diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 0dbea33b38..ba0e248f12 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -64,10 +64,10 @@ When asking a question, people will be better able to provide help if you provid - ✅ **Complete** – Provide **all** parts someone else needs to reproduce your problem in the question itself - ✅ **Reproducible** – Test the code you're about to provide to make sure it reproduces the problem -In addition to the above requirements, for [Ultralytics](https://ultralytics.com/) to provide assistance your code should be: +In addition to the above requirements, for [Ultralytics](https://www.ultralytics.com/) to provide assistance your code should be: - ✅ **Current** – Verify that your code is up-to-date with the current GitHub [master](https://github.com/ultralytics/yolov5/tree/master), and if necessary `git pull` or `git clone` a new copy to ensure your problem has not already been resolved by previous commits. -- ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this repository. [Ultralytics](https://ultralytics.com/) does not provide support for custom code ⚠️. +- ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this repository. [Ultralytics](https://www.ultralytics.com/) does not provide support for custom code ⚠️. If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the 🐛 **Bug Report** [template](https://github.com/ultralytics/yolov5/issues/new/choose) and provide a [minimum reproducible example](https://docs.ultralytics.com/help/minimum_reproducible_example/) to help us better understand and diagnose your problem. diff --git a/README.md b/README.md index 14bc041938..01bd24d056 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@

-[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/) +[中文](https://docs.ultralytics.com/zh) | [한국어](https://docs.ultralytics.com/ko) | [日本語](https://docs.ultralytics.com/ja) | [Русский](https://docs.ultralytics.com/ru) | [Deutsch](https://docs.ultralytics.com/de) | [Français](https://docs.ultralytics.com/fr) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt) | [Türkçe](https://docs.ultralytics.com/tr) | [Tiếng Việt](https://docs.ultralytics.com/vi) | [العربية](https://docs.ultralytics.com/ar)
YOLOv3 CI @@ -22,7 +22,7 @@ YOLOv3 🚀 is the world's most loved vision AI, representing Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions! -To request an Enterprise License please complete the form at [Ultralytics Licensing](https://ultralytics.com/license). +To request an Enterprise License please complete the form at [Ultralytics Licensing](https://www.ultralytics.com/license).
Ultralytics GitHub @@ -180,13 +180,13 @@ python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml -
-| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW | -| :--------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | -| Label and export your custom datasets directly to YOLOv3 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLOv3 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet2) lets you save YOLOv3 models, resume training, and interactively visualise and debug predictions | Run YOLOv3 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) | +| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW | +| :--------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | +| Label and export your custom datasets directly to YOLOv3 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLOv3 using [ClearML](https://clear.ml/) (open-source!) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet2) lets you save YOLOv3 models, resume training, and interactively visualise and debug predictions | Run YOLOv3 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) | ##
Ultralytics HUB
-Experience seamless AI with [Ultralytics HUB](https://ultralytics.com/hub) ⭐, the all-in-one solution for data visualization, YOLO 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendly [Ultralytics App](https://ultralytics.com/app_install). Start your journey for **Free** now! +Experience seamless AI with [Ultralytics HUB](https://www.ultralytics.com/hub) ⭐, the all-in-one solution for data visualization, YOLO 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendly [Ultralytics App](https://www.ultralytics.com/app-install). Start your journey for **Free** now! @@ -429,7 +429,7 @@ Get started in seconds with our verified environments. Click each icon below for ##
Contribute
-We love your input! We want to make contributing to YOLOv3 as easy and transparent as possible. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started, and fill out the [YOLOv3 Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experiences. Thank you to all our contributors! +We love your input! We want to make contributing to YOLOv3 as easy and transparent as possible. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started, and fill out the [YOLOv3 Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experiences. Thank you to all our contributors! @@ -440,12 +440,12 @@ We love your input! We want to make contributing to YOLOv3 as easy and transpare Ultralytics offers two licensing options to accommodate diverse use cases: -- **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/yolov3/blob/master/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). +- **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/license) open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/yolov3/blob/master/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://www.ultralytics.com/license). ##
Contact
-For YOLOv3 bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/yolov3/issues), and join our [Discord](https://ultralytics.com/discord) community for questions and discussions! +For YOLOv3 bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/yolov3/issues), and join our [Discord](https://discord.com/invite/ultralytics) community for questions and discussions!
diff --git a/README.zh-CN.md b/README.zh-CN.md index 1fbd24a74c..f8121080ec 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -4,7 +4,7 @@

-[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/) +[中文](https://docs.ultralytics.com/zh) | [한국어](https://docs.ultralytics.com/ko) | [日本語](https://docs.ultralytics.com/ja) | [Русский](https://docs.ultralytics.com/ru) | [Deutsch](https://docs.ultralytics.com/de) | [Français](https://docs.ultralytics.com/fr) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt) | [Türkçe](https://docs.ultralytics.com/tr) | [Tiếng Việt](https://docs.ultralytics.com/vi) | [العربية](https://docs.ultralytics.com/ar)
YOLOv3 CI @@ -22,7 +22,7 @@ YOLOv3 🚀 是世界上最受欢迎的视觉 AI，代表文档 了解详细信息，在 GitHub 上提交问题以获得支持，并加入我们的 Discord 社区进行问题和讨论！ -如需申请企业许可，请在 [Ultralytics Licensing](https://ultralytics.com/license) 处填写表格 +如需申请企业许可，请在 [Ultralytics Licensing](https://www.ultralytics.com/license) 处填写表格
@@ -180,13 +180,13 @@ python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml -
-| Roboflow | ClearML ⭐ 新 | Comet ⭐ 新 | Neural Magic ⭐ 新 | -| :--------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | -| 将您的自定义数据集进行标注并直接导出到 YOLOv3 以进行训练 [Roboflow](https://roboflow.com/?ref=ultralytics) | 自动跟踪、可视化甚至远程训练 YOLOv3 [ClearML](https://cutt.ly/yolov5-readme-clearml)（开源！） | 永远免费，[Comet](https://bit.ly/yolov5-readme-comet2)可让您保存 YOLOv3 模型、恢复训练以及交互式可视化和调试预测 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic)，运行 YOLOv3 推理的速度最高可提高6倍 | +| Roboflow | ClearML ⭐ 新 | Comet ⭐ 新 | Neural Magic ⭐ 新 | +| :--------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | +| 将您的自定义数据集进行标注并直接导出到 YOLOv3 以进行训练 [Roboflow](https://roboflow.com/?ref=ultralytics) | 自动跟踪、可视化甚至远程训练 YOLOv3 [ClearML](https://clear.ml/)（开源！） | 永远免费，[Comet](https://bit.ly/yolov5-readme-comet2)可让您保存 YOLOv3 模型、恢复训练以及交互式可视化和调试预测 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic)，运行 YOLOv3 推理的速度最高可提高6倍 | ##
Ultralytics HUB
-[Ultralytics HUB](https://ultralytics.com/hub) 是我们的⭐**新的**用于可视化数据集、训练 YOLOv3 🚀 模型并以无缝体验部署到现实世界的无代码解决方案。现在开始 **免费** 使用他！ +[Ultralytics HUB](https://www.ultralytics.com/hub) 是我们的⭐**新的**用于可视化数据集、训练 YOLOv3 🚀 模型并以无缝体验部署到现实世界的无代码解决方案。现在开始 **免费** 使用他！ @@ -430,7 +430,7 @@ python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --inclu ##
贡献
-我们喜欢您的意见或建议！我们希望尽可能简单和透明地为 YOLOv3 做出贡献。请看我们的 [投稿指南](https://docs.ultralytics.com/help/contributing/)，并填写 [YOLOv5调查](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) 向我们发送您的体验反馈。感谢我们所有的贡献者！ +我们喜欢您的意见或建议！我们希望尽可能简单和透明地为 YOLOv3 做出贡献。请看我们的 [投稿指南](https://docs.ultralytics.com/help/contributing/)，并填写 [YOLOv5调查](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) 向我们发送您的体验反馈。感谢我们所有的贡献者！ @@ -441,12 +441,12 @@ python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --inclu Ultralytics 提供两种许可证选项以适应各种使用场景： -- **AGPL-3.0 许可证**：这个[OSI 批准](https://opensource.org/licenses/)的开源许可证非常适合学生和爱好者，可以推动开放的协作和知识分享。请查看[LICENSE](https://github.com/ultralytics/yolov3/blob/master/LICENSE) 文件以了解更多细节。 -- **企业许可证**：专为商业用途设计，该许可证允许将 Ultralytics 的软件和 AI 模型无缝集成到商业产品和服务中，从而绕过 AGPL-3.0 的开源要求。如果您的场景涉及将我们的解决方案嵌入到商业产品中，请通过 [Ultralytics Licensing](https://ultralytics.com/license)与我们联系。 +- **AGPL-3.0 许可证**：这个[OSI 批准](https://opensource.org/license)的开源许可证非常适合学生和爱好者，可以推动开放的协作和知识分享。请查看[LICENSE](https://github.com/ultralytics/yolov3/blob/master/LICENSE) 文件以了解更多细节。 +- **企业许可证**：专为商业用途设计，该许可证允许将 Ultralytics 的软件和 AI 模型无缝集成到商业产品和服务中，从而绕过 AGPL-3.0 的开源要求。如果您的场景涉及将我们的解决方案嵌入到商业产品中，请通过 [Ultralytics Licensing](https://www.ultralytics.com/license)与我们联系。 ##
联系方式
-对于 Ultralytics 的错误报告和功能请求，请访问 [GitHub Issues](https://github.com/ultralytics/yolov3/issues)，并加入我们的 [Discord](https://ultralytics.com/discord) 社区进行问题和讨论！ +对于 Ultralytics 的错误报告和功能请求，请访问 [GitHub Issues](https://github.com/ultralytics/yolov3/issues)，并加入我们的 [Discord](https://discord.com/invite/ultralytics) 社区进行问题和讨论！
diff --git a/utils/flask_rest_api/README.md b/utils/flask_rest_api/README.md index b18a3011cf..d3ffaa2069 100644 --- a/utils/flask_rest_api/README.md +++ b/utils/flask_rest_api/README.md @@ -4,7 +4,7 @@ ## Requirements -[Flask](https://palletsprojects.com/p/flask/) is required. Install with: +[Flask](https://palletsprojects.com/projects/flask/) is required. Install with: ```shell $ pip install Flask diff --git a/utils/loggers/clearml/README.md b/utils/loggers/clearml/README.md index aff95d11a1..9ec33a39c6 100644 --- a/utils/loggers/clearml/README.md +++ b/utils/loggers/clearml/README.md @@ -4,7 +4,7 @@ ## About ClearML -[ClearML](https://cutt.ly/yolov5-tutorial-clearml) is an [open-source](https://github.com/allegroai/clearml) toolbox designed to save you time ⏱️. +[ClearML](https://clear.ml/) is an [open-source](https://github.com/allegroai/clearml) toolbox designed to save you time ⏱️. 🔨 Track every YOLOv5 training run in the experiment manager @@ -18,13 +18,13 @@ And so much more. It's up to you how many of these tools you want to use, you can stick to the experiment manager, or chain them all together into an impressive pipeline! -![ClearML scalars dashboard](https://github.com/thepycoder/clearml_screenshots/raw/main/experiment_manager_with_compare.gif) +![ClearML scalars dashboard](https://raw.githubusercontent.com/thepycoder/clearml_screenshots/main/experiment_manager_with_compare.gif) ## 🦾 Setting Things Up To keep track of your experiments and/or data, ClearML needs to communicate to a server. You have 2 options to get one: -Either sign up for free to the [ClearML Hosted Service](https://cutt.ly/yolov5-tutorial-clearml) or you can set up your own server, see [here](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server). Even the server is open-source, so even if you're dealing with sensitive data, you should be good to go! +Either sign up for free to the [ClearML Hosted Service](https://clear.ml/) or you can set up your own server, see [here](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server). Even the server is open-source, so even if you're dealing with sensitive data, you should be good to go! 1. Install the `clearml` python package: @@ -85,7 +85,7 @@ There even more we can do with all of this information, like hyperparameter opti Versioning your data separately from your code is generally a good idea and makes it easy to acquire the latest version too. This repository supports supplying a dataset version ID, and it will make sure to get the data if it's not there yet. Next to that, this workflow also saves the used dataset ID as part of the task parameters, so you will always know for sure which data was used in which experiment! -![ClearML Dataset Interface](https://github.com/thepycoder/clearml_screenshots/raw/main/clearml_data.gif) +![ClearML Dataset Interface](https://raw.githubusercontent.com/thepycoder/clearml_screenshots/main/clearml_data.gif) ### Prepare Your Dataset @@ -163,13 +163,13 @@ pip install optuna python utils/loggers/clearml/hpo.py ``` -![HPO](https://github.com/thepycoder/clearml_screenshots/raw/main/hpo.png) +![HPO](https://raw.githubusercontent.com/thepycoder/clearml_screenshots/main/hpo.png) ## 🤯 Remote Execution (advanced) Running HPO locally is really handy, but what if we want to run our experiments on a remote machine instead? Maybe you have access to a very powerful GPU machine on-site, or you have some budget to use cloud GPUs. This is where the ClearML Agent comes into play. Check out what the agent can do here: -- [YouTube video](https://youtu.be/MX3BrXnaULs) +- [YouTube video](https://www.youtube.com/watch?v=MX3BrXnaULs&feature=youtu.be) - [Documentation](https://clear.ml/docs/latest/docs/clearml_agent) In short: every experiment tracked by the experiment manager contains enough information to reproduce it on a different machine (installed packages, uncommitted changes etc.). So a ClearML agent does just that: it listens to a queue for incoming tasks and when it finds one, it recreates the environment and runs it while still reporting scalars, plots etc. to the experiment manager. @@ -190,7 +190,7 @@ With our agent running, we can give it some work. Remember from the HPO section ⏳ Enqueue the task to any of the queues by right-clicking it -![Enqueue a task from the UI](https://github.com/thepycoder/clearml_screenshots/raw/main/enqueue.gif) +![Enqueue a task from the UI](https://raw.githubusercontent.com/thepycoder/clearml_screenshots/main/enqueue.gif) ### Executing A Task Remotely