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2 changes: 1 addition & 1 deletion .github/workflows/format.yml
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Expand Up @@ -45,7 +45,7 @@ jobs:
pip install -r requirements.txt # install
```
## Environments
YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda-zone)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
- **Notebooks** with free GPU: <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/models/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)
- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)
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6 changes: 3 additions & 3 deletions README.md
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Expand Up @@ -30,7 +30,7 @@ To request an Enterprise License, please complete the form at [Ultralytics Licen
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Expand Down Expand Up @@ -487,7 +487,7 @@ We welcome your contributions! Making YOLOv5 accessible and effective is a commu

Ultralytics provides two licensing options to meet different needs:

- **AGPL-3.0 License**: An [OSI-approved](https://opensource.org/license/agpl-v3) open-source license ideal for academic research, personal projects, and testing. It promotes open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE) file for details.
- **AGPL-3.0 License**: An [OSI-approved](https://opensource.org/license/agpl-3.0) open-source license ideal for academic research, personal projects, and testing. It promotes open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE) file for details.
- **Enterprise License**: Tailored for commercial applications, this license allows seamless integration of Ultralytics software and AI models into commercial products and services, bypassing the open-source requirements of AGPL-3.0. For commercial use cases, please contact us via [Ultralytics Licensing](https://www.ultralytics.com/license).

## 📧 Contact
Expand All @@ -502,7 +502,7 @@ For bug reports and feature requests related to YOLOv5, please visit [GitHub Iss
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8 changes: 4 additions & 4 deletions README.zh-CN.md
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Expand Up @@ -30,7 +30,7 @@ Ultralytics YOLOv5 🚀 是由 [Ultralytics](https://www.ultralytics.com/) 开
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Expand Down Expand Up @@ -224,7 +224,7 @@ python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5x.yaml --

## ⭐ Ultralytics Platform

通过 [Ultralytics Platform](https://platform.ultralytics.com) ⭐ 体验无缝的 AI 开发,这是构建、训练和部署[计算机视觉](https://www.ultralytics.com/glossary/computer-vision-cv)模型的终极平台。可视化数据集,训练 [YOLOv5](https://docs.ultralytics.com/models/yolov5/) 和 [YOLOv8](https://docs.ultralytics.com/models/yolov8/) 🚀 模型,并将它们部署到实际应用中,无需编写任何代码。使用我们尖端的工具和用户友好的 [Ultralytics App](https://www.ultralytics.com/app-install) 将图像转化为可操作的见解。今天就**免费**开始您的旅程吧!
通过 [Ultralytics Platform](https://platform.ultralytics.com) ⭐ 体验无缝的 AI 开发,这是构建、训练和部署[计算机视觉](https://www.ultralytics.com/glossary/computer-vision-cv)模型的终极平台。可视化数据集,训练 [YOLOv5](https://docs.ultralytics.com/models/yolov5/) 和 [YOLOv8](https://docs.ultralytics.com/models/yolov8/) 🚀 模型,并将它们部署到实际应用中,无需编写任何代码。使用我们尖端的工具和用户友好的 [Ultralytics App](https://www.ultralytics.com/) 将图像转化为可操作的见解。今天就**免费**开始您的旅程吧!
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Expand Down Expand Up @@ -487,7 +487,7 @@ python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --inclu

Ultralytics 提供两种许可选项以满足不同需求:

- **AGPL-3.0 许可证**:一种 [OSI 批准的](https://opensource.org/license/agpl-v3)开源许可证,非常适合学术研究、个人项目和测试。它促进开放协作和知识共享。详情请参阅 [LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE) 文件。
- **AGPL-3.0 许可证**:一种 [OSI 批准的](https://opensource.org/license/agpl-3.0)开源许可证,非常适合学术研究、个人项目和测试。它促进开放协作和知识共享。详情请参阅 [LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE) 文件。
- **企业许可证**:专为商业应用量身定制,此许可证允许将 Ultralytics 软件和 AI 模型无缝集成到商业产品和服务中,绕过 AGPL-3.0 的开源要求。对于商业用例,请通过 [Ultralytics 授权许可](https://www.ultralytics.com/license)联系我们。

## 📧 联系
Expand All @@ -502,7 +502,7 @@ Ultralytics 提供两种许可选项以满足不同需求:
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2 changes: 1 addition & 1 deletion export.py
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Expand Up @@ -1048,7 +1048,7 @@ def add_tflite_metadata(file, metadata, num_outputs):
Notes:
TFLite metadata can include information such as model name, version, author, and other relevant details.
For more details on the structure of the metadata, refer to TensorFlow Lite
[metadata guidelines](https://ai.google.dev/edge/litert/models/metadata).
[metadata guidelines](https://ai.google.dev/edge/litert/conversion/tensorflow/metadata).
"""
with contextlib.suppress(ImportError):
# check_requirements('tflite_support')
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2 changes: 1 addition & 1 deletion utils/loggers/clearml/README.md
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Expand Up @@ -10,7 +10,7 @@

- **Experiment Management:** Effortlessly track every [YOLO training run](https://docs.ultralytics.com/modes/train/), including parameters, metrics, and outputs. Explore the [Ultralytics ClearML integration guide](https://docs.ultralytics.com/integrations/clearml/) for step-by-step instructions.
- **Data Versioning:** Manage and access your custom training data with ClearML's Data Versioning Tool, similar to [DVC integration](https://docs.ultralytics.com/integrations/dvc/).
- **Remote Execution:** [Remotely train and monitor models](https://docs.ultralytics.com/hub/cloud-training/) using ClearML Agent for seamless scaling.
- **Remote Execution:** [Remotely train and monitor models](https://docs.ultralytics.com/platform/) using ClearML Agent for seamless scaling.
- **Hyperparameter Optimization:** Boost your [mean average precision (mAP)](https://docs.ultralytics.com/guides/yolo-performance-metrics/) with ClearML's [hyperparameter tuning](https://docs.ultralytics.com/guides/hyperparameter-tuning/) capabilities.
- **Model Deployment:** Deploy your trained YOLO model as an API with ClearML Serving, complementing [Ultralytics model deployment options](https://docs.ultralytics.com/guides/model-deployment-options/).

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