work accessible to the broader community.
The original YOLOv7 paper can be found on [arXiv](https://arxiv.org/pdf/2207.02696.pdf). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/WongKinYiu/yolov7). We appreciate their efforts in advancing the field and making their work accessible to the broader community.
@ -112,4 +112,4 @@ If you use the YOLOv8 model or any other software from this repository in your w
}
```
Please note that the DOI is pending and will be added to the citation once it is available. The usage of the software is in accordance with the AGPL-3.0 license.
Please note that the DOI is pending and will be added to the citation once it is available. The usage of the software is in accordance with the AGPL-3.0 license.
Run inference on a collection of images, URLs, videos and directories listed in a CSV file.
```python
import torch
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
# Define a path to a CSV file with images, URLs, videos and directories
source = 'path/to/file.csv'
# Run inference on the source
results = model(source) # list of Results objects
```
=== "video"
Run inference on a video file. By using `stream=True`, you can create a generator of Results objects to reduce memory usage.
```python
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
# Define path to video file
source = 'path/to/video.mp4'
# Run inference on the source
results = model(source, stream=True) # generator of Results objects
```
=== "directory"
Run inference on all images and videos in a directory. To also capture images and videos in subdirectories use a glob pattern, i.e. `path/to/dir/**/*`.
```python
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
# Define path to directory containing images and videos for inference
source = 'path/to/dir'
# Run inference on the source
results = model(source, stream=True) # generator of Results objects
```
=== "glob"
Run inference on all images and videos that match a glob expression with `*` characters.
```python
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
# Define a glob search for all JPG files in a directory
source = 'path/to/dir/*.jpg'
# OR define a recursive glob search for all JPG files including subdirectories
source = 'path/to/dir/**/*.jpg'
# Run inference on the source
results = model(source, stream=True) # generator of Results objects
```
=== "YouTube"
Run inference on a YouTube video. By using `stream=True`, you can create a generator of Results objects to reduce memory usage for long videos.
```python
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
# Define source as YouTube video URL
source = 'https://youtu.be/Zgi9g1ksQHc'
# Run inference on the source
results = model(source, stream=True) # generator of Results objects
```
=== "Stream"
Run inference on remote streaming sources using RTSP, RTMP, and IP address protocols.
```python
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
# Define source as RTSP, RTMP or IP streaming address
source = 'rtsp://example.com/media.mp4'
# Run inference on the source
results = model(source, stream=True) # generator of Results objects
```
@ -417,7 +417,7 @@ operations are cached, meaning they're only calculated once per object, and thos
masks = results[0].masks # Masks object
masks.xy # x, y segments (pixels), List[segment] * N
masks.xyn # x, y segments (normalized), List[segment] * N
masks.data # raw masks tensor, (N, H, W) or masks.masks
masks.data # raw masks tensor, (N, H, W) or masks.masks
```
### Keypoints
@ -432,7 +432,7 @@ operations are cached, meaning they're only calculated once per object, and thos
keypoints.xy # x, y keypoints (pixels), (num_dets, num_kpts, 2/3), the last dimension can be 2 or 3, depends the model.
keypoints.xyn # x, y keypoints (normalized), (num_dets, num_kpts, 2/3)
keypoints.conf # confidence score(num_dets, num_kpts) of each keypoint if the last dimension is 3.
keypoints.data # raw keypoints tensor, (num_dets, num_kpts, 2/3)
keypoints.data # raw keypoints tensor, (num_dets, num_kpts, 2/3)
```
### probs
@ -448,7 +448,7 @@ operations are cached, meaning they're only calculated once per object, and thos
probs.top1 # The top1 indices of classification, a value with Int type.
probs.top5conf # The top5 scores of classification, a tensor with shape (5, ).
probs.top1conf # The top1 scores of classification. a value with torch.tensor type.
keypoints.data # raw probs tensor, (num_class, )
keypoints.data # raw probs tensor, (num_class, )
```
Class reference documentation for `Results` module and its components can be found [here](../reference/engine/results.md)
@ -489,37 +489,37 @@ Here's a Python script using OpenCV (cv2) and YOLOv8 to run inference on video f
```python
import cv2
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO('yolov8n.pt')
# Open the video file
video_path = "path/to/your/video/file.mp4"
cap = cv2.VideoCapture(video_path)
# Loop through the video frames
while cap.isOpened():
# Read a frame from the video
success, frame = cap.read()
if success:
# Run YOLOv8 inference on the frame
results = model(frame)
# Visualize the results on the frame
annotated_frame = results[0].plot()
# Display the annotated frame
cv2.imshow("YOLOv8 Inference", annotated_frame)
# Break the loop if 'q' is pressed
if cv2.waitKey(1) & 0xFF == ord("q"):
break
else:
# Break the loop if the end of the video is reached
break
# Release the video capture object and close the display window
This will load TensorBoard and direct it to the directory where your training logs are saved.
After setting up your logger, you can then proceed with your model training. All training metrics will be automatically logged in your chosen platform, and you can access these logs to monitor your model's performance over time, compare different models, and identify areas for improvement.
After setting up your logger, you can then proceed with your model training. All training metrics will be automatically logged in your chosen platform, and you can access these logs to monitor your model's performance over time, compare different models, and identify areas for improvement.
@ -19,7 +19,7 @@ Ultralytics provides various installation methods including pip, conda, and Dock
# Install the ultralytics package using pip
pip install ultralytics
```
=== "Conda install"
Conda is an alternative package manager to pip which may also be used for installation. Visit Anaconda for more details at [https://anaconda.org/conda-forge/ultralytics](https://anaconda.org/conda-forge/ultralytics). Ultralytics feedstock repository for updating the conda package is at [https://github.com/conda-forge/ultralytics-feedstock/](https://github.com/conda-forge/ultralytics-feedstock/).
@ -30,16 +30,16 @@ Ultralytics provides various installation methods including pip, conda, and Dock
# Install the ultralytics package using conda
conda install ultralytics
```
=== "Git clone"
Clone the `ultralytics` repository if you are interested in contributing to the development or wish to experiment with the latest source code. After cloning, navigate into the directory and install the package in editable mode `-e` using pip.
# Install the package in editable mode for development
pip install -e .
```
@ -48,27 +48,27 @@ Ultralytics provides various installation methods including pip, conda, and Dock
Utilize Docker to execute the `ultralytics` package in an isolated container. By employing the official `ultralytics` image from [Docker Hub](https://hub.docker.com/r/ultralytics/ultralytics), you can avoid local installation. Below are the commands to get the latest image and execute it:
# Pull the latest ultralytics image from Docker Hub
sudo docker pull $t
# Run the ultralytics image in a container with GPU support
sudo docker run -it --ipc=host --gpus all $t
```
The above command initializes a Docker container with the latest `ultralytics` image. The `-it` flag assigns a pseudo-TTY and maintains stdin open, enabling you to interact with the container. The `--ipc=host` flag sets the IPC (Inter-Process Communication) namespace to the host, which is essential for sharing memory between processes. The `--gpus all` flag enables access to all available GPUs inside the container, which is crucial for tasks that require GPU computation.
Note: To work with files on your local machine within the container, use Docker volumes for mounting a local directory into the container:
```bash
# Mount local directory to a directory inside the container
sudo docker run -it --ipc=host --gpus all -v /path/on/host:/path/in/container $t
```
Alter `/path/on/host` with the directory path on your local machine, and `/path/in/container` with the desired path inside the Docker container for accessibility.
See the `ultralytics` [requirements.txt](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) file for a list of dependencies. Note that all examples above install all required dependencies.
@ -160,24 +160,111 @@ For example, users can load a model, train it, evaluate its performance on a val
```python
from ultralytics import YOLO
# Create a new YOLO model from scratch
model = YOLO('yolov8n.yaml')
# Load a pretrained YOLO model (recommended for training)
model = YOLO('yolov8n.pt')
# Train the model using the 'coco128.yaml' dataset for 3 epochs
The Ultralytics library provides a powerful settings management system to enable fine-grained control over your experiments. By making use of the `SettingsManager` housed within the `ultralytics.utils` module, users can readily access and alter their settings. These are stored in a YAML file and can be viewed or modified either directly within the Python environment or via the Command-Line Interface (CLI).
### Inspecting Settings
To gain insight into the current configuration of your settings, you can view them directly:
!!! example "View settings"
=== "Python"
You can use Python to view your settings. Start by importing the `settings` object from the `ultralytics` module. Print and return settings using the following commands:
```python
from ultralytics import settings
# View all settings
print(settings)
# Return a specific setting
value = settings['runs_dir']
```
=== "CLI"
Alternatively, the command-line interface allows you to check your settings with a simple command:
```bash
yolo settings
```
### Modifying Settings
Ultralytics allows users to easily modify their settings. Changes can be performed in the following ways:
!!! example "Update settings"
=== "Python"
Within the Python environment, call the `update` method on the `settings` object to change your settings:
The table below provides an overview of the settings available for adjustment within Ultralytics. Each setting is outlined along with an example value, the data type, and a brief description.
| Name | Example Value | Data Type | Description |
Glenn Jocher
1 year ago
committed by
GitHub