Update Comet hyperlinks (#10500)
* Update README.md Signed-off-by: nerdyespresso <106761627+nerdyespresso@users.noreply.github.com> * Update README.md Signed-off-by: nerdyespresso <106761627+nerdyespresso@users.noreply.github.com> * Update README.md Signed-off-by: nerdyespresso <106761627+nerdyespresso@users.noreply.github.com> * Update README.md Signed-off-by: nerdyespresso <106761627+nerdyespresso@users.noreply.github.com> * Update tutorial.ipynb Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update tutorial.ipynb Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update tutorial.ipynb Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update README.md Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update README.md Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Signed-off-by: nerdyespresso <106761627+nerdyespresso@users.noreply.github.com> Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>pull/10502/head
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@ -264,7 +264,7 @@ python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml -
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|Roboflow|ClearML ⭐ NEW|Comet ⭐ NEW|Deci ⭐ NEW|
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|:-:|:-:|:-:|:-:|
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|Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics)|Automatically track, visualize and even remotely train YOLOv5 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!)|Free forever, [Comet](https://bit.ly/yolov5-readme-comet) lets you save YOLOv5 models, resume training, and interactively visualise and debug predictions|Automatically compile and quantize YOLOv5 for better inference performance in one click at [Deci](https://bit.ly/yolov5-deci-platform)|
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|Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics)|Automatically track, visualize and even remotely train YOLOv5 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!)|Free forever, [Comet](https://bit.ly/yolov5-readme-comet2) lets you save YOLOv5 models, resume training, and interactively visualise and debug predictions|Automatically compile and quantize YOLOv5 for better inference performance in one click at [Deci](https://bit.ly/yolov5-deci-platform)|
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## <div align="center">Ultralytics HUB</div>
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},
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"source": [
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"## Comet Logging and Visualization 🌟 NEW\n",
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"[Comet](https://bit.ly/yolov5-readme-comet) is now fully integrated with YOLOv5. Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://bit.ly/yolov5-colab-comet-panels)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes! \n",
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"[Comet](https://bit.ly/yolov5-readme-comet2) is now fully integrated with YOLOv5. Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://bit.ly/yolov5-colab-comet-panels)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes! \n",
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"\n",
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"Getting started is easy:\n",
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"```shell\n",
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},
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"nbformat": 4,
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},
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"source": [
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"## Comet Logging and Visualization 🌟 NEW\n",
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"[Comet](https://bit.ly/yolov5-readme-comet) is now fully integrated with YOLOv5. Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://bit.ly/yolov5-colab-comet-panels)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes! \n",
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"[Comet](https://bit.ly/yolov5-readme-comet2) is now fully integrated with YOLOv5. Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://bit.ly/yolov5-colab-comet-panels)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes! \n",
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"\n",
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"Getting started is easy:\n",
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"```shell\n",
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},
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"nbformat": 4,
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"nbformat_minor": 0
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"cell_type": "markdown",
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"source": [
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"## Comet Logging and Visualization 🌟 NEW\n",
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"[Comet](https://bit.ly/yolov5-readme-comet) is now fully integrated with YOLOv5. Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://bit.ly/yolov5-colab-comet-panels)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes! \n",
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"[Comet](https://bit.ly/yolov5-readme-comet2) is now fully integrated with YOLOv5. Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://bit.ly/yolov5-colab-comet-panels)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes! \n",
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"\n",
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"Getting started is easy:\n",
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"```shell\n",
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# YOLOv5 with Comet
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This guide will cover how to use YOLOv5 with [Comet](https://bit.ly/yolov5-readme-comet)
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This guide will cover how to use YOLOv5 with [Comet](https://bit.ly/yolov5-readme-comet2)
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# About Comet
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Comet builds tools that help data scientists, engineers, and team leaders accelerate and optimize machine learning and deep learning models.
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Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://bit.ly/yolov5-colab-comet-panels)!
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Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://www.comet.com/docs/v2/guides/comet-dashboard/code-panels/about-panels/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)!
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Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes!
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# Getting Started
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@ -54,7 +54,7 @@ That's it! Comet will automatically log your hyperparameters, command line argum
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<img width="1920" alt="yolo-ui" src="https://user-images.githubusercontent.com/26833433/202851203-164e94e1-2238-46dd-91f8-de020e9d6b41.png">
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# Try out an Example!
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Check out an example of a [completed run here](https://www.comet.com/examples/comet-example-yolov5/a0e29e0e9b984e4a822db2a62d0cb357?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step&ref=yolov5&utm_source=yolov5&utm_medium=affilliate&utm_campaign=yolov5_comet_integration)
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Check out an example of a [completed run here](https://www.comet.com/examples/comet-example-yolov5/a0e29e0e9b984e4a822db2a62d0cb357?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)
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Or better yet, try it out yourself in this Colab Notebook
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@ -119,7 +119,7 @@ You can control the frequency of logged predictions and the associated images by
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**Note:** The YOLOv5 validation dataloader will default to a batch size of 32, so you will have to set the logging frequency accordingly.
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Here is an [example project using the Panel](https://www.comet.com/examples/comet-example-yolov5?shareable=YcwMiJaZSXfcEXpGOHDD12vA1&ref=yolov5&utm_source=yolov5&utm_medium=affilliate&utm_campaign=yolov5_comet_integration)
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Here is an [example project using the Panel](https://www.comet.com/examples/comet-example-yolov5?shareable=YcwMiJaZSXfcEXpGOHDD12vA1&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)
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```shell
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@ -161,7 +161,7 @@ env COMET_LOG_PER_CLASS_METRICS=true python train.py \
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## Uploading a Dataset to Comet Artifacts
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If you would like to store your data using [Comet Artifacts](https://www.comet.com/docs/v2/guides/data-management/using-artifacts/#learn-more?ref=yolov5&utm_source=yolov5&utm_medium=affilliate&utm_campaign=yolov5_comet_integration), you can do so using the `upload_dataset` flag.
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If you would like to store your data using [Comet Artifacts](https://www.comet.com/docs/v2/guides/data-management/using-artifacts/#learn-more?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github), you can do so using the `upload_dataset` flag.
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The dataset be organized in the way described in the [YOLOv5 documentation](https://docs.ultralytics.com/tutorials/train-custom-datasets/#3-organize-directories). The dataset config `yaml` file must follow the same format as that of the `coco128.yaml` file.
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@ -251,6 +251,6 @@ comet optimizer -j <set number of workers> utils/loggers/comet/hpo.py \
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### Visualizing Results
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Comet provides a number of ways to visualize the results of your sweep. Take a look at a [project with a completed sweep here](https://www.comet.com/examples/comet-example-yolov5/view/PrlArHGuuhDTKC1UuBmTtOSXD/panels?ref=yolov5&utm_source=yolov5&utm_medium=affilliate&utm_campaign=yolov5_comet_integration)
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Comet provides a number of ways to visualize the results of your sweep. Take a look at a [project with a completed sweep here](https://www.comet.com/examples/comet-example-yolov5/view/PrlArHGuuhDTKC1UuBmTtOSXD/panels?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)
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<img width="1626" alt="hyperparameter-yolo" src="https://user-images.githubusercontent.com/7529846/186914869-7dc1de14-583f-4323-967b-c9a66a29e495.png">
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