Update to lowercase MkDocs admonitions (#15990)
Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
MatthewNoyce
GitHub
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133 changed files with 529 additions and 521 deletions
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@ -26,7 +26,7 @@ You can bring automation and efficiency to your machine learning workflow by imp
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To install the required packages, run:
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!!! Tip "Installation"
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!!! tip "Installation"
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=== "CLI"
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@ -43,7 +43,7 @@ Once you have installed the necessary packages, the next step is to initialize a
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Begin by initializing the ClearML SDK in your environment. The 'clearml-init' command starts the setup process and prompts you for the necessary credentials.
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!!! Tip "Initial SDK Setup"
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!!! tip "Initial SDK Setup"
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=== "CLI"
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@ -58,7 +58,7 @@ After executing this command, visit the [ClearML Settings page](https://app.clea
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Before diving into the usage instructions, be sure to check out the range of [YOLOv8 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
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!!! Example "Usage"
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!!! example "Usage"
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=== "Python"
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@ -26,7 +26,7 @@ By combining Ultralytics YOLOv8 with Comet ML, you unlock a range of benefits. T
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To install the required packages, run:
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!!! Tip "Installation"
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!!! tip "Installation"
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=== "CLI"
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@ -39,7 +39,7 @@ To install the required packages, run:
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After installing the required packages, you'll need to sign up, get a [Comet API Key](https://www.comet.com/signup), and configure it.
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!!! Tip "Configuring Comet ML"
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!!! tip "Configuring Comet ML"
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=== "CLI"
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@ -62,7 +62,7 @@ If you are using a Google Colab notebook, the code above will prompt you to ente
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Before diving into the usage instructions, be sure to check out the range of [YOLOv8 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
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!!! Example "Usage"
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!!! example "Usage"
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=== "Python"
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@ -60,7 +60,7 @@ Exporting YOLOv8 to CoreML enables optimized, on-device machine learning perform
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To install the required package, run:
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!!! Tip "Installation"
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!!! tip "Installation"
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=== "CLI"
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@ -75,7 +75,7 @@ For detailed instructions and best practices related to the installation process
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Before diving into the usage instructions, be sure to check out the range of [YOLOv8 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
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!!! Example "Usage"
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!!! example "Usage"
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=== "Python"
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@ -131,7 +131,7 @@ Also, if you'd like to know more about other Ultralytics YOLOv8 integrations, vi
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To export your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models to CoreML format, you'll first need to ensure you have the `ultralytics` package installed. You can install it using:
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!!! Example "Installation"
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!!! example "Installation"
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=== "CLI"
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@ -141,7 +141,7 @@ To export your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics)
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Next, you can export the model using the following Python or CLI commands:
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!!! Example "Usage"
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!!! example "Usage"
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=== "Python"
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@ -198,7 +198,7 @@ For more information on performance optimization, visit the [CoreML official doc
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Yes, you can run inference directly using the exported CoreML model. Below are the commands for Python and CLI:
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!!! Example "Running Inference"
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!!! example "Running Inference"
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=== "Python"
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@ -26,7 +26,7 @@ YOLOv8 training sessions can be effectively monitored with DVCLive. Additionally
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To install the required packages, run:
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!!! Tip "Installation"
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!!! tip "Installation"
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=== "CLI"
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@ -43,7 +43,7 @@ Once you have installed the necessary packages, the next step is to set up and c
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Begin by initializing a Git repository, as Git plays a crucial role in version control for both your code and DVCLive configurations.
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!!! Tip "Initial Environment Setup"
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!!! tip "Initial Environment Setup"
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=== "CLI"
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@ -176,7 +176,7 @@ Additionally, explore more integrations and capabilities of Ultralytics by visit
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Integrating DVCLive with Ultralytics YOLOv8 is straightforward. Start by installing the necessary packages:
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!!! Example "Installation"
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!!! example "Installation"
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=== "CLI"
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@ -186,7 +186,7 @@ Integrating DVCLive with Ultralytics YOLOv8 is straightforward. Start by install
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Next, initialize a Git repository and configure DVCLive in your project:
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!!! Example "Initial Environment Setup"
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!!! example "Initial Environment Setup"
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=== "CLI"
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@ -258,7 +258,7 @@ These steps ensure proper version control and setup for experiment tracking. For
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DVCLive offers powerful tools to visualize the results of YOLOv8 experiments. Here's how you can generate comparative plots:
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!!! Example "Generate Comparative Plots"
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!!! example "Generate Comparative Plots"
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=== "CLI"
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@ -50,7 +50,7 @@ You can expand model compatibility and deployment flexibility by converting YOLO
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To install the required package, run:
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!!! Tip "Installation"
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!!! tip "Installation"
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=== "CLI"
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@ -65,7 +65,7 @@ For detailed instructions and best practices related to the installation process
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Before diving into the usage instructions, it's important to note that while all [Ultralytics YOLOv8 models](../models/index.md) are available for exporting, you can ensure that the model you select supports export functionality [here](../modes/export.md).
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!!! Example "Usage"
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!!! example "Usage"
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=== "Python"
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@ -123,7 +123,7 @@ Also, for more information on other Ultralytics YOLOv8 integrations, please visi
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To export a YOLOv8 model to TFLite Edge TPU format, you can follow these steps:
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!!! Example "Usage"
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!!! example "Usage"
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=== "Python"
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@ -56,7 +56,7 @@ Once you do so, a notebook environment will open for you to load your data set.
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Next, you can install and import the necessary Python libraries.
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!!! Tip "Installation"
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!!! tip "Installation"
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=== "CLI"
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@ -71,7 +71,7 @@ For detailed instructions and best practices related to the installation process
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Then, you can import the needed packages.
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!!! Example "Import Relevant Libraries"
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!!! example "Import Relevant Libraries"
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=== "Python"
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@ -92,7 +92,7 @@ We can load the dataset directly into the notebook using the Kaggle API. First,
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Copy and paste your Kaggle username and API key into the following code. Then run the code to install the API and load the dataset into Watsonx.
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!!! Tip "Installation"
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!!! tip "Installation"
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=== "CLI"
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@ -103,7 +103,7 @@ Copy and paste your Kaggle username and API key into the following code. Then ru
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After installing Kaggle, we can load the dataset into Watsonx.
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!!! Example "Load the Data"
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!!! example "Load the Data"
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=== "Python"
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@ -155,7 +155,7 @@ But, YOLO models by default require separate images and labels in subdirectories
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To reorganize the data set directory, we can run the following script:
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!!! Example "Preprocess the Data"
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!!! example "Preprocess the Data"
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=== "Python"
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@ -207,7 +207,7 @@ names:
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Run the following script to delete the current contents of config.yaml and replace it with the above contents that reflect our new data set directory structure. Be certain to replace the work_dir portion of the root directory path in line 4 with your own working directory path we retrieved earlier. Leave the train, val, and test subdirectory definitions. Also, do not change {work_dir} in line 23 of the code.
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!!! Example "Edit the .yaml File"
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!!! example "Edit the .yaml File"
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=== "Python"
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@ -240,7 +240,7 @@ Run the following script to delete the current contents of config.yaml and repla
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Run the following command-line code to fine tune a pretrained default YOLOv8 model.
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!!! Example "Train the YOLOv8 model"
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!!! example "Train the YOLOv8 model"
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=== "CLI"
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@ -263,7 +263,7 @@ For a detailed understanding of the model training process and best practices, r
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We can now run inference to test the performance of our fine-tuned model:
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!!! Example "Test the YOLOv8 model"
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!!! example "Test the YOLOv8 model"
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=== "CLI"
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@ -279,7 +279,7 @@ The parameter `conf=0.5` informs the model to ignore all predictions with a conf
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Lastly, `iou=.5` directs the model to ignore boxes in the same class with an overlap of 50% or greater. It helps to reduce potential duplicate boxes generated for the same object.
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we can load the images with predicted bounding box overlays to view how our model performs on a handful of images.
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!!! Example "Display Predictions"
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!!! example "Display Predictions"
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=== "Python"
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@ -54,7 +54,7 @@ JupyterLab makes it easy to experiment with YOLOv8. To get started, follow these
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First, you need to install JupyterLab. Open your terminal and run the command:
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!!! Tip "Installation"
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!!! tip "Installation"
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=== "CLI"
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@ -71,7 +71,7 @@ Next, download the [tutorial.ipynb](https://github.com/ultralytics/ultralytics/b
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Navigate to the directory where you saved the notebook file using your terminal. Then, run the following command to launch JupyterLab:
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!!! Example "Usage"
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!!! example "Usage"
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=== "CLI"
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@ -34,7 +34,7 @@ pip install mlflow
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Make sure that MLflow logging is enabled in Ultralytics settings. Usually, this is controlled by the settings `mflow` key. See the [settings](../quickstart.md#ultralytics-settings) page for more info.
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!!! Example "Update Ultralytics MLflow Settings"
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!!! example "Update Ultralytics MLflow Settings"
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=== "Python"
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@ -130,7 +130,7 @@ pip install mlflow
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Next, enable MLflow logging in Ultralytics settings. This can be controlled using the `mlflow` key. For more information, see the [settings guide](../quickstart.md#ultralytics-settings).
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!!! Example "Update Ultralytics MLflow Settings"
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!!! example "Update Ultralytics MLflow Settings"
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=== "Python"
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@ -52,7 +52,7 @@ You can expand model compatibility and deployment flexibility by converting YOLO
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To install the required packages, run:
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!!! Tip "Installation"
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!!! tip "Installation"
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=== "CLI"
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@ -67,7 +67,7 @@ For detailed instructions and best practices related to the installation process
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Before diving into the usage instructions, it's important to note that while all [Ultralytics YOLOv8 models](../models/index.md) are available for exporting, you can ensure that the model you select supports export functionality [here](../modes/export.md).
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!!! Example "Usage"
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!!! example "Usage"
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=== "Python"
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@ -70,7 +70,7 @@ Deploying YOLOv8 with Neural Magic's DeepSparse involves a few straightforward s
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To install the required packages, run:
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!!! Tip "Installation"
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!!! tip "Installation"
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=== "CLI"
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@ -83,7 +83,7 @@ To install the required packages, run:
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DeepSparse Engine requires YOLOv8 models in ONNX format. Exporting your model to this format is essential for compatibility with DeepSparse. Use the following command to export YOLOv8 models:
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!!! Tip "Model Export"
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!!! tip "Model Export"
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=== "CLI"
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@ -98,7 +98,7 @@ This command will save the `yolov8n.onnx` model to your disk.
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With your YOLOv8 model in ONNX format, you can deploy and run inferences using DeepSparse. This can be done easily with their intuitive Python API:
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!!! Tip "Deploying and Running Inferences"
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!!! tip "Deploying and Running Inferences"
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=== "Python"
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@ -120,7 +120,7 @@ With your YOLOv8 model in ONNX format, you can deploy and run inferences using D
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It's important to check that your YOLOv8 model is performing optimally on DeepSparse. You can benchmark your model's performance to analyze throughput and latency:
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!!! Tip "Benchmarking"
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!!! tip "Benchmarking"
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=== "CLI"
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@ -133,7 +133,7 @@ It's important to check that your YOLOv8 model is performing optimally on DeepSp
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DeepSparse provides additional features for practical integration of YOLOv8 in applications, such as image annotation and dataset evaluation.
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!!! Tip "Additional Features"
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!!! tip "Additional Features"
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=== "CLI"
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@ -68,7 +68,7 @@ You can expand model compatibility and deployment flexibility by converting YOLO
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To install the required package, run:
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!!! Tip "Installation"
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!!! tip "Installation"
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=== "CLI"
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@ -83,7 +83,7 @@ For detailed instructions and best practices related to the installation process
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Before diving into the usage instructions, be sure to check out the range of [YOLOv8 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
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!!! Example "Usage"
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!!! example "Usage"
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=== "Python"
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@ -139,7 +139,7 @@ Also, if you'd like to know more about other Ultralytics YOLOv8 integrations, vi
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To export your YOLOv8 models to ONNX format using Ultralytics, follow these steps:
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!!! Example "Usage"
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!!! example "Usage"
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=== "Python"
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@ -27,7 +27,7 @@ OpenVINO, short for Open Visual Inference & Neural Network Optimization toolkit,
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Export a YOLOv8n model to OpenVINO format and run inference with the exported model.
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!!! Example
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!!! example
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=== "Python"
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@ -105,7 +105,7 @@ For more detailed steps and code snippets, refer to the [OpenVINO documentation]
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YOLOv8 benchmarks below were run by the Ultralytics team on 4 different model formats measuring speed and accuracy: PyTorch, TorchScript, ONNX and OpenVINO. Benchmarks were run on Intel Flex and Arc GPUs, and on Intel Xeon CPUs at FP32 precision (with the `half=False` argument).
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!!! Note
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!!! note
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The benchmarking results below are for reference and might vary based on the exact hardware and software configuration of a system, as well as the current workload of the system at the time the benchmarks are run.
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@ -255,7 +255,7 @@ Benchmarks below run on 13th Gen Intel® Core® i7-13700H CPU at FP32 precision.
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To reproduce the Ultralytics benchmarks above on all export [formats](../modes/export.md) run this code:
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!!! Example
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!!! example
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=== "Python"
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@ -294,7 +294,7 @@ For more detailed information and instructions on using OpenVINO, refer to the [
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Exporting YOLOv8 models to the OpenVINO format can significantly enhance CPU speed and enable GPU and NPU accelerations on Intel hardware. To export, you can use either Python or CLI as shown below:
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!!! Example
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!!! example
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=== "Python"
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@ -332,7 +332,7 @@ For detailed performance comparisons, visit our [benchmarks section](#openvino-y
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After exporting a YOLOv8 model to OpenVINO format, you can run inference using Python or CLI:
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!!! Example
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!!! example
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=== "Python"
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@ -369,7 +369,7 @@ For in-depth performance analysis, check our detailed [YOLOv8 benchmarks](#openv
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Yes, you can benchmark YOLOv8 models in various formats including PyTorch, TorchScript, ONNX, and OpenVINO. Use the following code snippet to run benchmarks on your chosen dataset:
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!!! Example
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!!! example
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=== "Python"
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@ -54,7 +54,7 @@ Converting YOLOv8 models to the PaddlePaddle format can improve execution flexib
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To install the required package, run:
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!!! Tip "Installation"
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!!! tip "Installation"
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=== "CLI"
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@ -69,7 +69,7 @@ For detailed instructions and best practices related to the installation process
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Before diving into the usage instructions, it's important to note that while all [Ultralytics YOLOv8 models](../models/index.md) are available for exporting, you can ensure that the model you select supports export functionality [here](../modes/export.md).
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!!! Example "Usage"
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!!! example "Usage"
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=== "Python"
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@ -127,7 +127,7 @@ Want to explore more ways to integrate your Ultralytics YOLOv8 models? Our [inte
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Exporting Ultralytics YOLOv8 models to PaddlePaddle format is straightforward. You can use the `export` method of the YOLO class to perform this exportation. Here is an example using Python:
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!!! Example "Usage"
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!!! example "Usage"
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=== "Python"
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@ -28,7 +28,7 @@ YOLOv8 also allows optional integration with [Weights & Biases](https://wandb.ai
|
|||
|
||||
To install the required packages, run:
|
||||
|
||||
!!! Tip "Installation"
|
||||
!!! tip "Installation"
|
||||
|
||||
=== "CLI"
|
||||
|
||||
|
|
@ -42,7 +42,7 @@ To install the required packages, run:
|
|||
|
||||
## Usage
|
||||
|
||||
!!! Example "Usage"
|
||||
!!! example "Usage"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
@ -103,7 +103,7 @@ The following table lists the default search space parameters for hyperparameter
|
|||
|
||||
In this example, we demonstrate how to use a custom search space for hyperparameter tuning with Ray Tune and YOLOv8. By providing a custom search space, you can focus the tuning process on specific hyperparameters of interest.
|
||||
|
||||
!!! Example "Usage"
|
||||
!!! example "Usage"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ keywords: Roboflow, YOLOv8, data labeling, computer vision, model training, mode
|
|||
|
||||
[Roboflow](https://roboflow.com/?ref=ultralytics) has everything you need to build and deploy computer vision models. Connect Roboflow at any step in your pipeline with APIs and SDKs, or use the end-to-end interface to automate the entire process from image to inference. Whether you're in need of [data labeling](https://roboflow.com/annotate?ref=ultralytics), [model training](https://roboflow.com/train?ref=ultralytics), or [model deployment](https://roboflow.com/deploy?ref=ultralytics), Roboflow gives you building blocks to bring custom computer vision solutions to your project.
|
||||
|
||||
!!! Question "Licensing"
|
||||
!!! question "Licensing"
|
||||
|
||||
Ultralytics offers two licensing options:
|
||||
|
||||
|
|
|
|||
|
|
@ -26,7 +26,7 @@ Using TensorBoard while training YOLOv8 models is straightforward and offers sig
|
|||
|
||||
To install the required package, run:
|
||||
|
||||
!!! Tip "Installation"
|
||||
!!! tip "Installation"
|
||||
|
||||
=== "CLI"
|
||||
|
||||
|
|
@ -43,7 +43,7 @@ For detailed instructions and best practices related to the installation process
|
|||
|
||||
When using Google Colab, it's important to set up TensorBoard before starting your training code:
|
||||
|
||||
!!! Example "Configure TensorBoard for Google Colab"
|
||||
!!! example "Configure TensorBoard for Google Colab"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
@ -56,7 +56,7 @@ When using Google Colab, it's important to set up TensorBoard before starting yo
|
|||
|
||||
Before diving into the usage instructions, be sure to check out the range of [YOLOv8 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
|
||||
|
||||
!!! Example "Usage"
|
||||
!!! example "Usage"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
@ -189,7 +189,7 @@ These visualizations are essential for tracking model performance and making nec
|
|||
|
||||
Yes, you can use TensorBoard in a Google Colab environment to train YOLOv8 models. Here's a quick setup:
|
||||
|
||||
!!! Example "Configure TensorBoard for Google Colab"
|
||||
!!! example "Configure TensorBoard for Google Colab"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
|
|||
|
|
@ -62,7 +62,7 @@ You can improve execution efficiency and optimize performance by converting YOLO
|
|||
|
||||
To install the required package, run:
|
||||
|
||||
!!! Tip "Installation"
|
||||
!!! tip "Installation"
|
||||
|
||||
=== "CLI"
|
||||
|
||||
|
|
@ -77,7 +77,7 @@ For detailed instructions and best practices related to the installation process
|
|||
|
||||
Before diving into the usage instructions, be sure to check out the range of [YOLOv8 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
|
||||
|
||||
!!! Example "Usage"
|
||||
!!! example "Usage"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
|
|||
|
|
@ -58,7 +58,7 @@ You can convert your YOLOv8 object detection model to the TF GraphDef format, wh
|
|||
|
||||
To install the required package, run:
|
||||
|
||||
!!! Tip "Installation"
|
||||
!!! tip "Installation"
|
||||
|
||||
=== "CLI"
|
||||
|
||||
|
|
@ -73,7 +73,7 @@ For detailed instructions and best practices related to the installation process
|
|||
|
||||
Before diving into the usage instructions, it's important to note that while all [Ultralytics YOLOv8 models](../models/index.md) are available for exporting, you can ensure that the model you select supports export functionality [here](../modes/export.md).
|
||||
|
||||
!!! Example "Usage"
|
||||
!!! example "Usage"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
@ -131,7 +131,7 @@ For more information on integrating Ultralytics YOLOv8 with other platforms and
|
|||
|
||||
Ultralytics YOLOv8 models can be exported to TensorFlow GraphDef (TF GraphDef) format seamlessly. This format provides a serialized, platform-independent representation of the model, ideal for deploying in varied environments like mobile and web. To export a YOLOv8 model to TF GraphDef, follow these steps:
|
||||
|
||||
!!! Example "Usage"
|
||||
!!! example "Usage"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
|
|||
|
|
@ -52,7 +52,7 @@ By exporting YOLOv8 models to the TF SavedModel format, you enhance their adapta
|
|||
|
||||
To install the required package, run:
|
||||
|
||||
!!! Tip "Installation"
|
||||
!!! tip "Installation"
|
||||
|
||||
=== "CLI"
|
||||
|
||||
|
|
@ -67,7 +67,7 @@ For detailed instructions and best practices related to the installation process
|
|||
|
||||
Before diving into the usage instructions, it's important to note that while all [Ultralytics YOLOv8 models](../models/index.md) are available for exporting, you can ensure that the model you select supports export functionality [here](../modes/export.md).
|
||||
|
||||
!!! Example "Usage"
|
||||
!!! example "Usage"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
@ -125,7 +125,7 @@ For more information on integrating Ultralytics YOLOv8 with other platforms and
|
|||
|
||||
Exporting an Ultralytics YOLO model to the TensorFlow SavedModel format is straightforward. You can use either Python or CLI to achieve this:
|
||||
|
||||
!!! Example "Exporting YOLOv8 to TF SavedModel"
|
||||
!!! example "Exporting YOLOv8 to TF SavedModel"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
|
|||
|
|
@ -50,7 +50,7 @@ You can expand model compatibility and deployment flexibility by converting YOLO
|
|||
|
||||
To install the required package, run:
|
||||
|
||||
!!! Tip "Installation"
|
||||
!!! tip "Installation"
|
||||
|
||||
=== "CLI"
|
||||
|
||||
|
|
@ -65,7 +65,7 @@ For detailed instructions and best practices related to the installation process
|
|||
|
||||
Before diving into the usage instructions, it's important to note that while all [Ultralytics YOLOv8 models](../models/index.md) are available for exporting, you can ensure that the model you select supports export functionality [here](../modes/export.md).
|
||||
|
||||
!!! Example "Usage"
|
||||
!!! example "Usage"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
@ -123,7 +123,7 @@ For more information on integrating Ultralytics YOLOv8 with other platforms and
|
|||
|
||||
Exporting Ultralytics YOLOv8 models to TensorFlow.js (TF.js) format is straightforward. You can follow these steps:
|
||||
|
||||
!!! Example "Usage"
|
||||
!!! example "Usage"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
|
|||
|
|
@ -56,7 +56,7 @@ You can improve on-device model execution efficiency and optimize performance by
|
|||
|
||||
To install the required packages, run:
|
||||
|
||||
!!! Tip "Installation"
|
||||
!!! tip "Installation"
|
||||
|
||||
=== "CLI"
|
||||
|
||||
|
|
@ -71,7 +71,7 @@ For detailed instructions and best practices related to the installation process
|
|||
|
||||
Before diving into the usage instructions, it's important to note that while all [Ultralytics YOLOv8 models](../models/index.md) are available for exporting, you can ensure that the model you select supports export functionality [here](../modes/export.md).
|
||||
|
||||
!!! Example "Usage"
|
||||
!!! example "Usage"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
|
|||
|
|
@ -60,7 +60,7 @@ Exporting YOLOv8 models to TorchScript makes it easier to use them in different
|
|||
|
||||
To install the required package, run:
|
||||
|
||||
!!! Tip "Installation"
|
||||
!!! tip "Installation"
|
||||
|
||||
=== "CLI"
|
||||
|
||||
|
|
@ -75,7 +75,7 @@ For detailed instructions and best practices related to the installation process
|
|||
|
||||
Before diving into the usage instructions, it's important to note that while all [Ultralytics YOLOv8 models](../models/index.md) are available for exporting, you can ensure that the model you select supports export functionality [here](../modes/export.md).
|
||||
|
||||
!!! Example "Usage"
|
||||
!!! example "Usage"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
@ -135,7 +135,7 @@ Exporting an Ultralytics YOLOv8 model to TorchScript allows for flexible, cross-
|
|||
|
||||
To export a YOLOv8 model to TorchScript, you can use the following example code:
|
||||
|
||||
!!! Example "Usage"
|
||||
!!! example "Usage"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
@ -182,7 +182,7 @@ For more insights into deployment, visit the [PyTorch Mobile Documentation](http
|
|||
|
||||
To install the required package for exporting YOLOv8 models, use the following command:
|
||||
|
||||
!!! Tip "Installation"
|
||||
!!! tip "Installation"
|
||||
|
||||
=== "CLI"
|
||||
|
||||
|
|
|
|||
|
|
@ -39,7 +39,7 @@ Want to let us know what you use for developing code? Head over to our Discourse
|
|||
|
||||
## Installing the Extension
|
||||
|
||||
!!! Note
|
||||
!!! note
|
||||
|
||||
Any code environment that will allow for installing VS Code extensions _should be_ compatible with the Ultralytics-snippets extension. After publishing the extension, it was discovered that [neovim](https://neovim.io/) can be made compatible with VS Code extensions. To learn more see the [`neovim` install section][neovim install] of the Readme in the [Ultralytics-Snippets repository][repo].
|
||||
|
||||
|
|
@ -127,7 +127,7 @@ These are the current snippet categories available to the Ultralytics-snippets e
|
|||
|
||||
The `ultra.examples` snippets are to useful for anyone looking to learn how to get started with the basics of working with Ultralytics YOLO. Example snippets are intended to run once inserted (some have dropdown options as well). An example of this is shown at the animation at the [top] of this page, where after the snippet is inserted, all code is selected and run interactively using <kbd>Shift ⇑</kbd>+<kbd>Enter ↵</kbd>.
|
||||
|
||||
!!! Example
|
||||
!!! example
|
||||
|
||||
Just like the animation shows at the [top] of this page, you can use the snippet `ultra.example-yolo-predict` to insert the following code example. Once inserted, the only configurable option is for the model scale which can be any one of: `n`, `s`, `m`, `l`, or `x`.
|
||||
|
||||
|
|
@ -146,7 +146,7 @@ The `ultra.examples` snippets are to useful for anyone looking to learn how to g
|
|||
|
||||
The aim for snippets other than the `ultra.examples` are for making development easier and quicker when working with Ultralytics. A common code block to be used in many projects, is to iterate the list of `Results` returned from using the model [predict] method. The `ultra.result-loop` snippet can help with this.
|
||||
|
||||
!!! Example
|
||||
!!! example
|
||||
|
||||
Using the `ultra.result-loop` will insert the following default code (including comments).
|
||||
|
||||
|
|
@ -170,7 +170,7 @@ However, since Ultralytics supports numerous [tasks], when [working with inferen
|
|||
|
||||
There are over 💯 keyword arguments for all of the various Ultralytics [tasks] and [modes]! That's a lot to remember and it can be easy to forget if the argument is `save_frame` or `save_frames` (it's definitely `save_frames` by the way). This is where the `ultra.kwargs` snippets can help out!
|
||||
|
||||
!!! Example
|
||||
!!! example
|
||||
|
||||
To insert the [predict] method, including all [inference arguments], use `ultra.kwargs-predict`, which will insert the following code (including comments).
|
||||
|
||||
|
|
|
|||
|
|
@ -37,7 +37,7 @@ You can use Weights & Biases to bring efficiency and automation to your YOLOv8 t
|
|||
|
||||
To install the required packages, run:
|
||||
|
||||
!!! Tip "Installation"
|
||||
!!! tip "Installation"
|
||||
|
||||
=== "CLI"
|
||||
|
||||
|
|
@ -54,7 +54,7 @@ After installing the necessary packages, the next step is to set up your Weights
|
|||
|
||||
Start by initializing the Weights & Biases environment in your workspace. You can do this by running the following command and following the prompted instructions.
|
||||
|
||||
!!! Tip "Initial SDK Setup"
|
||||
!!! tip "Initial SDK Setup"
|
||||
|
||||
=== "CLI"
|
||||
|
||||
|
|
@ -70,7 +70,7 @@ Navigate to the Weights & Biases authorization page to create and retrieve your
|
|||
|
||||
Before diving into the usage instructions for YOLOv8 model training with Weights & Biases, be sure to check out the range of [YOLOv8 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
|
||||
|
||||
!!! Example "Usage: Training YOLOv8 with Weights & Biases"
|
||||
!!! example "Usage: Training YOLOv8 with Weights & Biases"
|
||||
|
||||
=== "Python"
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue