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docs/en/integrations/amazon-sagemaker.md
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@ -1,14 +1,14 @@
---
comments: true
description: Learn step-by-step how to deploy Ultralytics' YOLOv8 on Amazon SageMaker Endpoints, from setup to testing, for powerful real-time inference with AWS services.
keywords: YOLOv8, Amazon SageMaker, AWS, Ultralytics, machine learning, computer vision, model deployment, AWS CloudFormation, AWS CDK, real-time inference
description: Learn step-by-step how to deploy Ultralytics' YOLO11 on Amazon SageMaker Endpoints, from setup to testing, for powerful real-time inference with AWS services.
keywords: YOLO11, Amazon SageMaker, AWS, Ultralytics, machine learning, computer vision, model deployment, AWS CloudFormation, AWS CDK, real-time inference
---
# A Guide to Deploying YOLOv8 on Amazon SageMaker Endpoints
# A Guide to Deploying YOLO11 on Amazon SageMaker Endpoints
Deploying advanced [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models like [Ultralytics' YOLOv8](https://github.com/ultralytics/ultralytics) on Amazon SageMaker Endpoints opens up a wide range of possibilities for various [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) applications. The key to effectively using these models lies in understanding their setup, configuration, and deployment processes. YOLOv8 becomes even more powerful when integrated seamlessly with Amazon SageMaker, a robust and scalable machine learning service by AWS.
Deploying advanced [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models like [Ultralytics' YOLO11](https://github.com/ultralytics/ultralytics) on Amazon SageMaker Endpoints opens up a wide range of possibilities for various [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) applications. The key to effectively using these models lies in understanding their setup, configuration, and deployment processes. YOLO11 becomes even more powerful when integrated seamlessly with Amazon SageMaker, a robust and scalable machine learning service by AWS.
This guide will take you through the process of deploying YOLOv8 [PyTorch](https://www.ultralytics.com/glossary/pytorch) models on Amazon SageMaker Endpoints step by step. You'll learn the essentials of preparing your AWS environment, configuring the model appropriately, and using tools like AWS CloudFormation and the AWS Cloud Development Kit (CDK) for deployment.
This guide will take you through the process of deploying YOLO11 [PyTorch](https://www.ultralytics.com/glossary/pytorch) models on Amazon SageMaker Endpoints step by step. You'll learn the essentials of preparing your AWS environment, configuring the model appropriately, and using tools like AWS CloudFormation and the AWS Cloud Development Kit (CDK) for deployment.
## Amazon SageMaker
@ -18,9 +18,9 @@ This guide will take you through the process of deploying YOLOv8 [PyTorch](https
[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a machine learning service from Amazon Web Services (AWS) that simplifies the process of building, training, and deploying machine learning models. It provides a broad range of tools for handling various aspects of machine learning workflows. This includes automated features for tuning models, options for training models at scale, and straightforward methods for deploying models into production. SageMaker supports popular machine learning frameworks, offering the flexibility needed for diverse projects. Its features also cover data labeling, workflow management, and performance analysis.
## Deploying YOLOv8 on Amazon SageMaker Endpoints
## Deploying YOLO11 on Amazon SageMaker Endpoints
Deploying YOLOv8 on Amazon SageMaker lets you use its managed environment for real-time inference and take advantage of features like autoscaling. Take a look at the AWS architecture below.
Deploying YOLO11 on Amazon SageMaker lets you use its managed environment for real-time inference and take advantage of features like autoscaling. Take a look at the AWS architecture below.
<p align="center">
<img width="640" src="https://github.com/ultralytics/docs/releases/download/0/aws-architecture.avif" alt="AWS Architecture">
@ -40,9 +40,9 @@ First, ensure you have the following prerequisites in place:
- Adequate Service Quota: Confirm that you have sufficient quotas for two separate resources in Amazon SageMaker: one for `ml.m5.4xlarge` for endpoint usage and another for `ml.m5.4xlarge` for notebook instance usage. Each of these requires a minimum of one quota value. If your current quotas are below this requirement, it's important to request an increase for each. You can request a quota increase by following the detailed instructions in the [AWS Service Quotas documentation](https://docs.aws.amazon.com/servicequotas/latest/userguide/request-quota-increase.html#quota-console-increase).
### Step 2: Clone the YOLOv8 SageMaker Repository
### Step 2: Clone the YOLO11 SageMaker Repository
The next step is to clone the specific AWS repository that contains the resources for deploying YOLOv8 on SageMaker. This repository, hosted on GitHub, includes the necessary CDK scripts and configuration files.
The next step is to clone the specific AWS repository that contains the resources for deploying YOLO11 on SageMaker. This repository, hosted on GitHub, includes the necessary CDK scripts and configuration files.
- Clone the GitHub Repository: Execute the following command in your terminal to clone the host-yolov8-on-sagemaker-endpoint repository:
@ -104,11 +104,11 @@ cdk bootstrap
cdk deploy
```
### Step 5: Deploy the YOLOv8 Model
### Step 5: Deploy the YOLO Model
Before diving into the deployment 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.
Before diving into the deployment instructions, be sure to check out the range of [YOLO11 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
After creating the AWS CloudFormation Stack, the next step is to deploy YOLOv8.
After creating the AWS CloudFormation Stack, the next step is to deploy YOLO11.
- Open the Notebook Instance: Go to the AWS Console and navigate to the Amazon SageMaker service. Select "Notebook Instances" from the dashboard, then locate the notebook instance that was created by your CDK deployment script. Open the notebook instance to access the Jupyter environment.
@ -136,18 +136,18 @@ def output_fn(prediction_output):
return json.dumps(infer)
```
- Deploy the Endpoint Using 1_DeployEndpoint.ipynb: In the Jupyter environment, open the 1_DeployEndpoint.ipynb notebook located in the sm-notebook directory. Follow the instructions in the notebook and run the cells to download the YOLOv8 model, package it with the updated inference code, and upload it to an Amazon S3 bucket. The notebook will guide you through creating and deploying a SageMaker endpoint for the YOLOv8 model.
- Deploy the Endpoint Using 1_DeployEndpoint.ipynb: In the Jupyter environment, open the 1_DeployEndpoint.ipynb notebook located in the sm-notebook directory. Follow the instructions in the notebook and run the cells to download the YOLO11 model, package it with the updated inference code, and upload it to an Amazon S3 bucket. The notebook will guide you through creating and deploying a SageMaker endpoint for the YOLO11 model.
### Step 6: Testing Your Deployment
Now that your YOLOv8 model is deployed, it's important to test its performance and functionality.
Now that your YOLO11 model is deployed, it's important to test its performance and functionality.
- Open the Test Notebook: In the same Jupyter environment, locate and open the 2_TestEndpoint.ipynb notebook, also in the sm-notebook directory.
- Run the Test Notebook: Follow the instructions within the notebook to test the deployed SageMaker endpoint. This includes sending an image to the endpoint and running inferences. Then, you'll plot the output to visualize the model's performance and [accuracy](https://www.ultralytics.com/glossary/accuracy), as shown below.
<p align="center">
<img width="640" src="https://github.com/ultralytics/docs/releases/download/0/testing-results-yolov8.avif" alt="Testing Results YOLOv8">
<img width="640" src="https://github.com/ultralytics/docs/releases/download/0/testing-results-yolov8.avif" alt="Testing Results YOLO11">
</p>
- Clean-Up Resources: The test notebook will also guide you through the process of cleaning up the endpoint and the hosted model. This is an important step to manage costs and resources effectively, especially if you do not plan to use the deployed model immediately.
@ -160,24 +160,24 @@ After testing, continuous monitoring and management of your deployed model are e
- Manage the Endpoint: Use the SageMaker console for ongoing management of the endpoint. This includes scaling, updating, or redeploying the model as required.
By completing these steps, you will have successfully deployed and tested a YOLOv8 model on Amazon SageMaker Endpoints. This process not only equips you with practical experience in using AWS services for machine learning deployment but also lays the foundation for deploying other advanced models in the future.
By completing these steps, you will have successfully deployed and tested a YOLO11 model on Amazon SageMaker Endpoints. This process not only equips you with practical experience in using AWS services for machine learning deployment but also lays the foundation for deploying other advanced models in the future.
## Summary
This guide took you step by step through deploying YOLOv8 on Amazon SageMaker Endpoints using AWS CloudFormation and the AWS Cloud Development Kit (CDK). The process includes cloning the necessary GitHub repository, setting up the CDK environment, deploying the model using AWS services, and testing its performance on SageMaker.
This guide took you step by step through deploying YOLO11 on Amazon SageMaker Endpoints using AWS CloudFormation and the AWS Cloud Development Kit (CDK). The process includes cloning the necessary GitHub repository, setting up the CDK environment, deploying the model using AWS services, and testing its performance on SageMaker.
For more technical details, refer to [this article](https://aws.amazon.com/blogs/machine-learning/hosting-yolov8-pytorch-model-on-amazon-sagemaker-endpoints/) on the AWS Machine Learning Blog. You can also check out the official [Amazon SageMaker Documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/realtime-endpoints.html) for more insights into various features and functionalities.
Are you interested in learning more about different YOLOv8 integrations? Visit the [Ultralytics integrations guide page](../integrations/index.md) to discover additional tools and capabilities that can enhance your machine-learning projects.
Are you interested in learning more about different YOLO11 integrations? Visit the [Ultralytics integrations guide page](../integrations/index.md) to discover additional tools and capabilities that can enhance your machine-learning projects.
## FAQ
### How do I deploy the Ultralytics YOLOv8 model on Amazon SageMaker Endpoints?
### How do I deploy the Ultralytics YOLO11 model on Amazon SageMaker Endpoints?
To deploy the Ultralytics YOLOv8 model on Amazon SageMaker Endpoints, follow these steps:
To deploy the Ultralytics YOLO11 model on Amazon SageMaker Endpoints, follow these steps:
1. **Set Up Your AWS Environment**: Ensure you have an AWS Account, IAM roles with necessary permissions, and the AWS CLI configured. Install AWS CDK if not already done (refer to the [AWS CDK instructions](https://docs.aws.amazon.com/cdk/v2/guide/getting_started.html#getting_started_install)).
2. **Clone the YOLOv8 SageMaker Repository**:
2. **Clone the YOLO11 SageMaker Repository**:
```bash
git clone https://github.com/aws-samples/host-yolov8-on-sagemaker-endpoint.git
cd host-yolov8-on-sagemaker-endpoint/yolov8-pytorch-cdk
@ -196,11 +196,11 @@ To deploy the Ultralytics YOLOv8 model on Amazon SageMaker Endpoints, follow the
cdk deploy
```
For further details, review the [documentation section](#step-5-deploy-the-yolov8-model).
For further details, review the [documentation section](#step-5-deploy-the-yolo-model).
### What are the prerequisites for deploying YOLOv8 on Amazon SageMaker?
### What are the prerequisites for deploying YOLO11 on Amazon SageMaker?
To deploy YOLOv8 on Amazon SageMaker, ensure you have the following prerequisites:
To deploy YOLO11 on Amazon SageMaker, ensure you have the following prerequisites:
1. **AWS Account**: Active AWS account ([sign up here](https://aws.amazon.com/)).
2. **IAM Roles**: Configured IAM roles with permissions for SageMaker, CloudFormation, and Amazon S3.
@ -210,9 +210,9 @@ To deploy YOLOv8 on Amazon SageMaker, ensure you have the following prerequisite
For detailed setup, refer to [this section](#step-1-setup-your-aws-environment).
### Why should I use Ultralytics YOLOv8 on Amazon SageMaker?
### Why should I use Ultralytics YOLO11 on Amazon SageMaker?
Using Ultralytics YOLOv8 on Amazon SageMaker offers several advantages:
Using Ultralytics YOLO11 on Amazon SageMaker offers several advantages:
1. **Scalability and Management**: SageMaker provides a managed environment with features like autoscaling, which helps in real-time inference needs.
2. **Integration with AWS Services**: Seamlessly integrate with other AWS services, such as S3 for data storage, CloudFormation for infrastructure as code, and CloudWatch for monitoring.
@ -221,9 +221,9 @@ Using Ultralytics YOLOv8 on Amazon SageMaker offers several advantages:
Explore more about the advantages of using SageMaker in the [introduction section](#amazon-sagemaker).
### Can I customize the inference logic for YOLOv8 on Amazon SageMaker?
### Can I customize the inference logic for YOLO11 on Amazon SageMaker?
Yes, you can customize the inference logic for YOLOv8 on Amazon SageMaker:
Yes, you can customize the inference logic for YOLO11 on Amazon SageMaker:
1. **Modify `inference.py`**: Locate and customize the `output_fn` function in the `inference.py` file to tailor output formats.
@ -243,11 +243,11 @@ Yes, you can customize the inference logic for YOLOv8 on Amazon SageMaker:
2. **Deploy Updated Model**: Ensure you redeploy the model using Jupyter notebooks provided (`1_DeployEndpoint.ipynb`) to include these changes.
Refer to the [detailed steps](#step-5-deploy-the-yolov8-model) for deploying the modified model.
Refer to the [detailed steps](#step-5-deploy-the-yolo-model) for deploying the modified model.
### How can I test the deployed YOLOv8 model on Amazon SageMaker?
### How can I test the deployed YOLO11 model on Amazon SageMaker?
To test the deployed YOLOv8 model on Amazon SageMaker:
To test the deployed YOLO11 model on Amazon SageMaker:
1. **Open the Test Notebook**: Locate the `2_TestEndpoint.ipynb` notebook in the SageMaker Jupyter environment.
2. **Run the Notebook**: Follow the notebook's instructions to send an image to the endpoint, perform inference, and display results.

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@ -1,14 +1,14 @@
---
comments: true
description: Discover how to integrate YOLOv8 with ClearML to streamline your MLOps workflow, automate experiments, and enhance model management effortlessly.
keywords: YOLOv8, ClearML, MLOps, Ultralytics, machine learning, object detection, model training, automation, experiment management
description: Discover how to integrate YOLO11 with ClearML to streamline your MLOps workflow, automate experiments, and enhance model management effortlessly.
keywords: YOLO11, ClearML, MLOps, Ultralytics, machine learning, object detection, model training, automation, experiment management
---
# Training YOLOv8 with ClearML: Streamlining Your MLOps Workflow
# Training YOLO11 with ClearML: Streamlining Your MLOps Workflow
MLOps bridges the gap between creating and deploying [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) models in real-world settings. It focuses on efficient deployment, scalability, and ongoing management to ensure models perform well in practical applications.
[Ultralytics YOLOv8](https://www.ultralytics.com/) effortlessly integrates with ClearML, streamlining and enhancing your [object detection](https://www.ultralytics.com/glossary/object-detection) model's training and management. This guide will walk you through the integration process, detailing how to set up ClearML, manage experiments, automate model management, and collaborate effectively.
[Ultralytics YOLO11](https://www.ultralytics.com/) effortlessly integrates with ClearML, streamlining and enhancing your [object detection](https://www.ultralytics.com/glossary/object-detection) model's training and management. This guide will walk you through the integration process, detailing how to set up ClearML, manage experiments, automate model management, and collaborate effectively.
## ClearML
@ -18,9 +18,9 @@ MLOps bridges the gap between creating and deploying [machine learning](https://
[ClearML](https://clear.ml/) is an innovative open-source MLOps platform that is skillfully designed to automate, monitor, and orchestrate machine learning workflows. Its key features include automated logging of all training and inference data for full experiment reproducibility, an intuitive web UI for easy [data visualization](https://www.ultralytics.com/glossary/data-visualization) and analysis, advanced hyperparameter [optimization algorithms](https://www.ultralytics.com/glossary/optimization-algorithm), and robust model management for efficient deployment across various platforms.
## YOLOv8 Training with ClearML
## YOLO11 Training with ClearML
You can bring automation and efficiency to your machine learning workflow by improving your training process by integrating YOLOv8 with ClearML.
You can bring automation and efficiency to your machine learning workflow by improving your training process by integrating YOLO11 with ClearML.
## Installation
@ -31,11 +31,11 @@ To install the required packages, run:
=== "CLI"
```bash
# Install the required packages for YOLOv8 and ClearML
# Install the required packages for YOLO11 and ClearML
pip install ultralytics clearml
```
For detailed instructions and best practices related to the installation process, be sure to check our [YOLOv8 Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
For detailed instructions and best practices related to the installation process, be sure to check our [YOLO11 Installation guide](../quickstart.md). While installing the required packages for YOLO11, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
## Configuring ClearML
@ -56,7 +56,7 @@ After executing this command, visit the [ClearML Settings page](https://app.clea
## Usage
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.
Before diving into the usage instructions, be sure to check out the range of [YOLO11 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
!!! example "Usage"
@ -70,11 +70,11 @@ Before diving into the usage instructions, be sure to check out the range of [YO
# Step 1: Creating a ClearML Task
task = Task.init(project_name="my_project", task_name="my_yolov8_task")
# Step 2: Selecting the YOLOv8 Model
model_variant = "yolov8n"
# Step 2: Selecting the YOLO11 Model
model_variant = "yolo11n"
task.set_parameter("model_variant", model_variant)
# Step 3: Loading the YOLOv8 Model
# Step 3: Loading the YOLO11 Model
model = YOLO(f"{model_variant}.pt")
# Step 4: Setting Up Training Arguments
@ -91,11 +91,11 @@ Let's understand the steps showcased in the usage code snippet above.
**Step 1: Creating a ClearML Task**: A new task is initialized in ClearML, specifying your project and task names. This task will track and manage your model's training.
**Step 2: Selecting the YOLOv8 Model**: The `model_variant` variable is set to 'yolov8n', one of the YOLOv8 models. This variant is then logged in ClearML for tracking.
**Step 2: Selecting the YOLO11 Model**: The `model_variant` variable is set to 'yolo11n', one of the YOLO11 models. This variant is then logged in ClearML for tracking.
**Step 3: Loading the YOLOv8 Model**: The selected YOLOv8 model is loaded using Ultralytics' YOLO class, preparing it for training.
**Step 3: Loading the YOLO11 Model**: The selected YOLO11 model is loaded using Ultralytics' YOLO class, preparing it for training.
**Step 4: Setting Up Training Arguments**: Key training arguments like the dataset (`coco8.yaml`) and the number of [epochs](https://www.ultralytics.com/glossary/epoch) (`16`) are organized in a dictionary and connected to the ClearML task. This allows for tracking and potential modification via the ClearML UI. For a detailed understanding of the model training process and best practices, refer to our [YOLOv8 Model Training guide](../modes/train.md).
**Step 4: Setting Up Training Arguments**: Key training arguments like the dataset (`coco8.yaml`) and the number of [epochs](https://www.ultralytics.com/glossary/epoch) (`16`) are organized in a dictionary and connected to the ClearML task. This allows for tracking and potential modification via the ClearML UI. For a detailed understanding of the model training process and best practices, refer to our [YOLO11 Model Training guide](../modes/train.md).
**Step 5: Initiating Model Training**: The model training is started with the specified arguments. The results of the training process are captured in the `results` variable.
@ -106,7 +106,7 @@ Upon running the usage code snippet above, you can expect the following output:
- A confirmation message indicating the creation of a new ClearML task, along with its unique ID.
- An informational message about the script code being stored, indicating that the code execution is being tracked by ClearML.
- A URL link to the ClearML results page where you can monitor the training progress and view detailed logs.
- Download progress for the YOLOv8 model and the specified dataset, followed by a summary of the model architecture and training configuration.
- Download progress for the YOLO11 model and the specified dataset, followed by a summary of the model architecture and training configuration.
- Initialization messages for various training components like TensorBoard, Automatic [Mixed Precision](https://www.ultralytics.com/glossary/mixed-precision) (AMP), and dataset preparation.
- Finally, the training process starts, with progress updates as the model trains on the specified dataset. For an in-depth understanding of the performance metrics used during training, read [our guide on performance metrics](../guides/yolo-performance-metrics.md).
@ -151,7 +151,7 @@ For a visual walkthrough of what the ClearML Results Page looks like, watch the
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> YOLOv8 MLOps Integration using ClearML
<strong>Watch:</strong> YOLO11 MLOps Integration using ClearML
</p>
### Advanced Features in ClearML
@ -180,7 +180,7 @@ ClearML's user-friendly interface allows easy cloning, editing, and enqueuing of
## Summary
This guide has led you through the process of integrating ClearML with Ultralytics' YOLOv8. Covering everything from initial setup to advanced model management, you've discovered how to leverage ClearML for efficient training, experiment tracking, and workflow optimization in your machine learning projects.
This guide has led you through the process of integrating ClearML with Ultralytics' YOLO11. Covering everything from initial setup to advanced model management, you've discovered how to leverage ClearML for efficient training, experiment tracking, and workflow optimization in your machine learning projects.
For further details on usage, visit [ClearML's official documentation](https://clear.ml/docs/latest/docs/integrations/yolov8/).
@ -188,9 +188,9 @@ Additionally, explore more integrations and capabilities of Ultralytics by visit
## FAQ
### What is the process for integrating Ultralytics YOLOv8 with ClearML?
### What is the process for integrating Ultralytics YOLO11 with ClearML?
Integrating Ultralytics YOLOv8 with ClearML involves a series of steps to streamline your MLOps workflow. First, install the necessary packages:
Integrating Ultralytics YOLO11 with ClearML involves a series of steps to streamline your MLOps workflow. First, install the necessary packages:
```bash
pip install ultralytics clearml
@ -202,19 +202,19 @@ Next, initialize the ClearML SDK in your environment using:
clearml-init
```
You then configure ClearML with your credentials from the [ClearML Settings page](https://app.clear.ml/settings/workspace-configuration). Detailed instructions on the entire setup process, including model selection and training configurations, can be found in our [YOLOv8 Model Training guide](../modes/train.md).
You then configure ClearML with your credentials from the [ClearML Settings page](https://app.clear.ml/settings/workspace-configuration). Detailed instructions on the entire setup process, including model selection and training configurations, can be found in our [YOLO11 Model Training guide](../modes/train.md).
### Why should I use ClearML with Ultralytics YOLOv8 for my machine learning projects?
### Why should I use ClearML with Ultralytics YOLO11 for my machine learning projects?
Using ClearML with Ultralytics YOLOv8 enhances your machine learning projects by automating experiment tracking, streamlining workflows, and enabling robust model management. ClearML offers real-time metrics tracking, resource utilization monitoring, and a user-friendly interface for comparing experiments. These features help optimize your model's performance and make the development process more efficient. Learn more about the benefits and procedures in our [MLOps Integration guide](../modes/train.md).
Using ClearML with Ultralytics YOLO11 enhances your machine learning projects by automating experiment tracking, streamlining workflows, and enabling robust model management. ClearML offers real-time metrics tracking, resource utilization monitoring, and a user-friendly interface for comparing experiments. These features help optimize your model's performance and make the development process more efficient. Learn more about the benefits and procedures in our [MLOps Integration guide](../modes/train.md).
### How do I troubleshoot common issues during YOLOv8 and ClearML integration?
### How do I troubleshoot common issues during YOLO11 and ClearML integration?
If you encounter issues during the integration of YOLOv8 with ClearML, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips. Typical problems might involve package installation errors, credential setup, or configuration issues. This guide provides step-by-step troubleshooting instructions to resolve these common issues efficiently.
If you encounter issues during the integration of YOLO11 with ClearML, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips. Typical problems might involve package installation errors, credential setup, or configuration issues. This guide provides step-by-step troubleshooting instructions to resolve these common issues efficiently.
### How do I set up the ClearML task for YOLOv8 model training?
### How do I set up the ClearML task for YOLO11 model training?
Setting up a ClearML task for YOLOv8 training involves initializing a task, selecting the model variant, loading the model, setting up training arguments, and finally, starting the model training. Here's a simplified example:
Setting up a ClearML task for YOLO11 training involves initializing a task, selecting the model variant, loading the model, setting up training arguments, and finally, starting the model training. Here's a simplified example:
```python
from clearml import Task
@ -224,11 +224,11 @@ from ultralytics import YOLO
# Step 1: Creating a ClearML Task
task = Task.init(project_name="my_project", task_name="my_yolov8_task")
# Step 2: Selecting the YOLOv8 Model
model_variant = "yolov8n"
# Step 2: Selecting the YOLO11 Model
model_variant = "yolo11n"
task.set_parameter("model_variant", model_variant)
# Step 3: Loading the YOLOv8 Model
# Step 3: Loading the YOLO11 Model
model = YOLO(f"{model_variant}.pt")
# Step 4: Setting Up Training Arguments
@ -241,6 +241,6 @@ results = model.train(**args)
Refer to our [Usage guide](#usage) for a detailed breakdown of these steps.
### Where can I view the results of my YOLOv8 training in ClearML?
### Where can I view the results of my YOLO11 training in ClearML?
After running your YOLOv8 training script with ClearML, you can view the results on the ClearML results page. The output will include a URL link to the ClearML dashboard, where you can track metrics, compare experiments, and monitor resource usage. For more details on how to view and interpret the results, check our section on [Viewing the ClearML Results Page](#viewing-the-clearml-results-page).
After running your YOLO11 training script with ClearML, you can view the results on the ClearML results page. The output will include a URL link to the ClearML dashboard, where you can track metrics, compare experiments, and monitor resource usage. For more details on how to view and interpret the results, check our section on [Viewing the ClearML Results Page](#viewing-the-clearml-results-page).

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@ -1,14 +1,14 @@
---
comments: true
description: Learn to simplify the logging of YOLOv8 training with Comet ML. This guide covers installation, setup, real-time insights, and custom logging.
keywords: YOLOv8, Comet ML, logging, machine learning, training, model checkpoints, metrics, installation, configuration, real-time insights, custom logging
description: Learn to simplify the logging of YOLO11 training with Comet ML. This guide covers installation, setup, real-time insights, and custom logging.
keywords: YOLO11, Comet ML, logging, machine learning, training, model checkpoints, metrics, installation, configuration, real-time insights, custom logging
---
# Elevating YOLOv8 Training: Simplify Your Logging Process with Comet ML
# Elevating YOLO11 Training: Simplify Your Logging Process with Comet ML
Logging key training details such as parameters, metrics, image predictions, and model checkpoints is essential in [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml)—it keeps your project transparent, your progress measurable, and your results repeatable.
[Ultralytics YOLOv8](https://www.ultralytics.com/) seamlessly integrates with Comet ML, efficiently capturing and optimizing every aspect of your YOLOv8 [object detection](https://www.ultralytics.com/glossary/object-detection) model's training process. In this guide, we'll cover the installation process, Comet ML setup, real-time insights, custom logging, and offline usage, ensuring that your YOLOv8 training is thoroughly documented and fine-tuned for outstanding results.
[Ultralytics YOLO11](https://www.ultralytics.com/) seamlessly integrates with Comet ML, efficiently capturing and optimizing every aspect of your YOLO11 [object detection](https://www.ultralytics.com/glossary/object-detection) model's training process. In this guide, we'll cover the installation process, Comet ML setup, real-time insights, custom logging, and offline usage, ensuring that your YOLO11 training is thoroughly documented and fine-tuned for outstanding results.
## Comet ML
@ -18,9 +18,9 @@ Logging key training details such as parameters, metrics, image predictions, and
[Comet ML](https://www.comet.com/site/) is a platform for tracking, comparing, explaining, and optimizing machine learning models and experiments. It allows you to log metrics, parameters, media, and more during your model training and monitor your experiments through an aesthetically pleasing web interface. Comet ML helps data scientists iterate more rapidly, enhances transparency and reproducibility, and aids in the development of production models.
## Harnessing the Power of YOLOv8 and Comet ML
## Harnessing the Power of YOLO11 and Comet ML
By combining Ultralytics YOLOv8 with Comet ML, you unlock a range of benefits. These include simplified experiment management, real-time insights for quick adjustments, flexible and tailored logging options, and the ability to log experiments offline when internet access is limited. This integration empowers you to make data-driven decisions, analyze performance metrics, and achieve exceptional results.
By combining Ultralytics YOLO11 with Comet ML, you unlock a range of benefits. These include simplified experiment management, real-time insights for quick adjustments, flexible and tailored logging options, and the ability to log experiments offline when internet access is limited. This integration empowers you to make data-driven decisions, analyze performance metrics, and achieve exceptional results.
## Installation
@ -31,7 +31,7 @@ To install the required packages, run:
=== "CLI"
```bash
# Install the required packages for YOLOv8 and Comet ML
# Install the required packages for YOLO11 and Comet ML
pip install ultralytics comet_ml torch torchvision
```
@ -60,7 +60,7 @@ If you are using a Google Colab notebook, the code above will prompt you to ente
## Usage
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.
Before diving into the usage instructions, be sure to check out the range of [YOLO11 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
!!! example "Usage"
@ -70,7 +70,7 @@ Before diving into the usage instructions, be sure to check out the range of [YO
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
# Train the model
results = model.train(
@ -83,13 +83,13 @@ Before diving into the usage instructions, be sure to check out the range of [YO
)
```
After running the training code, Comet ML will create an experiment in your Comet workspace to track the run automatically. You will then be provided with a link to view the detailed logging of your [YOLOv8 model's training](../modes/train.md) process.
After running the training code, Comet ML will create an experiment in your Comet workspace to track the run automatically. You will then be provided with a link to view the detailed logging of your [YOLO11 model's training](../modes/train.md) process.
Comet automatically logs the following data with no additional configuration: metrics such as mAP and loss, hyperparameters, model checkpoints, interactive confusion matrix, and image [bounding box](https://www.ultralytics.com/glossary/bounding-box) predictions.
## Understanding Your Model's Performance with Comet ML Visualizations
Let's dive into what you'll see on the Comet ML dashboard once your YOLOv8 model begins training. The dashboard is where all the action happens, presenting a range of automatically logged information through visuals and statistics. Here's a quick tour:
Let's dive into what you'll see on the Comet ML dashboard once your YOLO11 model begins training. The dashboard is where all the action happens, presenting a range of automatically logged information through visuals and statistics. Here's a quick tour:
**Experiment Panels**
@ -169,19 +169,19 @@ os.environ["COMET_MODE"] = "offline"
## Summary
This guide has walked you through integrating Comet ML with Ultralytics' YOLOv8. From installation to customization, you've learned to streamline experiment management, gain real-time insights, and adapt logging to your project's needs.
This guide has walked you through integrating Comet ML with Ultralytics' YOLO11. From installation to customization, you've learned to streamline experiment management, gain real-time insights, and adapt logging to your project's needs.
Explore [Comet ML's official documentation](https://www.comet.com/docs/v2/integrations/third-party-tools/yolov8/) for more insights on integrating with YOLOv8.
Explore [Comet ML's official documentation](https://www.comet.com/docs/v2/integrations/third-party-tools/yolov8/) for more insights on integrating with YOLO11.
Furthermore, if you're looking to dive deeper into the practical applications of YOLOv8, specifically for [image segmentation](https://www.ultralytics.com/glossary/image-segmentation) tasks, this detailed guide on [fine-tuning YOLOv8 with Comet ML](https://www.comet.com/site/blog/fine-tuning-yolov8-for-image-segmentation-with-comet/) offers valuable insights and step-by-step instructions to enhance your model's performance.
Furthermore, if you're looking to dive deeper into the practical applications of YOLO11, specifically for [image segmentation](https://www.ultralytics.com/glossary/image-segmentation) tasks, this detailed guide on [fine-tuning YOLO11 with Comet ML](https://www.comet.com/site/blog/fine-tuning-yolov8-for-image-segmentation-with-comet/) offers valuable insights and step-by-step instructions to enhance your model's performance.
Additionally, to explore other exciting integrations with Ultralytics, check out the [integration guide page](../integrations/index.md), which offers a wealth of resources and information.
## FAQ
### How do I integrate Comet ML with Ultralytics YOLOv8 for training?
### How do I integrate Comet ML with Ultralytics YOLO11 for training?
To integrate Comet ML with Ultralytics YOLOv8, follow these steps:
To integrate Comet ML with Ultralytics YOLO11, follow these steps:
1. **Install the required packages**:
@ -203,12 +203,12 @@ To integrate Comet ML with Ultralytics YOLOv8, follow these steps:
comet_ml.login(project_name="comet-example-yolov8-coco128")
```
4. **Train your YOLOv8 model and log metrics**:
4. **Train your YOLO11 model and log metrics**:
```python
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
results = model.train(
data="coco8.yaml",
project="comet-example-yolov8-coco128",
@ -221,9 +221,9 @@ To integrate Comet ML with Ultralytics YOLOv8, follow these steps:
For more detailed instructions, refer to the [Comet ML configuration section](#configuring-comet-ml).
### What are the benefits of using Comet ML with YOLOv8?
### What are the benefits of using Comet ML with YOLO11?
By integrating Ultralytics YOLOv8 with Comet ML, you can:
By integrating Ultralytics YOLO11 with Comet ML, you can:
- **Monitor real-time insights**: Get instant feedback on your training results, allowing for quick adjustments.
- **Log extensive metrics**: Automatically capture essential metrics such as mAP, loss, hyperparameters, and model checkpoints.
@ -232,7 +232,7 @@ By integrating Ultralytics YOLOv8 with Comet ML, you can:
By leveraging these features, you can optimize your machine learning workflows for better performance and reproducibility. For more information, visit the [Comet ML integration guide](../integrations/index.md).
### How do I customize the logging behavior of Comet ML during YOLOv8 training?
### How do I customize the logging behavior of Comet ML during YOLO11 training?
Comet ML allows for extensive customization of its logging behavior using environment variables:
@ -262,9 +262,9 @@ Comet ML allows for extensive customization of its logging behavior using enviro
Refer to the [Customizing Comet ML Logging](#customizing-comet-ml-logging) section for more customization options.
### How do I view detailed metrics and visualizations of my YOLOv8 training on Comet ML?
### How do I view detailed metrics and visualizations of my YOLO11 training on Comet ML?
Once your YOLOv8 model starts training, you can access a wide range of metrics and visualizations on the Comet ML dashboard. Key features include:
Once your YOLO11 model starts training, you can access a wide range of metrics and visualizations on the Comet ML dashboard. Key features include:
- **Experiment Panels**: View different runs and their metrics, including segment mask loss, class loss, and mean average [precision](https://www.ultralytics.com/glossary/precision).
- **Metrics**: Examine metrics in tabular format for detailed analysis.
@ -273,7 +273,7 @@ Once your YOLOv8 model starts training, you can access a wide range of metrics a
For a detailed overview of these features, visit the [Understanding Your Model's Performance with Comet ML Visualizations](#understanding-your-models-performance-with-comet-ml-visualizations) section.
### Can I use Comet ML for offline logging when training YOLOv8 models?
### Can I use Comet ML for offline logging when training YOLO11 models?
Yes, you can enable offline logging in Comet ML by setting the `COMET_MODE` environment variable to "offline":

68
docs/en/integrations/coreml.md
View file

@ -1,14 +1,14 @@
---
comments: true
description: Learn how to export YOLOv8 models to CoreML for optimized, on-device machine learning on iOS and macOS. Follow step-by-step instructions.
keywords: CoreML export, YOLOv8 models, CoreML conversion, Ultralytics, iOS object detection, macOS machine learning, AI deployment, machine learning integration
description: Learn how to export YOLO11 models to CoreML for optimized, on-device machine learning on iOS and macOS. Follow step-by-step instructions.
keywords: CoreML export, YOLO11 models, CoreML conversion, Ultralytics, iOS object detection, macOS machine learning, AI deployment, machine learning integration
---
# CoreML Export for YOLOv8 Models
# CoreML Export for YOLO11 Models
Deploying [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models on Apple devices like iPhones and Macs requires a format that ensures seamless performance.
The CoreML export format allows you to optimize your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models for efficient [object detection](https://www.ultralytics.com/glossary/object-detection) in iOS and macOS applications. In this guide, we'll walk you through the steps for converting your models to the CoreML format, making it easier for your models to perform well on Apple devices.
The CoreML export format allows you to optimize your [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) models for efficient [object detection](https://www.ultralytics.com/glossary/object-detection) in iOS and macOS applications. In this guide, we'll walk you through the steps for converting your models to the CoreML format, making it easier for your models to perform well on Apple devices.
## CoreML
@ -40,7 +40,7 @@ Apple's CoreML framework offers robust features for on-device machine learning.
## CoreML Deployment Options
Before we look at the code for exporting YOLOv8 models to the CoreML format, let's understand where CoreML models are usually used.
Before we look at the code for exporting YOLO11 models to the CoreML format, let's understand where CoreML models are usually used.
CoreML offers various deployment options for machine learning models, including:
@ -52,9 +52,9 @@ CoreML offers various deployment options for machine learning models, including:
- **Cloud-Based Deployment**: CoreML models are hosted on servers and accessed by the iOS app through API requests. This scalable and flexible option enables easy model updates without app revisions. It's ideal for complex models or large-scale apps requiring regular updates. However, it does require an internet connection and may pose latency and security issues.
## Exporting YOLOv8 Models to CoreML
## Exporting YOLO11 Models to CoreML
Exporting YOLOv8 to CoreML enables optimized, on-device machine learning performance within Apple's ecosystem, offering benefits in terms of efficiency, security, and seamless integration with iOS, macOS, watchOS, and tvOS platforms.
Exporting YOLO11 to CoreML enables optimized, on-device machine learning performance within Apple's ecosystem, offering benefits in terms of efficiency, security, and seamless integration with iOS, macOS, watchOS, and tvOS platforms.
### Installation
@ -65,15 +65,15 @@ To install the required package, run:
=== "CLI"
```bash
# Install the required package for YOLOv8
# Install the required package for YOLO11
pip install ultralytics
```
For detailed instructions and best practices related to the installation process, check our [YOLOv8 Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
For detailed instructions and best practices related to the installation process, check our [YOLO11 Installation guide](../quickstart.md). While installing the required packages for YOLO11, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
### Usage
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.
Before diving into the usage instructions, be sure to check out the range of [YOLO11 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
!!! example "Usage"
@ -82,14 +82,14 @@ Before diving into the usage instructions, be sure to check out the range of [YO
```python
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO("yolov8n.pt")
# Load the YOLO11 model
model = YOLO("yolo11n.pt")
# Export the model to CoreML format
model.export(format="coreml") # creates 'yolov8n.mlpackage'
model.export(format="coreml") # creates 'yolo11n.mlpackage'
# Load the exported CoreML model
coreml_model = YOLO("yolov8n.mlpackage")
coreml_model = YOLO("yolo11n.mlpackage")
# Run inference
results = coreml_model("https://ultralytics.com/images/bus.jpg")
@ -98,18 +98,18 @@ Before diving into the usage instructions, be sure to check out the range of [YO
=== "CLI"
```bash
# Export a YOLOv8n PyTorch model to CoreML format
yolo export model=yolov8n.pt format=coreml # creates 'yolov8n.mlpackage''
# Export a YOLO11n PyTorch model to CoreML format
yolo export model=yolo11n.pt format=coreml # creates 'yolo11n.mlpackage''
# Run inference with the exported model
yolo predict model=yolov8n.mlpackage source='https://ultralytics.com/images/bus.jpg'
yolo predict model=yolo11n.mlpackage source='https://ultralytics.com/images/bus.jpg'
```
For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
## Deploying Exported YOLOv8 CoreML Models
## Deploying Exported YOLO11 CoreML Models
Having successfully exported your Ultralytics YOLOv8 models to CoreML, the next critical phase is deploying these models effectively. For detailed guidance on deploying CoreML models in various environments, check out these resources:
Having successfully exported your Ultralytics YOLO11 models to CoreML, the next critical phase is deploying these models effectively. For detailed guidance on deploying CoreML models in various environments, check out these resources:
- **[CoreML Tools](https://apple.github.io/coremltools/docs-guides/)**: This guide includes instructions and examples to convert models from [TensorFlow](https://www.ultralytics.com/glossary/tensorflow), PyTorch, and other libraries to Core ML.
@ -119,17 +119,17 @@ Having successfully exported your Ultralytics YOLOv8 models to CoreML, the next
## Summary
In this guide, we went over how to export Ultralytics YOLOv8 models to CoreML format. By following the steps outlined in this guide, you can ensure maximum compatibility and performance when exporting YOLOv8 models to CoreML.
In this guide, we went over how to export Ultralytics YOLO11 models to CoreML format. By following the steps outlined in this guide, you can ensure maximum compatibility and performance when exporting YOLO11 models to CoreML.
For further details on usage, visit the [CoreML official documentation](https://developer.apple.com/documentation/coreml).
Also, if you'd like to know more about other Ultralytics YOLOv8 integrations, visit our [integration guide page](../integrations/index.md). You'll find plenty of valuable resources and insights there.
Also, if you'd like to know more about other Ultralytics YOLO11 integrations, visit our [integration guide page](../integrations/index.md). You'll find plenty of valuable resources and insights there.
## FAQ
### How do I export YOLOv8 models to CoreML format?
### How do I export YOLO11 models to CoreML format?
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:
To export your [Ultralytics YOLO11](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:
!!! example "Installation"
@ -148,21 +148,21 @@ Next, you can export the model using the following Python or CLI commands:
```python
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
model.export(format="coreml")
```
=== "CLI"
```bash
yolo export model=yolov8n.pt format=coreml
yolo export model=yolo11n.pt format=coreml
```
For further details, refer to the [Exporting YOLOv8 Models to CoreML](../modes/export.md) section of our documentation.
For further details, refer to the [Exporting YOLO11 Models to CoreML](../modes/export.md) section of our documentation.
### What are the benefits of using CoreML for deploying YOLOv8 models?
### What are the benefits of using CoreML for deploying YOLO11 models?
CoreML provides numerous advantages for deploying [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models on Apple devices:
CoreML provides numerous advantages for deploying [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) models on Apple devices:
- **On-device Processing**: Enables local model inference on devices, ensuring [data privacy](https://www.ultralytics.com/glossary/data-privacy) and minimizing latency.
- **Performance Optimization**: Leverages the full potential of the device's CPU, GPU, and Neural Engine, optimizing both speed and efficiency.
@ -171,9 +171,9 @@ CoreML provides numerous advantages for deploying [Ultralytics YOLOv8](https://g
For more details on integrating your CoreML model into an iOS app, check out the guide on [Integrating a Core ML Model into Your App](https://developer.apple.com/documentation/coreml/integrating-a-core-ml-model-into-your-app).
### What are the deployment options for YOLOv8 models exported to CoreML?
### What are the deployment options for YOLO11 models exported to CoreML?
Once you export your YOLOv8 model to CoreML format, you have multiple deployment options:
Once you export your YOLO11 model to CoreML format, you have multiple deployment options:
1. **On-Device Deployment**: Directly integrate CoreML models into your app for enhanced privacy and offline functionality. This can be done as:
@ -184,9 +184,9 @@ Once you export your YOLOv8 model to CoreML format, you have multiple deployment
For detailed guidance on deploying CoreML models, refer to [CoreML Deployment Options](#coreml-deployment-options).
### How does CoreML ensure optimized performance for YOLOv8 models?
### How does CoreML ensure optimized performance for YOLO11 models?
CoreML ensures optimized performance for [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models by utilizing various optimization techniques:
CoreML ensures optimized performance for [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) models by utilizing various optimization techniques:
- **Hardware Acceleration**: Uses the device's CPU, GPU, and Neural Engine for efficient computation.
- **Model Compression**: Provides tools for compressing models to reduce their footprint without compromising accuracy.
@ -205,14 +205,14 @@ Yes, you can run inference directly using the exported CoreML model. Below are t
```python
from ultralytics import YOLO
coreml_model = YOLO("yolov8n.mlpackage")
coreml_model = YOLO("yolo11n.mlpackage")
results = coreml_model("https://ultralytics.com/images/bus.jpg")
```
=== "CLI"
```bash
yolo predict model=yolov8n.mlpackage source='https://ultralytics.com/images/bus.jpg'
yolo predict model=yolo11n.mlpackage source='https://ultralytics.com/images/bus.jpg'
```
For additional information, refer to the [Usage section](#usage) of the CoreML export guide.

66
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@ -1,14 +1,14 @@
---
comments: true
description: Unlock seamless YOLOv8 tracking with DVCLive. Discover how to log, visualize, and analyze experiments for optimized ML model performance.
keywords: YOLOv8, DVCLive, experiment tracking, machine learning, model training, data visualization, Git integration
description: Unlock seamless YOLO11 tracking with DVCLive. Discover how to log, visualize, and analyze experiments for optimized ML model performance.
keywords: YOLO11, DVCLive, experiment tracking, machine learning, model training, data visualization, Git integration
---
# Advanced YOLOv8 Experiment Tracking with DVCLive
# Advanced YOLO11 Experiment Tracking with DVCLive
Experiment tracking in [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) is critical to model development and evaluation. It involves recording and analyzing various parameters, metrics, and outcomes from numerous training runs. This process is essential for understanding model performance and making data-driven decisions to refine and optimize models.
Integrating DVCLive with [Ultralytics YOLOv8](https://www.ultralytics.com/) transforms the way experiments are tracked and managed. This integration offers a seamless solution for automatically logging key experiment details, comparing results across different runs, and visualizing data for in-depth analysis. In this guide, we'll understand how DVCLive can be used to streamline the process.
Integrating DVCLive with [Ultralytics YOLO11](https://www.ultralytics.com/) transforms the way experiments are tracked and managed. This integration offers a seamless solution for automatically logging key experiment details, comparing results across different runs, and visualizing data for in-depth analysis. In this guide, we'll understand how DVCLive can be used to streamline the process.
## DVCLive
@ -18,9 +18,9 @@ Integrating DVCLive with [Ultralytics YOLOv8](https://www.ultralytics.com/) tran
[DVCLive](https://dvc.org/doc/dvclive), developed by DVC, is an innovative open-source tool for experiment tracking in machine learning. Integrating seamlessly with Git and DVC, it automates the logging of crucial experiment data like model parameters and training metrics. Designed for simplicity, DVCLive enables effortless comparison and analysis of multiple runs, enhancing the efficiency of machine learning projects with intuitive [data visualization](https://www.ultralytics.com/glossary/data-visualization) and analysis tools.
## YOLOv8 Training with DVCLive
## YOLO11 Training with DVCLive
YOLOv8 training sessions can be effectively monitored with DVCLive. Additionally, DVC provides integral features for visualizing these experiments, including the generation of a report that enables the comparison of metric plots across all tracked experiments, offering a comprehensive view of the training process.
YOLO11 training sessions can be effectively monitored with DVCLive. Additionally, DVC provides integral features for visualizing these experiments, including the generation of a report that enables the comparison of metric plots across all tracked experiments, offering a comprehensive view of the training process.
## Installation
@ -31,11 +31,11 @@ To install the required packages, run:
=== "CLI"
```bash
# Install the required packages for YOLOv8 and DVCLive
# Install the required packages for YOLO11 and DVCLive
pip install ultralytics dvclive
```
For detailed instructions and best practices related to the installation process, be sure to check our [YOLOv8 Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
For detailed instructions and best practices related to the installation process, be sure to check our [YOLO11 Installation guide](../quickstart.md). While installing the required packages for YOLO11, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
## Configuring DVCLive
@ -66,27 +66,27 @@ In these commands, ensure to replace "you@example.com" with the email address as
## Usage
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.
Before diving into the usage instructions, be sure to check out the range of [YOLO11 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
### Training YOLOv8 Models with DVCLive
### Training YOLO11 Models with DVCLive
Start by running your YOLOv8 training sessions. You can use different model configurations and training parameters to suit your project needs. For instance:
Start by running your YOLO11 training sessions. You can use different model configurations and training parameters to suit your project needs. For instance:
```bash
# Example training commands for YOLOv8 with varying configurations
yolo train model=yolov8n.pt data=coco8.yaml epochs=5 imgsz=512
yolo train model=yolov8n.pt data=coco8.yaml epochs=5 imgsz=640
# Example training commands for YOLO11 with varying configurations
yolo train model=yolo11n.pt data=coco8.yaml epochs=5 imgsz=512
yolo train model=yolo11n.pt data=coco8.yaml epochs=5 imgsz=640
```
Adjust the model, data, [epochs](https://www.ultralytics.com/glossary/epoch), and imgsz parameters according to your specific requirements. For a detailed understanding of the model training process and best practices, refer to our [YOLOv8 Model Training guide](../modes/train.md).
Adjust the model, data, [epochs](https://www.ultralytics.com/glossary/epoch), and imgsz parameters according to your specific requirements. For a detailed understanding of the model training process and best practices, refer to our [YOLO11 Model Training guide](../modes/train.md).
### Monitoring Experiments with DVCLive
DVCLive enhances the training process by enabling the tracking and visualization of key metrics. When installed, Ultralytics YOLOv8 automatically integrates with DVCLive for experiment tracking, which you can later analyze for performance insights. For a comprehensive understanding of the specific performance metrics used during training, be sure to explore [our detailed guide on performance metrics](../guides/yolo-performance-metrics.md).
DVCLive enhances the training process by enabling the tracking and visualization of key metrics. When installed, Ultralytics YOLO11 automatically integrates with DVCLive for experiment tracking, which you can later analyze for performance insights. For a comprehensive understanding of the specific performance metrics used during training, be sure to explore [our detailed guide on performance metrics](../guides/yolo-performance-metrics.md).
### Analyzing Results
After your YOLOv8 training sessions are complete, you can leverage DVCLive's powerful visualization tools for in-depth analysis of the results. DVCLive's integration ensures that all training metrics are systematically logged, facilitating a comprehensive evaluation of your model's performance.
After your YOLO11 training sessions are complete, you can leverage DVCLive's powerful visualization tools for in-depth analysis of the results. DVCLive's integration ensures that all training metrics are systematically logged, facilitating a comprehensive evaluation of your model's performance.
To start the analysis, you can extract the experiment data using DVC's API and process it with Pandas for easier handling and visualization:
@ -108,7 +108,7 @@ df.reset_index(drop=True, inplace=True)
print(df)
```
The output of the code snippet above provides a clear tabular view of the different experiments conducted with YOLOv8 models. Each row represents a different training run, detailing the experiment's name, the number of epochs, image size (imgsz), the specific model used, and the mAP50-95(B) metric. This metric is crucial for evaluating the model's [accuracy](https://www.ultralytics.com/glossary/accuracy), with higher values indicating better performance.
The output of the code snippet above provides a clear tabular view of the different experiments conducted with YOLO11 models. Each row represents a different training run, detailing the experiment's name, the number of epochs, image size (imgsz), the specific model used, and the mAP50-95(B) metric. This metric is crucial for evaluating the model's [accuracy](https://www.ultralytics.com/glossary/accuracy), with higher values indicating better performance.
#### Visualizing Results with Plotly
@ -164,7 +164,7 @@ Based on your analysis, iterate on your experiments. Adjust model configurations
## Summary
This guide has led you through the process of integrating DVCLive with Ultralytics' YOLOv8. You have learned how to harness the power of DVCLive for detailed experiment monitoring, effective visualization, and insightful analysis in your machine learning endeavors.
This guide has led you through the process of integrating DVCLive with Ultralytics' YOLO11. You have learned how to harness the power of DVCLive for detailed experiment monitoring, effective visualization, and insightful analysis in your machine learning endeavors.
For further details on usage, visit [DVCLive's official documentation](https://dvc.org/doc/dvclive/ml-frameworks/yolo).
@ -172,9 +172,9 @@ Additionally, explore more integrations and capabilities of Ultralytics by visit
## FAQ
### How do I integrate DVCLive with Ultralytics YOLOv8 for experiment tracking?
### How do I integrate DVCLive with Ultralytics YOLO11 for experiment tracking?
Integrating DVCLive with Ultralytics YOLOv8 is straightforward. Start by installing the necessary packages:
Integrating DVCLive with Ultralytics YOLO11 is straightforward. Start by installing the necessary packages:
!!! example "Installation"
@ -198,21 +198,21 @@ Next, initialize a Git repository and configure DVCLive in your project:
git commit -m "DVC init"
```
Follow our [YOLOv8 Installation guide](../quickstart.md) for detailed setup instructions.
Follow our [YOLO11 Installation guide](../quickstart.md) for detailed setup instructions.
### Why should I use DVCLive for tracking YOLOv8 experiments?
### Why should I use DVCLive for tracking YOLO11 experiments?
Using DVCLive with YOLOv8 provides several advantages, such as:
Using DVCLive with YOLO11 provides several advantages, such as:
- **Automated Logging**: DVCLive automatically records key experiment details like model parameters and metrics.
- **Easy Comparison**: Facilitates comparison of results across different runs.
- **Visualization Tools**: Leverages DVCLive's robust data visualization capabilities for in-depth analysis.
For further details, refer to our guide on [YOLOv8 Model Training](../modes/train.md) and [YOLO Performance Metrics](../guides/yolo-performance-metrics.md) to maximize your experiment tracking efficiency.
For further details, refer to our guide on [YOLO11 Model Training](../modes/train.md) and [YOLO Performance Metrics](../guides/yolo-performance-metrics.md) to maximize your experiment tracking efficiency.
### How can DVCLive improve my results analysis for YOLOv8 training sessions?
### How can DVCLive improve my results analysis for YOLO11 training sessions?
After completing your YOLOv8 training sessions, DVCLive helps in visualizing and analyzing the results effectively. Example code for loading and displaying experiment data:
After completing your YOLO11 training sessions, DVCLive helps in visualizing and analyzing the results effectively. Example code for loading and displaying experiment data:
```python
import dvc.api
@ -241,11 +241,11 @@ fig = parallel_coordinates(df, columns, color="metrics.mAP50-95(B)")
fig.show()
```
Refer to our guide on [YOLOv8 Training with DVCLive](#yolov8-training-with-dvclive) for more examples and best practices.
Refer to our guide on [YOLO11 Training with DVCLive](#yolo11-training-with-dvclive) for more examples and best practices.
### What are the steps to configure my environment for DVCLive and YOLOv8 integration?
### What are the steps to configure my environment for DVCLive and YOLO11 integration?
To configure your environment for a smooth integration of DVCLive and YOLOv8, follow these steps:
To configure your environment for a smooth integration of DVCLive and YOLO11, follow these steps:
1. **Install Required Packages**: Use `pip install ultralytics dvclive`.
2. **Initialize Git Repository**: Run `git init -q`.
@ -254,9 +254,9 @@ To configure your environment for a smooth integration of DVCLive and YOLOv8, fo
These steps ensure proper version control and setup for experiment tracking. For in-depth configuration details, visit our [Configuration guide](../quickstart.md).
### How do I visualize YOLOv8 experiment results using DVCLive?
### How do I visualize YOLO11 experiment results using DVCLive?
DVCLive offers powerful tools to visualize the results of YOLOv8 experiments. Here's how you can generate comparative plots:
DVCLive offers powerful tools to visualize the results of YOLO11 experiments. Here's how you can generate comparative plots:
!!! example "Generate Comparative Plots"
@ -275,4 +275,4 @@ from IPython.display import HTML
HTML(filename="./dvc_plots/index.html")
```
These visualizations help identify trends and optimize model performance. Check our detailed guides on [YOLOv8 Experiment Analysis](#analyzing-results) for comprehensive steps and examples.
These visualizations help identify trends and optimize model performance. Check our detailed guides on [YOLO11 Experiment Analysis](#analyzing-results) for comprehensive steps and examples.

66
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@ -1,14 +1,14 @@
---
comments: true
description: Learn how to export YOLOv8 models to TFLite Edge TPU format for high-speed, low-power inferencing on mobile and embedded devices.
keywords: YOLOv8, TFLite Edge TPU, TensorFlow Lite, model export, machine learning, edge computing, neural networks, Ultralytics
description: Learn how to export YOLO11 models to TFLite Edge TPU format for high-speed, low-power inferencing on mobile and embedded devices.
keywords: YOLO11, TFLite Edge TPU, TensorFlow Lite, model export, machine learning, edge computing, neural networks, Ultralytics
---
# Learn to Export to TFLite Edge TPU Format From YOLOv8 Model
# Learn to Export to TFLite Edge TPU Format From YOLO11 Model
Deploying computer vision models on devices with limited computational power, such as mobile or embedded systems, can be tricky. Using a model format that is optimized for faster performance simplifies the process. The [TensorFlow Lite](https://ai.google.dev/edge/litert) [Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) or TFLite Edge TPU model format is designed to use minimal power while delivering fast performance for neural networks.
The export to TFLite Edge TPU format feature allows you to optimize your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models for high-speed and low-power inferencing. In this guide, we'll walk you through converting your models to the TFLite Edge TPU format, making it easier for your models to perform well on various mobile and embedded devices.
The export to TFLite Edge TPU format feature allows you to optimize your [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) models for high-speed and low-power inferencing. In this guide, we'll walk you through converting your models to the TFLite Edge TPU format, making it easier for your models to perform well on various mobile and embedded devices.
## Why Should You Export to TFLite Edge TPU?
@ -32,7 +32,7 @@ Here are the key features that make TFLite Edge TPU a great model format choice
## Deployment Options with TFLite Edge TPU
Before we jump into how to export YOLOv8 models to the TFLite Edge TPU format, let's understand where TFLite Edge TPU models are usually used.
Before we jump into how to export YOLO11 models to the TFLite Edge TPU format, let's understand where TFLite Edge TPU models are usually used.
TFLite Edge TPU offers various deployment options for machine learning models, including:
@ -42,9 +42,9 @@ TFLite Edge TPU offers various deployment options for machine learning models, i
- **Hybrid Deployment**: A hybrid approach combines on-device and cloud deployment and offers a versatile and scalable solution for deploying machine learning models. Advantages include on-device processing for quick responses and [cloud computing](https://www.ultralytics.com/glossary/cloud-computing) for more complex computations.
## Exporting YOLOv8 Models to TFLite Edge TPU
## Exporting YOLO11 Models to TFLite Edge TPU
You can expand model compatibility and deployment flexibility by converting YOLOv8 models to TensorFlow Edge TPU.
You can expand model compatibility and deployment flexibility by converting YOLO11 models to TensorFlow Edge TPU.
### Installation
@ -55,15 +55,15 @@ To install the required package, run:
=== "CLI"
```bash
# Install the required package for YOLOv8
# Install the required package for YOLO11
pip install ultralytics
```
For detailed instructions and best practices related to the installation process, check our [Ultralytics Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
For detailed instructions and best practices related to the installation process, check our [Ultralytics Installation guide](../quickstart.md). While installing the required packages for YOLO11, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
### Usage
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).
Before diving into the usage instructions, it's important to note that while all [Ultralytics YOLO11 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"
@ -72,14 +72,14 @@ Before diving into the usage instructions, it's important to note that while all
```python
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO("yolov8n.pt")
# Load the YOLO11 model
model = YOLO("yolo11n.pt")
# Export the model to TFLite Edge TPU format
model.export(format="edgetpu") # creates 'yolov8n_full_integer_quant_edgetpu.tflite'
model.export(format="edgetpu") # creates 'yolo11n_full_integer_quant_edgetpu.tflite'
# Load the exported TFLite Edge TPU model
edgetpu_model = YOLO("yolov8n_full_integer_quant_edgetpu.tflite")
edgetpu_model = YOLO("yolo11n_full_integer_quant_edgetpu.tflite")
# Run inference
results = edgetpu_model("https://ultralytics.com/images/bus.jpg")
@ -88,22 +88,22 @@ Before diving into the usage instructions, it's important to note that while all
=== "CLI"
```bash
# Export a YOLOv8n PyTorch model to TFLite Edge TPU format
yolo export model=yolov8n.pt format=edgetpu # creates 'yolov8n_full_integer_quant_edgetpu.tflite'
# Export a YOLO11n PyTorch model to TFLite Edge TPU format
yolo export model=yolo11n.pt format=edgetpu # creates 'yolo11n_full_integer_quant_edgetpu.tflite'
# Run inference with the exported model
yolo predict model=yolov8n_full_integer_quant_edgetpu.tflite source='https://ultralytics.com/images/bus.jpg'
yolo predict model=yolo11n_full_integer_quant_edgetpu.tflite source='https://ultralytics.com/images/bus.jpg'
```
For more details about supported export options, visit the [Ultralytics documentation page on deployment options](../guides/model-deployment-options.md).
## Deploying Exported YOLOv8 TFLite Edge TPU Models
## Deploying Exported YOLO11 TFLite Edge TPU Models
After successfully exporting your Ultralytics YOLOv8 models to TFLite Edge TPU format, you can now deploy them. The primary and recommended first step for running a TFLite Edge TPU model is to use the YOLO("model_edgetpu.tflite") method, as outlined in the previous usage code snippet.
After successfully exporting your Ultralytics YOLO11 models to TFLite Edge TPU format, you can now deploy them. The primary and recommended first step for running a TFLite Edge TPU model is to use the YOLO("model_edgetpu.tflite") method, as outlined in the previous usage code snippet.
However, for in-depth instructions on deploying your TFLite Edge TPU models, take a look at the following resources:
- **[Coral Edge TPU on a Raspberry Pi with Ultralytics YOLOv8](../guides/coral-edge-tpu-on-raspberry-pi.md)**: Discover how to integrate Coral Edge TPUs with Raspberry Pi for enhanced machine learning capabilities.
- **[Coral Edge TPU on a Raspberry Pi with Ultralytics YOLO11](../guides/coral-edge-tpu-on-raspberry-pi.md)**: Discover how to integrate Coral Edge TPUs with Raspberry Pi for enhanced machine learning capabilities.
- **[Code Examples](https://coral.ai/docs/edgetpu/compiler/)**: Access practical TensorFlow Edge TPU deployment examples to kickstart your projects.
@ -111,17 +111,17 @@ However, for in-depth instructions on deploying your TFLite Edge TPU models, tak
## Summary
In this guide, we've learned how to export Ultralytics YOLOv8 models to TFLite Edge TPU format. By following the steps mentioned above, you can increase the speed and power of your [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) applications.
In this guide, we've learned how to export Ultralytics YOLO11 models to TFLite Edge TPU format. By following the steps mentioned above, you can increase the speed and power of your [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) applications.
For further details on usage, visit the [Edge TPU official website](https://cloud.google.com/tpu).
Also, for more information on other Ultralytics YOLOv8 integrations, please visit our [integration guide page](index.md). There, you'll discover valuable resources and insights.
Also, for more information on other Ultralytics YOLO11 integrations, please visit our [integration guide page](index.md). There, you'll discover valuable resources and insights.
## FAQ
### How do I export a YOLOv8 model to TFLite Edge TPU format?
### How do I export a YOLO11 model to TFLite Edge TPU format?
To export a YOLOv8 model to TFLite Edge TPU format, you can follow these steps:
To export a YOLO11 model to TFLite Edge TPU format, you can follow these steps:
!!! example "Usage"
@ -130,14 +130,14 @@ To export a YOLOv8 model to TFLite Edge TPU format, you can follow these steps:
```python
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO("yolov8n.pt")
# Load the YOLO11 model
model = YOLO("yolo11n.pt")
# Export the model to TFLite Edge TPU format
model.export(format="edgetpu") # creates 'yolov8n_full_integer_quant_edgetpu.tflite'
model.export(format="edgetpu") # creates 'yolo11n_full_integer_quant_edgetpu.tflite'
# Load the exported TFLite Edge TPU model
edgetpu_model = YOLO("yolov8n_full_integer_quant_edgetpu.tflite")
edgetpu_model = YOLO("yolo11n_full_integer_quant_edgetpu.tflite")
# Run inference
results = edgetpu_model("https://ultralytics.com/images/bus.jpg")
@ -146,18 +146,18 @@ To export a YOLOv8 model to TFLite Edge TPU format, you can follow these steps:
=== "CLI"
```bash
# Export a YOLOv8n PyTorch model to TFLite Edge TPU format
yolo export model=yolov8n.pt format=edgetpu # creates 'yolov8n_full_integer_quant_edgetpu.tflite'
# Export a YOLO11n PyTorch model to TFLite Edge TPU format
yolo export model=yolo11n.pt format=edgetpu # creates 'yolo11n_full_integer_quant_edgetpu.tflite'
# Run inference with the exported model
yolo predict model=yolov8n_full_integer_quant_edgetpu.tflite source='https://ultralytics.com/images/bus.jpg'
yolo predict model=yolo11n_full_integer_quant_edgetpu.tflite source='https://ultralytics.com/images/bus.jpg'
```
For complete details on exporting models to other formats, refer to our [export guide](../modes/export.md).
### What are the benefits of exporting YOLOv8 models to TFLite Edge TPU?
### What are the benefits of exporting YOLO11 models to TFLite Edge TPU?
Exporting YOLOv8 models to TFLite Edge TPU offers several benefits:
Exporting YOLO11 models to TFLite Edge TPU offers several benefits:
- **Optimized Performance**: Achieve high-speed neural network performance with minimal power consumption.
- **Reduced Latency**: Quick local data processing without the need for cloud dependency.

42
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@ -1,14 +1,14 @@
---
comments: true
description: Learn how to efficiently train Ultralytics YOLOv8 models using Google Colab's powerful cloud-based environment. Start your project with ease.
keywords: YOLOv8, Google Colab, machine learning, deep learning, model training, GPU, TPU, cloud computing, Jupyter Notebook, Ultralytics
description: Learn how to efficiently train Ultralytics YOLO11 models using Google Colab's powerful cloud-based environment. Start your project with ease.
keywords: YOLO11, Google Colab, machine learning, deep learning, model training, GPU, TPU, cloud computing, Jupyter Notebook, Ultralytics
---
# Accelerating YOLOv8 Projects with Google Colab
# Accelerating YOLO11 Projects with Google Colab
Many developers lack the powerful computing resources needed to build [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models. Acquiring high-end hardware or renting a decent GPU can be expensive. Google Colab is a great solution to this. It's a browser-based platform that allows you to work with large datasets, develop complex models, and share your work with others without a huge cost.
You can use Google Colab to work on projects related to [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models. Google Colab's user-friendly environment is well suited for efficient model development and experimentation. Let's learn more about Google Colab, its key features, and how you can use it to train YOLOv8 models.
You can use Google Colab to work on projects related to [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) models. Google Colab's user-friendly environment is well suited for efficient model development and experimentation. Let's learn more about Google Colab, its key features, and how you can use it to train YOLO11 models.
## Google Colaboratory
@ -16,15 +16,15 @@ Google Colaboratory, commonly known as Google Colab, was developed by Google Res
You can use Google Colab regardless of the specifications and configurations of your local computer. All you need is a Google account and a web browser, and you're good to go.
## Training YOLOv8 Using Google Colaboratory
## Training YOLO11 Using Google Colaboratory
Training YOLOv8 models on Google Colab is pretty straightforward. Thanks to the integration, you can access the [Google Colab YOLOv8 Notebook](https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb) and start training your model immediately. For a detailed understanding of the model training process and best practices, refer to our [YOLOv8 Model Training guide](../modes/train.md).
Training YOLO11 models on Google Colab is pretty straightforward. Thanks to the integration, you can access the [Google Colab YOLO11 Notebook](https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb) and start training your model immediately. For a detailed understanding of the model training process and best practices, refer to our [YOLO11 Model Training guide](../modes/train.md).
Sign in to your Google account and run the notebook's cells to train your model.
![Training YOLOv8 Using Google Colab](https://github.com/ultralytics/docs/releases/download/0/training-yolov8-using-google-colab.avif)
![Training YOLO11 Using Google Colab](https://github.com/ultralytics/docs/releases/download/0/training-yolov8-using-google-colab.avif)
Learn how to train a YOLOv8 model with custom data on YouTube with Nicolai. Check out the guide below.
Learn how to train a YOLO11 model with custom data on YouTube with Nicolai. Check out the guide below.
<p align="center">
<br>
@ -34,7 +34,7 @@ Learn how to train a YOLOv8 model with custom data on YouTube with Nicolai. Chec
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> How to Train Ultralytics YOLOv8 models on Your Custom Dataset in Google Colab | Episode 3
<strong>Watch:</strong> How to Train Ultralytics YOLO11 models on Your Custom Dataset in Google Colab | Episode 3
</p>
### Common Questions While Working with Google Colab
@ -75,9 +75,9 @@ Now, let's look at some of the standout features that make Google Colab a go-to
- **Educational Resources:** Google Colab offers a range of tutorials and example notebooks to help users learn and explore various functionalities.
## Why Should You Use Google Colab for Your YOLOv8 Projects?
## Why Should You Use Google Colab for Your YOLO11 Projects?
There are many options for training and evaluating YOLOv8 models, so what makes the integration with Google Colab unique? Let's explore the advantages of this integration:
There are many options for training and evaluating YOLO11 models, so what makes the integration with Google Colab unique? Let's explore the advantages of this integration:
- **Zero Setup:** Since Colab runs in the cloud, users can start training models immediately without the need for complex environment setups. Just create an account and start coding.
@ -95,7 +95,7 @@ There are many options for training and evaluating YOLOv8 models, so what makes
If you'd like to dive deeper into Google Colab, here are a few resources to guide you.
- **[Training Custom Datasets with Ultralytics YOLOv8 in Google Colab](https://www.ultralytics.com/blog/training-custom-datasets-with-ultralytics-yolov8-in-google-colab)**: Learn how to train custom datasets with Ultralytics YOLOv8 on Google Colab. This comprehensive blog post will take you through the entire process, from initial setup to the training and evaluation stages.
- **[Training Custom Datasets with Ultralytics YOLO11 in Google Colab](https://www.ultralytics.com/blog/training-custom-datasets-with-ultralytics-yolov8-in-google-colab)**: Learn how to train custom datasets with Ultralytics YOLO11 on Google Colab. This comprehensive blog post will take you through the entire process, from initial setup to the training and evaluation stages.
- **[Curated Notebooks](https://colab.google/notebooks/)**: Here you can explore a series of organized and educational notebooks, each grouped by specific topic areas.
@ -103,21 +103,21 @@ If you'd like to dive deeper into Google Colab, here are a few resources to guid
## Summary
We've discussed how you can easily experiment with Ultralytics YOLOv8 models on Google Colab. You can use Google Colab to train and evaluate your models on GPUs and TPUs with a few clicks.
We've discussed how you can easily experiment with Ultralytics YOLO11 models on Google Colab. You can use Google Colab to train and evaluate your models on GPUs and TPUs with a few clicks.
For more details, visit [Google Colab's FAQ page](https://research.google.com/colaboratory/intl/en-GB/faq.html).
Interested in more YOLOv8 integrations? Visit the [Ultralytics integration guide page](index.md) to explore additional tools and capabilities that can improve your machine-learning projects.
Interested in more YOLO11 integrations? Visit the [Ultralytics integration guide page](index.md) to explore additional tools and capabilities that can improve your machine-learning projects.
## FAQ
### How do I start training Ultralytics YOLOv8 models on Google Colab?
### How do I start training Ultralytics YOLO11 models on Google Colab?
To start training Ultralytics YOLOv8 models on Google Colab, sign in to your Google account, then access the [Google Colab YOLOv8 Notebook](https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb). This notebook guides you through the setup and training process. After launching the notebook, run the cells step-by-step to train your model. For a full guide, refer to the [YOLOv8 Model Training guide](../modes/train.md).
To start training Ultralytics YOLO11 models on Google Colab, sign in to your Google account, then access the [Google Colab YOLO11 Notebook](https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb). This notebook guides you through the setup and training process. After launching the notebook, run the cells step-by-step to train your model. For a full guide, refer to the [YOLO11 Model Training guide](../modes/train.md).
### What are the advantages of using Google Colab for training YOLOv8 models?
### What are the advantages of using Google Colab for training YOLO11 models?
Google Colab offers several advantages for training YOLOv8 models:
Google Colab offers several advantages for training YOLO11 models:
- **Zero Setup:** No initial environment setup is required; just log in and start coding.
- **Free GPU Access:** Use powerful GPUs or TPUs without the need for expensive hardware.
@ -126,7 +126,7 @@ Google Colab offers several advantages for training YOLOv8 models:
For more information on why you should use Google Colab, explore the [training guide](../modes/train.md) and visit the [Google Colab page](https://colab.google/notebooks/).
### How can I handle Google Colab session timeouts during YOLOv8 training?
### How can I handle Google Colab session timeouts during YOLO11 training?
Google Colab sessions timeout due to inactivity, especially for free users. To handle this:
@ -136,9 +136,9 @@ Google Colab sessions timeout due to inactivity, especially for free users. To h
For more tips on managing your Colab session, visit the [Google Colab FAQ page](https://research.google.com/colaboratory/intl/en-GB/faq.html).
### Can I use custom datasets for training YOLOv8 models in Google Colab?
### Can I use custom datasets for training YOLO11 models in Google Colab?
Yes, you can use custom datasets to train YOLOv8 models in Google Colab. Upload your dataset to Google Drive and load it directly into your Colab notebook. You can follow Nicolai's YouTube guide, [How to Train YOLOv8 Models on Your Custom Dataset](https://www.youtube.com/watch?v=LNwODJXcvt4), or refer to the [Custom Dataset Training guide](https://www.ultralytics.com/blog/training-custom-datasets-with-ultralytics-yolov8-in-google-colab) for detailed steps.
Yes, you can use custom datasets to train YOLO11 models in Google Colab. Upload your dataset to Google Drive and load it directly into your Colab notebook. You can follow Nicolai's YouTube guide, [How to Train YOLO11 Models on Your Custom Dataset](https://www.youtube.com/watch?v=LNwODJXcvt4), or refer to the [Custom Dataset Training guide](https://www.ultralytics.com/blog/training-custom-datasets-with-ultralytics-yolov8-in-google-colab) for detailed steps.
### What should I do if my Google Colab training session is interrupted?

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@ -1,14 +1,14 @@
---
comments: true
description: Discover an interactive way to perform object detection with Ultralytics YOLOv8 using Gradio. Upload images and adjust settings for real-time results.
keywords: Ultralytics, YOLOv8, Gradio, object detection, interactive, real-time, image processing, AI
description: Discover an interactive way to perform object detection with Ultralytics YOLO11 using Gradio. Upload images and adjust settings for real-time results.
keywords: Ultralytics, YOLO11, Gradio, object detection, interactive, real-time, image processing, AI
---
# Interactive [Object Detection](https://www.ultralytics.com/glossary/object-detection): Gradio & Ultralytics YOLOv8 🚀
# Interactive [Object Detection](https://www.ultralytics.com/glossary/object-detection): Gradio & Ultralytics YOLO11 🚀
## Introduction to Interactive Object Detection
This Gradio interface provides an easy and interactive way to perform object detection using the [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) model. Users can upload images and adjust parameters like confidence threshold and intersection-over-union (IoU) threshold to get real-time detection results.
This Gradio interface provides an easy and interactive way to perform object detection using the [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) model. Users can upload images and adjust parameters like confidence threshold and intersection-over-union (IoU) threshold to get real-time detection results.
<p align="center">
<br>
@ -18,7 +18,7 @@ This Gradio interface provides an easy and interactive way to perform object det
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Gradio Integration with Ultralytics YOLOv8
<strong>Watch:</strong> Gradio Integration with Ultralytics YOLO11
</p>
## Why Use Gradio for Object Detection?
@ -52,7 +52,7 @@ pip install gradio
## Usage Example
This section provides the Python code used to create the Gradio interface with the Ultralytics YOLOv8 model. Supports classification tasks, detection tasks, segmentation tasks, and key point tasks.
This section provides the Python code used to create the Gradio interface with the Ultralytics YOLO11 model. Supports classification tasks, detection tasks, segmentation tasks, and key point tasks.
```python
import gradio as gr
@ -60,11 +60,11 @@ import PIL.Image as Image
from ultralytics import ASSETS, YOLO
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
def predict_image(img, conf_threshold, iou_threshold):
"""Predicts objects in an image using a YOLOv8 model with adjustable confidence and IOU thresholds."""
"""Predicts objects in an image using a YOLO11 model with adjustable confidence and IOU thresholds."""
results = model.predict(
source=img,
conf=conf_threshold,
@ -90,7 +90,7 @@ iface = gr.Interface(
],
outputs=gr.Image(type="pil", label="Result"),
title="Ultralytics Gradio",
description="Upload images for inference. The Ultralytics YOLOv8n model is used by default.",
description="Upload images for inference. The Ultralytics YOLO11n model is used by default.",
examples=[
[ASSETS / "bus.jpg", 0.25, 0.45],
[ASSETS / "zidane.jpg", 0.25, 0.45],
@ -119,9 +119,9 @@ if __name__ == "__main__":
## FAQ
### How do I use Gradio with Ultralytics YOLOv8 for object detection?
### How do I use Gradio with Ultralytics YOLO11 for object detection?
To use Gradio with Ultralytics YOLOv8 for object detection, you can follow these steps:
To use Gradio with Ultralytics YOLO11 for object detection, you can follow these steps:
1. **Install Gradio:** Use the command `pip install gradio`.
2. **Create Interface:** Write a Python script to initialize the Gradio interface. You can refer to the provided code example in the [documentation](#usage-example) for details.
@ -134,7 +134,7 @@ import gradio as gr
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
def predict_image(img, conf_threshold, iou_threshold):
@ -156,15 +156,15 @@ iface = gr.Interface(
gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold"),
],
outputs=gr.Image(type="pil", label="Result"),
title="Ultralytics Gradio YOLOv8",
description="Upload images for YOLOv8 object detection.",
title="Ultralytics Gradio YOLO11",
description="Upload images for YOLO11 object detection.",
)
iface.launch()
```
### What are the benefits of using Gradio for Ultralytics YOLOv8 object detection?
### What are the benefits of using Gradio for Ultralytics YOLO11 object detection?
Using Gradio for Ultralytics YOLOv8 object detection offers several benefits:
Using Gradio for Ultralytics YOLO11 object detection offers several benefits:
- **User-Friendly Interface:** Gradio provides an intuitive interface for users to upload images and visualize detection results without any coding effort.
- **Real-Time Adjustments:** You can dynamically adjust detection parameters such as confidence and IoU thresholds and see the effects immediately.
@ -172,22 +172,22 @@ Using Gradio for Ultralytics YOLOv8 object detection offers several benefits:
For more details, you can read this [blog post](https://www.ultralytics.com/blog/ai-and-radiology-a-new-era-of-precision-and-efficiency).
### Can I use Gradio and Ultralytics YOLOv8 together for educational purposes?
### Can I use Gradio and Ultralytics YOLO11 together for educational purposes?
Yes, Gradio and Ultralytics YOLOv8 can be utilized together for educational purposes effectively. Gradio's intuitive web interface makes it easy for students and educators to interact with state-of-the-art [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models like Ultralytics YOLOv8 without needing advanced programming skills. This setup is ideal for demonstrating key concepts in object detection and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv), as Gradio provides immediate visual feedback which helps in understanding the impact of different parameters on the detection performance.
Yes, Gradio and Ultralytics YOLO11 can be utilized together for educational purposes effectively. Gradio's intuitive web interface makes it easy for students and educators to interact with state-of-the-art [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models like Ultralytics YOLO11 without needing advanced programming skills. This setup is ideal for demonstrating key concepts in object detection and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv), as Gradio provides immediate visual feedback which helps in understanding the impact of different parameters on the detection performance.
### How do I adjust the confidence and IoU thresholds in the Gradio interface for YOLOv8?
### How do I adjust the confidence and IoU thresholds in the Gradio interface for YOLO11?
In the Gradio interface for YOLOv8, you can adjust the confidence and IoU thresholds using the sliders provided. These thresholds help control the prediction [accuracy](https://www.ultralytics.com/glossary/accuracy) and object separation:
In the Gradio interface for YOLO11, you can adjust the confidence and IoU thresholds using the sliders provided. These thresholds help control the prediction [accuracy](https://www.ultralytics.com/glossary/accuracy) and object separation:
- **Confidence Threshold:** Determines the minimum confidence level for detecting objects. Slide to increase or decrease the confidence required.
- **IoU Threshold:** Sets the intersection-over-union threshold for distinguishing between overlapping objects. Adjust this value to refine object separation.
For more information on these parameters, visit the [parameters explanation section](#parameters-explanation).
### What are some practical applications of using Ultralytics YOLOv8 with Gradio?
### What are some practical applications of using Ultralytics YOLO11 with Gradio?
Practical applications of combining Ultralytics YOLOv8 with Gradio include:
Practical applications of combining Ultralytics YOLO11 with Gradio include:
- **Real-Time Object Detection Demonstrations:** Ideal for showcasing how object detection works in real-time.
- **Educational Tools:** Useful in academic settings to teach object detection and computer vision concepts.
@ -196,4 +196,4 @@ Practical applications of combining Ultralytics YOLOv8 with Gradio include:
For examples of similar use cases, check out the [Ultralytics blog](https://www.ultralytics.com/blog/monitoring-animal-behavior-using-ultralytics-yolov8).
Providing this information within the documentation will help in enhancing the usability and accessibility of Ultralytics YOLOv8, making it more approachable for users at all levels of expertise.
Providing this information within the documentation will help in enhancing the usability and accessibility of Ultralytics YOLO11, making it more approachable for users at all levels of expertise.

54
docs/en/integrations/ibm-watsonx.md
View file

@ -1,14 +1,14 @@
---
comments: true
description: Dive into our detailed integration guide on using IBM Watson to train a YOLOv8 model. Uncover key features and step-by-step instructions on model training.
keywords: IBM Watsonx, IBM Watsonx AI, What is Watson?, IBM Watson Integration, IBM Watson Features, YOLOv8, Ultralytics, Model Training, GPU, TPU, cloud computing
description: Dive into our detailed integration guide on using IBM Watson to train a YOLO11 model. Uncover key features and step-by-step instructions on model training.
keywords: IBM Watsonx, IBM Watsonx AI, What is Watson?, IBM Watson Integration, IBM Watson Features, YOLO11, Ultralytics, Model Training, GPU, TPU, cloud computing
---
# A Step-by-Step Guide to Training YOLOv8 Models with IBM Watsonx
# A Step-by-Step Guide to Training YOLO11 Models with IBM Watsonx
Nowadays, scalable [computer vision solutions](../guides/steps-of-a-cv-project.md) are becoming more common and transforming the way we handle visual data. A great example is IBM Watsonx, an advanced AI and data platform that simplifies the development, deployment, and management of AI models. It offers a complete suite for the entire AI lifecycle and seamless integration with IBM Cloud services.
You can train [Ultralytics YOLOv8 models](https://github.com/ultralytics/ultralytics) using IBM Watsonx. It's a good option for enterprises interested in efficient [model training](../modes/train.md), fine-tuning for specific tasks, and improving [model performance](../guides/model-evaluation-insights.md) with robust tools and a user-friendly setup. In this guide, we'll walk you through the process of training YOLOv8 with IBM Watsonx, covering everything from setting up your environment to evaluating your trained models. Let's get started!
You can train [Ultralytics YOLO11 models](https://github.com/ultralytics/ultralytics) using IBM Watsonx. It's a good option for enterprises interested in efficient [model training](../modes/train.md), fine-tuning for specific tasks, and improving [model performance](../guides/model-evaluation-insights.md) with robust tools and a user-friendly setup. In this guide, we'll walk you through the process of training YOLO11 with IBM Watsonx, covering everything from setting up your environment to evaluating your trained models. Let's get started!
## What is IBM Watsonx?
@ -36,9 +36,9 @@ Watsonx.data supports both cloud and on-premises deployments through the IBM Sto
Watsonx.governance makes compliance easier by automatically identifying regulatory changes and enforcing policies. It links requirements to internal risk data and provides up-to-date AI factsheets. The platform helps manage risk with alerts and tools to detect issues such as [bias and drift](../guides/model-monitoring-and-maintenance.md). It also automates the monitoring and documentation of the AI lifecycle, organizes AI development with a model inventory, and enhances collaboration with user-friendly dashboards and reporting tools.
## How to Train YOLOv8 Using IBM Watsonx
## How to Train YOLO11 Using IBM Watsonx
You can use IBM Watsonx to accelerate your YOLOv8 model training workflow.
You can use IBM Watsonx to accelerate your YOLO11 model training workflow.
### Prerequisites
@ -67,7 +67,7 @@ Next, you can install and import the necessary Python libraries.
pip install ultralytics==8.0.196
```
For detailed instructions and best practices related to the installation process, check our [Ultralytics Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
For detailed instructions and best practices related to the installation process, check our [Ultralytics Installation guide](../quickstart.md). While installing the required packages for YOLO11, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
Then, you can import the needed packages.
@ -86,7 +86,7 @@ Then, you can import the needed packages.
### Step 3: Load the Data
For this tutorial, we will use a [marine litter dataset](https://www.kaggle.com/datasets/atiqishrak/trash-dataset-icra19) available on Kaggle. With this dataset, we will custom-train a YOLOv8 model to detect and classify litter and biological objects in underwater images.
For this tutorial, we will use a [marine litter dataset](https://www.kaggle.com/datasets/atiqishrak/trash-dataset-icra19) available on Kaggle. With this dataset, we will custom-train a YOLO11 model to detect and classify litter and biological objects in underwater images.
We can load the dataset directly into the notebook using the Kaggle API. First, create a free Kaggle account. Once you have created an account, you'll need to generate an API key. Directions for generating your key can be found in the [Kaggle API documentation](https://github.com/Kaggle/kaggle-api/blob/main/docs/README.md) under the section "API credentials".
@ -236,34 +236,34 @@ Run the following script to delete the current contents of config.yaml and repla
print(f"{file_path} updated successfully.")
```
### Step 5: Train the YOLOv8 model
### Step 5: Train the YOLO11 model
Run the following command-line code to fine tune a pretrained default YOLOv8 model.
Run the following command-line code to fine tune a pretrained default YOLO11 model.
!!! example "Train the YOLOv8 model"
!!! example "Train the YOLO11 model"
=== "CLI"
```bash
!yolo task=detect mode=train data={work_dir}/trash_ICRA19/config.yaml model=yolov8s.pt epochs=2 batch=32 lr0=.04 plots=True
!yolo task=detect mode=train data={work_dir}/trash_ICRA19/config.yaml model=yolo11n.pt epochs=2 batch=32 lr0=.04 plots=True
```
Here's a closer look at the parameters in the model training command:
- **task**: It specifies the [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) task for which you are using the specified YOLO model and data set.
- **mode**: Denotes the purpose for which you are loading the specified model and data. Since we are training a model, it is set to "train." Later, when we test our model's performance, we will set it to "predict."
- **epochs**: This delimits the number of times YOLOv8 will pass through our entire data set.
- **epochs**: This delimits the number of times YOLO11 will pass through our entire data set.
- **batch**: The numerical value stipulates the training [batch sizes](https://www.ultralytics.com/glossary/batch-size). Batches are the number of images a model processes before it updates its parameters.
- **lr0**: Specifies the model's initial [learning rate](https://www.ultralytics.com/glossary/learning-rate).
- **plots**: Directs YOLO to generate and save plots of our model's training and evaluation metrics.
For a detailed understanding of the model training process and best practices, refer to the [YOLOv8 Model Training guide](../modes/train.md). This guide will help you get the most out of your experiments and ensure you're using YOLOv8 effectively.
For a detailed understanding of the model training process and best practices, refer to the [YOLO11 Model Training guide](../modes/train.md). This guide will help you get the most out of your experiments and ensure you're using YOLO11 effectively.
### Step 6: Test the Model
We can now run inference to test the performance of our fine-tuned model:
!!! example "Test the YOLOv8 model"
!!! example "Test the YOLO11 model"
=== "CLI"
@ -312,11 +312,11 @@ Unlike precision, recall moves in the opposite direction, showing greater recall
### Step 8: Calculating [Intersection Over Union](https://www.ultralytics.com/glossary/intersection-over-union-iou)
You can measure the prediction [accuracy](https://www.ultralytics.com/glossary/accuracy) by calculating the IoU between a predicted bounding box and a ground truth bounding box for the same object. Check out [IBM's tutorial on training YOLOv8](https://developer.ibm.com/tutorials/awb-train-yolo-object-detection-model-in-python/) for more details.
You can measure the prediction [accuracy](https://www.ultralytics.com/glossary/accuracy) by calculating the IoU between a predicted bounding box and a ground truth bounding box for the same object. Check out [IBM's tutorial on training YOLO11](https://developer.ibm.com/tutorials/awb-train-yolo-object-detection-model-in-python/) for more details.
## Summary
We explored IBM Watsonx key features, and how to train a YOLOv8 model using IBM Watsonx. We also saw how IBM Watsonx can enhance your AI workflows with advanced tools for model building, data management, and compliance.
We explored IBM Watsonx key features, and how to train a YOLO11 model using IBM Watsonx. We also saw how IBM Watsonx can enhance your AI workflows with advanced tools for model building, data management, and compliance.
For further details on usage, visit [IBM Watsonx official documentation](https://www.ibm.com/watsonx).
@ -324,9 +324,9 @@ Also, be sure to check out the [Ultralytics integration guide page](./index.md),
## FAQ
### How do I train a YOLOv8 model using IBM Watsonx?
### How do I train a YOLO11 model using IBM Watsonx?
To train a YOLOv8 model using IBM Watsonx, follow these steps:
To train a YOLO11 model using IBM Watsonx, follow these steps:
1. **Set Up Your Environment**: Create an IBM Cloud account and set up a Watsonx.ai project. Use a Jupyter Notebook for your coding environment.
2. **Install Libraries**: Install necessary libraries like `torch`, `opencv`, and `ultralytics`.
@ -335,7 +335,7 @@ To train a YOLOv8 model using IBM Watsonx, follow these steps:
5. **Train the Model**: Use the YOLO command-line interface to train your model with specific parameters like `epochs`, `batch size`, and `learning rate`.
6. **Test and Evaluate**: Run inference to test the model and evaluate its performance using metrics like precision and recall.
For detailed instructions, refer to our [YOLOv8 Model Training guide](../modes/train.md).
For detailed instructions, refer to our [YOLO11 Model Training guide](../modes/train.md).
### What are the key features of IBM Watsonx for AI model training?
@ -347,20 +347,20 @@ IBM Watsonx offers several key features for AI model training:
For more information, visit the [IBM Watsonx official documentation](https://www.ibm.com/watsonx).
### Why should I use IBM Watsonx for training Ultralytics YOLOv8 models?
### Why should I use IBM Watsonx for training Ultralytics YOLO11 models?
IBM Watsonx is an excellent choice for training Ultralytics YOLOv8 models due to its comprehensive suite of tools that streamline the AI lifecycle. Key benefits include:
IBM Watsonx is an excellent choice for training Ultralytics YOLO11 models due to its comprehensive suite of tools that streamline the AI lifecycle. Key benefits include:
- **Scalability**: Easily scale your model training with IBM Cloud services.
- **Integration**: Seamlessly integrate with various data sources and APIs.
- **User-Friendly Interface**: Simplifies the development process with a collaborative and intuitive interface.
- **Advanced Tools**: Access to powerful tools like the Prompt Lab, Tuning Studio, and Flows Engine for enhancing model performance.
Learn more about [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) and how to train models using IBM Watsonx in our [integration guide](./index.md).
Learn more about [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) and how to train models using IBM Watsonx in our [integration guide](./index.md).
### How can I preprocess my dataset for YOLOv8 training on IBM Watsonx?
### How can I preprocess my dataset for YOLO11 training on IBM Watsonx?
To preprocess your dataset for YOLOv8 training on IBM Watsonx:
To preprocess your dataset for YOLO11 training on IBM Watsonx:
1. **Organize Directories**: Ensure your dataset follows the YOLO directory structure with separate subdirectories for images and labels within the train/val/test split.
2. **Update .yaml File**: Modify the `.yaml` configuration file to reflect the new directory structure and class names.
@ -399,9 +399,9 @@ if __name__ == "__main__":
For more details, refer to our [data preprocessing guide](../guides/preprocessing_annotated_data.md).
### What are the prerequisites for training a YOLOv8 model on IBM Watsonx?
### What are the prerequisites for training a YOLO11 model on IBM Watsonx?
Before you start training a YOLOv8 model on IBM Watsonx, ensure you have the following prerequisites:
Before you start training a YOLO11 model on IBM Watsonx, ensure you have the following prerequisites:
- **IBM Cloud Account**: Create an account on IBM Cloud to access Watsonx.ai.
- **Kaggle Account**: For loading datasets, you'll need a Kaggle account and an API key.

10
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View file

@ -18,7 +18,7 @@ Welcome to the Ultralytics Integrations page! This page provides an overview of
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Ultralytics YOLOv8 Deployment and Integrations
<strong>Watch:</strong> Ultralytics YOLO11 Deployment and Integrations
</p>
## Datasets Integrations
@ -47,7 +47,7 @@ Welcome to the Ultralytics Integrations page! This page provides an overview of
- [Amazon SageMaker](amazon-sagemaker.md): Leverage Amazon SageMaker to efficiently build, train, and deploy Ultralytics models, providing an all-in-one platform for the ML lifecycle.
- [Paperspace Gradient](paperspace.md): Paperspace Gradient simplifies working on YOLOv8 projects by providing easy-to-use cloud tools for training, testing, and deploying your models quickly.
- [Paperspace Gradient](paperspace.md): Paperspace Gradient simplifies working on YOLO11 projects by providing easy-to-use cloud tools for training, testing, and deploying your models quickly.
- [Google Colab](google-colab.md): Use Google Colab to train and evaluate Ultralytics models in a cloud-based environment that supports collaboration and sharing.
@ -111,7 +111,7 @@ Let's collaborate to make the Ultralytics YOLO ecosystem more expansive and feat
### What is Ultralytics HUB, and how does it streamline the ML workflow?
Ultralytics HUB is a cloud-based platform designed to make machine learning (ML) workflows for Ultralytics models seamless and efficient. By using this tool, you can easily upload datasets, train models, perform real-time tracking, and deploy YOLOv8 models without needing extensive coding skills. You can explore the key features on the [Ultralytics HUB](https://hub.ultralytics.com/) page and get started quickly with our [Quickstart](https://docs.ultralytics.com/hub/quickstart/) guide.
Ultralytics HUB is a cloud-based platform designed to make machine learning (ML) workflows for Ultralytics models seamless and efficient. By using this tool, you can easily upload datasets, train models, perform real-time tracking, and deploy YOLO11 models without needing extensive coding skills. You can explore the key features on the [Ultralytics HUB](https://hub.ultralytics.com/) page and get started quickly with our [Quickstart](https://docs.ultralytics.com/hub/quickstart/) guide.
### How do I integrate Ultralytics YOLO models with Roboflow for dataset management?
@ -121,9 +121,9 @@ Integrating Ultralytics YOLO models with Roboflow enhances dataset management by
Yes, you can. Integrating MLFlow with Ultralytics models allows you to track experiments, improve reproducibility, and streamline the entire ML lifecycle. Detailed instructions for setting up this integration can be found on the [MLFlow](mlflow.md) integration page. This integration is particularly useful for monitoring model metrics and managing the ML workflow efficiently.
### What are the benefits of using Neural Magic for YOLOv8 model optimization?
### What are the benefits of using Neural Magic for YOLO11 model optimization?
Neural Magic optimizes YOLOv8 models by leveraging techniques like Quantization Aware Training (QAT) and pruning, resulting in highly efficient, smaller models that perform better on resource-limited hardware. Check out the [Neural Magic](neural-magic.md) integration page to learn how to implement these optimizations for superior performance and leaner models. This is especially beneficial for deployment on edge devices.
Neural Magic optimizes YOLO11 models by leveraging techniques like Quantization Aware Training (QAT) and pruning, resulting in highly efficient, smaller models that perform better on resource-limited hardware. Check out the [Neural Magic](neural-magic.md) integration page to learn how to implement these optimizations for superior performance and leaner models. This is especially beneficial for deployment on edge devices.
### How do I deploy Ultralytics YOLO models with Gradio for interactive demos?

58
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@ -1,14 +1,14 @@
---
comments: true
description: Explore our integration guide that explains how you can use JupyterLab to train a YOLOv8 model. We'll also cover key features and tips for common issues.
keywords: JupyterLab, What is JupyterLab, How to Use JupyterLab, JupyterLab How to Use, YOLOv8, Ultralytics, Model Training, GPU, TPU, cloud computing
description: Explore our integration guide that explains how you can use JupyterLab to train a YOLO11 model. We'll also cover key features and tips for common issues.
keywords: JupyterLab, What is JupyterLab, How to Use JupyterLab, JupyterLab How to Use, YOLO11, Ultralytics, Model Training, GPU, TPU, cloud computing
---
# A Guide on How to Use JupyterLab to Train Your YOLOv8 Models
# A Guide on How to Use JupyterLab to Train Your YOLO11 Models
Building [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models can be tough, especially when you don't have the right tools or environment to work with. If you are facing this issue, JupyterLab might be the right solution for you. JupyterLab is a user-friendly, web-based platform that makes coding more flexible and interactive. You can use it to handle big datasets, create complex models, and even collaborate with others, all in one place.
You can use JupyterLab to [work on projects](../guides/steps-of-a-cv-project.md) related to [Ultralytics YOLOv8 models](https://github.com/ultralytics/ultralytics). JupyterLab is a great option for efficient model development and experimentation. It makes it easy to start experimenting with and [training YOLOv8 models](../modes/train.md) right from your computer. Let's dive deeper into JupyterLab, its key features, and how you can use it to train YOLOv8 models.
You can use JupyterLab to [work on projects](../guides/steps-of-a-cv-project.md) related to [Ultralytics YOLO11 models](https://github.com/ultralytics/ultralytics). JupyterLab is a great option for efficient model development and experimentation. It makes it easy to start experimenting with and [training YOLO11 models](../modes/train.md) right from your computer. Let's dive deeper into JupyterLab, its key features, and how you can use it to train YOLO11 models.
## What is JupyterLab?
@ -26,7 +26,7 @@ Here are some of the key features that make JupyterLab a great option for model
- **Markdown Preview**: Working with Markdown files is more efficient in JupyterLab, thanks to its simultaneous preview feature. As you write or edit your Markdown file, you can see the formatted output in real-time. It makes it easier to double-check that your documentation looks perfect, saving you from having to switch back and forth between editing and preview modes.
- **Run Code from Text Files**: If you're sharing a text file with code, JupyterLab makes it easy to run it directly within the platform. You can highlight the code and press Shift + Enter to execute it. It is great for verifying code snippets quickly and helps guarantee that the code you share is functional and error-free.
## Why Should You Use JupyterLab for Your YOLOv8 Projects?
## Why Should You Use JupyterLab for Your YOLO11 Projects?
There are multiple platforms for developing and evaluating machine learning models, so what makes JupyterLab stand out? Let's explore some of the unique aspects that JupyterLab offers for your machine-learning projects:
@ -46,9 +46,9 @@ When working with Kaggle, you might come across some common issues. Here are som
- **Installing JupyterLab Extensions**: JupyterLab supports various extensions to enhance functionality. You can install and customize these extensions to suit your needs. For detailed instructions, refer to [JupyterLab Extensions Guide](https://jupyterlab.readthedocs.io/en/latest/user/extensions.html) for more information.
- **Using Multiple Versions of Python**: If you need to work with different versions of Python, you can use Jupyter kernels configured with different Python versions.
## How to Use JupyterLab to Try Out YOLOv8
## How to Use JupyterLab to Try Out YOLO11
JupyterLab makes it easy to experiment with YOLOv8. To get started, follow these simple steps.
JupyterLab makes it easy to experiment with YOLO11. To get started, follow these simple steps.
### Step 1: Install JupyterLab
@ -63,7 +63,7 @@ First, you need to install JupyterLab. Open your terminal and run the command:
pip install jupyterlab
```
### Step 2: Download the YOLOv8 Tutorial Notebook
### Step 2: Download the YOLO11 Tutorial Notebook
Next, download the [tutorial.ipynb](https://github.com/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb) file from the Ultralytics GitHub repository. Save this file to any directory on your local machine.
@ -85,13 +85,13 @@ Once you've run this command, it will open JupyterLab in your default web browse
### Step 4: Start Experimenting
In JupyterLab, open the tutorial.ipynb notebook. You can now start running the cells to explore and experiment with YOLOv8.
In JupyterLab, open the tutorial.ipynb notebook. You can now start running the cells to explore and experiment with YOLO11.
![Image Showing Opened YOLOv8 Notebook in JupyterLab](https://github.com/ultralytics/docs/releases/download/0/opened-yolov8-notebook-jupyterlab.avif)
![Image Showing Opened YOLO11 Notebook in JupyterLab](https://github.com/ultralytics/docs/releases/download/0/opened-yolov8-notebook-jupyterlab.avif)
JupyterLab's interactive environment allows you to modify code, visualize outputs, and document your findings all in one place. You can try out different configurations and understand how YOLOv8 works.
JupyterLab's interactive environment allows you to modify code, visualize outputs, and document your findings all in one place. You can try out different configurations and understand how YOLO11 works.
For a detailed understanding of the model training process and best practices, refer to the [YOLOv8 Model Training guide](../modes/train.md). This guide will help you get the most out of your experiments and ensure you're using YOLOv8 effectively.
For a detailed understanding of the model training process and best practices, refer to the [YOLO11 Model Training guide](../modes/train.md). This guide will help you get the most out of your experiments and ensure you're using YOLO11 effectively.
## Keep Learning about Jupyterlab
@ -103,17 +103,17 @@ If you're excited to learn more about JupyterLab, here are some great resources
## Summary
We've explored how JupyterLab can be a powerful tool for experimenting with Ultralytics YOLOv8 models. Using its flexible and interactive environment, you can easily set up JupyterLab on your local machine and start working with YOLOv8. JupyterLab makes it simple to [train](../guides/model-training-tips.md) and [evaluate](../guides/model-testing.md) your models, visualize outputs, and [document your findings](../guides/model-monitoring-and-maintenance.md) all in one place.
We've explored how JupyterLab can be a powerful tool for experimenting with Ultralytics YOLO11 models. Using its flexible and interactive environment, you can easily set up JupyterLab on your local machine and start working with YOLO11. JupyterLab makes it simple to [train](../guides/model-training-tips.md) and [evaluate](../guides/model-testing.md) your models, visualize outputs, and [document your findings](../guides/model-monitoring-and-maintenance.md) all in one place.
For more details, visit the [JupyterLab FAQ Page](https://jupyterlab.readthedocs.io/en/stable/getting_started/faq.html).
Interested in more YOLOv8 integrations? Check out the [Ultralytics integration guide](./index.md) to explore additional tools and capabilities for your machine learning projects.
Interested in more YOLO11 integrations? Check out the [Ultralytics integration guide](./index.md) to explore additional tools and capabilities for your machine learning projects.
## FAQ
### How do I use JupyterLab to train a YOLOv8 model?
### How do I use JupyterLab to train a YOLO11 model?
To train a YOLOv8 model using JupyterLab:
To train a YOLO11 model using JupyterLab:
1. Install JupyterLab and the Ultralytics package:
@ -128,7 +128,7 @@ To train a YOLOv8 model using JupyterLab:
```python
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
```
4. Train the model on your custom dataset:
@ -147,22 +147,22 @@ To train a YOLOv8 model using JupyterLab:
JupyterLab's interactive environment allows you to easily modify parameters, visualize results, and iterate on your model training process.
### What are the key features of JupyterLab that make it suitable for YOLOv8 projects?
### What are the key features of JupyterLab that make it suitable for YOLO11 projects?
JupyterLab offers several features that make it ideal for YOLOv8 projects:
JupyterLab offers several features that make it ideal for YOLO11 projects:
1. Interactive code execution: Test and debug YOLOv8 code snippets in real-time.
1. Interactive code execution: Test and debug YOLO11 code snippets in real-time.
2. Integrated file browser: Easily manage datasets, model weights, and configuration files.
3. Flexible layout: Arrange multiple notebooks, terminals, and output windows side-by-side for efficient workflow.
4. Rich output display: Visualize YOLOv8 detection results, training curves, and model performance metrics inline.
5. Markdown support: Document your YOLOv8 experiments and findings with rich text and images.
4. Rich output display: Visualize YOLO11 detection results, training curves, and model performance metrics inline.
5. Markdown support: Document your YOLO11 experiments and findings with rich text and images.
6. Extension ecosystem: Enhance functionality with extensions for version control, [remote computing](google-colab.md), and more.
These features allow for a seamless development experience when working with YOLOv8 models, from data preparation to [model deployment](https://www.ultralytics.com/glossary/model-deployment).
These features allow for a seamless development experience when working with YOLO11 models, from data preparation to [model deployment](https://www.ultralytics.com/glossary/model-deployment).
### How can I optimize YOLOv8 model performance using JupyterLab?
### How can I optimize YOLO11 model performance using JupyterLab?
To optimize YOLOv8 model performance in JupyterLab:
To optimize YOLO11 model performance in JupyterLab:
1. Use the autobatch feature to determine the optimal batch size:
@ -190,11 +190,11 @@ To optimize YOLOv8 model performance in JupyterLab:
4. Experiment with different model architectures and [export formats](../modes/export.md) to find the best balance of speed and [accuracy](https://www.ultralytics.com/glossary/accuracy) for your specific use case.
JupyterLab's interactive environment allows for quick iterations and real-time feedback, making it easier to optimize your YOLOv8 models efficiently.
JupyterLab's interactive environment allows for quick iterations and real-time feedback, making it easier to optimize your YOLO11 models efficiently.
### How do I handle common issues when working with JupyterLab and YOLOv8?
### How do I handle common issues when working with JupyterLab and YOLO11?
When working with JupyterLab and YOLOv8, you might encounter some common issues. Here's how to handle them:
When working with JupyterLab and YOLO11, you might encounter some common issues. Here's how to handle them:
1. GPU memory issues:
@ -203,7 +203,7 @@ When working with JupyterLab and YOLOv8, you might encounter some common issues.
2. Package conflicts:
- Create a separate conda environment for your YOLOv8 projects to avoid conflicts.
- Create a separate conda environment for your YOLO11 projects to avoid conflicts.
- Use `!pip install package_name` in a notebook cell to install missing packages.
3. Kernel crashes:

46
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@ -1,14 +1,14 @@
---
comments: true
description: Dive into our guide on YOLOv8's integration with Kaggle. Find out what Kaggle is, its key features, and how to train a YOLOv8 model using the integration.
keywords: What is Kaggle, What is Kaggle Used For, YOLOv8, Kaggle Machine Learning, Model Training, GPU, TPU, cloud computing
description: Dive into our guide on YOLO11's integration with Kaggle. Find out what Kaggle is, its key features, and how to train a YOLO11 model using the integration.
keywords: What is Kaggle, What is Kaggle Used For, YOLO11, Kaggle Machine Learning, Model Training, GPU, TPU, cloud computing
---
# A Guide on Using Kaggle to Train Your YOLOv8 Models
# A Guide on Using Kaggle to Train Your YOLO11 Models
If you are learning about AI and working on [small projects](../solutions/index.md), you might not have access to powerful computing resources yet, and high-end hardware can be pretty expensive. Fortunately, Kaggle, a platform owned by Google, offers a great solution. Kaggle provides a free, cloud-based environment where you can access GPU resources, handle large datasets, and collaborate with a diverse community of data scientists and [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) enthusiasts.
Kaggle is a great choice for [training](../guides/model-training-tips.md) and experimenting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics?tab=readme-ov-file) models. Kaggle Notebooks make using popular machine-learning libraries and frameworks in your projects easy. Let's explore Kaggle's main features and learn how you can train YOLOv8 models on this platform!
Kaggle is a great choice for [training](../guides/model-training-tips.md) and experimenting with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics?tab=readme-ov-file) models. Kaggle Notebooks make using popular machine-learning libraries and frameworks in your projects easy. Let's explore Kaggle's main features and learn how you can train YOLO11 models on this platform!
## What is Kaggle?
@ -16,21 +16,21 @@ Kaggle is a platform that brings together data scientists from around the world
With more than [10 million users](https://www.kaggle.com/discussions/general/332147) as of 2022, Kaggle provides a rich environment for developing and experimenting with machine learning models. You don't need to worry about your local machine's specs or setup; you can dive right in with just a Kaggle account and a web browser.
## Training YOLOv8 Using Kaggle
## Training YOLO11 Using Kaggle
Training YOLOv8 models on Kaggle is simple and efficient, thanks to the platform's access to powerful GPUs.
Training YOLO11 models on Kaggle is simple and efficient, thanks to the platform's access to powerful GPUs.
To get started, access the [Kaggle YOLOv8 Notebook](https://www.kaggle.com/code/ultralytics/yolov8). Kaggle's environment comes with pre-installed libraries like [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) and [PyTorch](https://www.ultralytics.com/glossary/pytorch), making the setup process hassle-free.
To get started, access the [Kaggle YOLO11 Notebook](https://www.kaggle.com/code/ultralytics/yolov8). Kaggle's environment comes with pre-installed libraries like [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) and [PyTorch](https://www.ultralytics.com/glossary/pytorch), making the setup process hassle-free.
![What is the kaggle integration with respect to YOLOv8?](https://github.com/ultralytics/docs/releases/download/0/kaggle-integration-yolov8.avif)
![What is the kaggle integration with respect to YOLO11?](https://github.com/ultralytics/docs/releases/download/0/kaggle-integration-yolov8.avif)
Once you sign in to your Kaggle account, you can click on the option to copy and edit the code, select a GPU under the accelerator settings, and run the notebook's cells to begin training your model. For a detailed understanding of the model training process and best practices, refer to our [YOLOv8 Model Training guide](../modes/train.md).
Once you sign in to your Kaggle account, you can click on the option to copy and edit the code, select a GPU under the accelerator settings, and run the notebook's cells to begin training your model. For a detailed understanding of the model training process and best practices, refer to our [YOLO11 Model Training guide](../modes/train.md).
![Using kaggle for machine learning model training with a GPU](https://github.com/ultralytics/docs/releases/download/0/using-kaggle-for-machine-learning-model-training-with-a-gpu.avif)
On the [official YOLOv8 Kaggle notebook page](https://www.kaggle.com/code/ultralytics/yolov8), if you click on the three dots in the upper right-hand corner, you'll notice more options will pop up.
On the [official YOLO11 Kaggle notebook page](https://www.kaggle.com/code/ultralytics/yolov8), if you click on the three dots in the upper right-hand corner, you'll notice more options will pop up.
![Overview of Options From the Official YOLOv8 Kaggle Notebook Page](https://github.com/ultralytics/docs/releases/download/0/overview-options-yolov8-kaggle-notebook.avif)
![Overview of Options From the Official YOLO11 Kaggle Notebook Page](https://github.com/ultralytics/docs/releases/download/0/overview-options-yolov8-kaggle-notebook.avif)
These options include:
@ -59,17 +59,17 @@ When working with Kaggle, you might come across some common issues. Here are som
Next, let's understand the features Kaggle offers that make it an excellent platform for data science and machine learning enthusiasts. Here are some of the key highlights:
- **Datasets**: Kaggle hosts a massive collection of datasets on various topics. You can easily search and use these datasets in your projects, which is particularly handy for training and testing your YOLOv8 models.
- **Datasets**: Kaggle hosts a massive collection of datasets on various topics. You can easily search and use these datasets in your projects, which is particularly handy for training and testing your YOLO11 models.
- **Competitions**: Known for its exciting competitions, Kaggle allows data scientists and machine learning enthusiasts to solve real-world problems. Competing helps you improve your skills, learn new techniques, and gain recognition in the community.
- **Free Access to TPUs**: Kaggle provides free access to powerful TPUs, which are essential for training complex machine learning models. This means you can speed up processing and boost the performance of your YOLOv8 projects without incurring extra costs.
- **Free Access to TPUs**: Kaggle provides free access to powerful TPUs, which are essential for training complex machine learning models. This means you can speed up processing and boost the performance of your YOLO11 projects without incurring extra costs.
- **Integration with Github**: Kaggle allows you to easily connect your GitHub repository to upload notebooks and save your work. This integration makes it convenient to manage and access your files.
- **Community and Discussions**: Kaggle boasts a strong community of data scientists and machine learning practitioners. The discussion forums and shared notebooks are fantastic resources for learning and troubleshooting. You can easily find help, share your knowledge, and collaborate with others.
## Why Should You Use Kaggle for Your YOLOv8 Projects?
## Why Should You Use Kaggle for Your YOLO11 Projects?
There are multiple platforms for training and evaluating machine learning models, so what makes Kaggle stand out? Let's dive into the benefits of using Kaggle for your machine-learning projects:
- **Public Notebooks**: You can make your Kaggle notebooks public, allowing other users to view, vote, fork, and discuss your work. Kaggle promotes collaboration, feedback, and the sharing of ideas, helping you improve your YOLOv8 models.
- **Public Notebooks**: You can make your Kaggle notebooks public, allowing other users to view, vote, fork, and discuss your work. Kaggle promotes collaboration, feedback, and the sharing of ideas, helping you improve your YOLO11 models.
- **Comprehensive History of Notebook Commits**: Kaggle creates a detailed history of your notebook commits. This allows you to review and track changes over time, making it easier to understand the evolution of your project and revert to previous versions if needed.
- **Console Access**: Kaggle provides a console, giving you more control over your environment. This feature allows you to perform various tasks directly from the command line, enhancing your workflow and productivity.
- **Resource Availability**: Each notebook editing session on Kaggle is provided with significant resources: 12 hours of execution time for CPU and GPU sessions, 9 hours of execution time for TPU sessions, and 20 gigabytes of auto-saved disk space.
@ -85,21 +85,21 @@ If you want to learn more about Kaggle, here are some helpful resources to guide
## Summary
We've seen how Kaggle can boost your YOLOv8 projects by providing free access to powerful GPUs, making model training and evaluation efficient. Kaggle's platform is user-friendly, with pre-installed libraries for quick setup.
We've seen how Kaggle can boost your YOLO11 projects by providing free access to powerful GPUs, making model training and evaluation efficient. Kaggle's platform is user-friendly, with pre-installed libraries for quick setup.
For more details, visit [Kaggle's documentation](https://www.kaggle.com/docs).
Interested in more YOLOv8 integrations? Check out the[ Ultralytics integration guide](https://docs.ultralytics.com/integrations/) to explore additional tools and capabilities for your machine learning projects.
Interested in more YOLO11 integrations? Check out the[ Ultralytics integration guide](https://docs.ultralytics.com/integrations/) to explore additional tools and capabilities for your machine learning projects.
## FAQ
### How do I train a YOLOv8 model on Kaggle?
### How do I train a YOLO11 model on Kaggle?
Training a YOLOv8 model on Kaggle is straightforward. First, access the [Kaggle YOLOv8 Notebook](https://www.kaggle.com/ultralytics/yolov8). Sign in to your Kaggle account, copy and edit the notebook, and select a GPU under the accelerator settings. Run the notebook cells to start training. For more detailed steps, refer to our [YOLOv8 Model Training guide](../modes/train.md).
Training a YOLO11 model on Kaggle is straightforward. First, access the [Kaggle YOLO11 Notebook](https://www.kaggle.com/ultralytics/yolov8). Sign in to your Kaggle account, copy and edit the notebook, and select a GPU under the accelerator settings. Run the notebook cells to start training. For more detailed steps, refer to our [YOLO11 Model Training guide](../modes/train.md).
### What are the benefits of using Kaggle for YOLOv8 model training?
### What are the benefits of using Kaggle for YOLO11 model training?
Kaggle offers several advantages for training YOLOv8 models:
Kaggle offers several advantages for training YOLO11 models:
- **Free GPU Access**: Utilize powerful GPUs like Nvidia Tesla P100 or T4 x2 for up to 30 hours per week.
- **Pre-installed Libraries**: Libraries like TensorFlow and PyTorch are pre-installed, simplifying the setup.
@ -108,7 +108,7 @@ Kaggle offers several advantages for training YOLOv8 models:
For more details, visit our [Ultralytics integration guide](https://docs.ultralytics.com/integrations/).
### What common issues might I encounter when using Kaggle for YOLOv8, and how can I resolve them?
### What common issues might I encounter when using Kaggle for YOLO11, and how can I resolve them?
Common issues include:
@ -119,7 +119,7 @@ Common issues include:
For more troubleshooting tips, see our [Common Issues guide](../guides/yolo-common-issues.md).
### Why should I choose Kaggle over other platforms like Google Colab for training YOLOv8 models?
### Why should I choose Kaggle over other platforms like Google Colab for training YOLO11 models?
Kaggle offers unique features that make it an excellent choice:

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@ -1,14 +1,14 @@
---
comments: true
description: Optimize YOLOv8 models for mobile and embedded devices by exporting to NCNN format. Enhance performance in resource-constrained environments.
keywords: Ultralytics, YOLOv8, NCNN, model export, machine learning, deployment, mobile, embedded systems, deep learning, AI models
description: Optimize YOLO11 models for mobile and embedded devices by exporting to NCNN format. Enhance performance in resource-constrained environments.
keywords: Ultralytics, YOLO11, NCNN, model export, machine learning, deployment, mobile, embedded systems, deep learning, AI models
---
# How to Export to NCNN from YOLOv8 for Smooth Deployment
# How to Export to NCNN from YOLO11 for Smooth Deployment
Deploying [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models on devices with limited computational power, such as mobile or embedded systems, can be tricky. You need to make sure you use a format optimized for optimal performance. This makes sure that even devices with limited processing power can handle advanced computer vision tasks well.
The export to NCNN format feature allows you to optimize your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models for lightweight device-based applications. In this guide, we'll walk you through how to convert your models to the NCNN format, making it easier for your models to perform well on various mobile and embedded devices.
The export to NCNN format feature allows you to optimize your [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) models for lightweight device-based applications. In this guide, we'll walk you through how to convert your models to the NCNN format, making it easier for your models to perform well on various mobile and embedded devices.
## Why should you export to NCNN?
@ -34,7 +34,7 @@ NCNN models offer a wide range of key features that enable on-device [machine le
## Deployment Options with NCNN
Before we look at the code for exporting YOLOv8 models to the NCNN format, let's understand how NCNN models are normally used.
Before we look at the code for exporting YOLO11 models to the NCNN format, let's understand how NCNN models are normally used.
NCNN models, designed for efficiency and performance, are compatible with a variety of deployment platforms:
@ -44,9 +44,9 @@ NCNN models, designed for efficiency and performance, are compatible with a vari
- **Desktop and Server Deployment**: Capable of being deployed in desktop and server environments across Linux, Windows, and macOS, supporting development, training, and evaluation with higher computational capacities.
## Export to NCNN: Converting Your YOLOv8 Model
## Export to NCNN: Converting Your YOLO11 Model
You can expand model compatibility and deployment flexibility by converting YOLOv8 models to NCNN format.
You can expand model compatibility and deployment flexibility by converting YOLO11 models to NCNN format.
### Installation
@ -57,15 +57,15 @@ To install the required packages, run:
=== "CLI"
```bash
# Install the required package for YOLOv8
# Install the required package for YOLO11
pip install ultralytics
```
For detailed instructions and best practices related to the installation process, check our [Ultralytics Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
For detailed instructions and best practices related to the installation process, check our [Ultralytics Installation guide](../quickstart.md). While installing the required packages for YOLO11, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
### Usage
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).
Before diving into the usage instructions, it's important to note that while all [Ultralytics YOLO11 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"
@ -74,14 +74,14 @@ Before diving into the usage instructions, it's important to note that while all
```python
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO("yolov8n.pt")
# Load the YOLO11 model
model = YOLO("yolo11n.pt")
# Export the model to NCNN format
model.export(format="ncnn") # creates '/yolov8n_ncnn_model'
model.export(format="ncnn") # creates '/yolo11n_ncnn_model'
# Load the exported NCNN model
ncnn_model = YOLO("./yolov8n_ncnn_model")
ncnn_model = YOLO("./yolo11n_ncnn_model")
# Run inference
results = ncnn_model("https://ultralytics.com/images/bus.jpg")
@ -90,18 +90,18 @@ Before diving into the usage instructions, it's important to note that while all
=== "CLI"
```bash
# Export a YOLOv8n PyTorch model to NCNN format
yolo export model=yolov8n.pt format=ncnn # creates '/yolov8n_ncnn_model'
# Export a YOLO11n PyTorch model to NCNN format
yolo export model=yolo11n.pt format=ncnn # creates '/yolo11n_ncnn_model'
# Run inference with the exported model
yolo predict model='./yolov8n_ncnn_model' source='https://ultralytics.com/images/bus.jpg'
yolo predict model='./yolo11n_ncnn_model' source='https://ultralytics.com/images/bus.jpg'
```
For more details about supported export options, visit the [Ultralytics documentation page on deployment options](../guides/model-deployment-options.md).
## Deploying Exported YOLOv8 NCNN Models
## Deploying Exported YOLO11 NCNN Models
After successfully exporting your Ultralytics YOLOv8 models to NCNN format, you can now deploy them. The primary and recommended first step for running a NCNN model is to utilize the YOLO("./model_ncnn_model") method, as outlined in the previous usage code snippet. However, for in-depth instructions on deploying your NCNN models in various other settings, take a look at the following resources:
After successfully exporting your Ultralytics YOLO11 models to NCNN format, you can now deploy them. The primary and recommended first step for running a NCNN model is to utilize the YOLO("./model_ncnn_model") method, as outlined in the previous usage code snippet. However, for in-depth instructions on deploying your NCNN models in various other settings, take a look at the following resources:
- **[Android](https://github.com/Tencent/ncnn/wiki/how-to-build#build-for-android)**: This blog explains how to use NCNN models for performing tasks like [object detection](https://www.ultralytics.com/glossary/object-detection) through Android applications.
@ -113,40 +113,40 @@ After successfully exporting your Ultralytics YOLOv8 models to NCNN format, you
## Summary
In this guide, we've gone over exporting Ultralytics YOLOv8 models to the NCNN format. This conversion step is crucial for improving the efficiency and speed of YOLOv8 models, making them more effective and suitable for limited-resource computing environments.
In this guide, we've gone over exporting Ultralytics YOLO11 models to the NCNN format. This conversion step is crucial for improving the efficiency and speed of YOLO11 models, making them more effective and suitable for limited-resource computing environments.
For detailed instructions on usage, please refer to the [official NCNN documentation](https://ncnn.readthedocs.io/en/latest/index.html).
Also, if you're interested in exploring other integration options for Ultralytics YOLOv8, be sure to visit our [integration guide page](index.md) for further insights and information.
Also, if you're interested in exploring other integration options for Ultralytics YOLO11, be sure to visit our [integration guide page](index.md) for further insights and information.
## FAQ
### How do I export Ultralytics YOLOv8 models to NCNN format?
### How do I export Ultralytics YOLO11 models to NCNN format?
To export your Ultralytics YOLOv8 model to NCNN format, follow these steps:
To export your Ultralytics YOLO11 model to NCNN format, follow these steps:
- **Python**: Use the `export` function from the YOLO class.
```python
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO("yolov8n.pt")
# Load the YOLO11 model
model = YOLO("yolo11n.pt")
# Export to NCNN format
model.export(format="ncnn") # creates '/yolov8n_ncnn_model'
model.export(format="ncnn") # creates '/yolo11n_ncnn_model'
```
- **CLI**: Use the `yolo` command with the `export` argument.
```bash
yolo export model=yolov8n.pt format=ncnn # creates '/yolov8n_ncnn_model'
yolo export model=yolo11n.pt format=ncnn # creates '/yolo11n_ncnn_model'
```
For detailed export options, check the [Export](../modes/export.md) page in the documentation.
### What are the advantages of exporting YOLOv8 models to NCNN?
### What are the advantages of exporting YOLO11 models to NCNN?
Exporting your Ultralytics YOLOv8 models to NCNN offers several benefits:
Exporting your Ultralytics YOLO11 models to NCNN offers several benefits:
- **Efficiency**: NCNN models are optimized for mobile and embedded devices, ensuring high performance even with limited computational resources.
- **Quantization**: NCNN supports techniques like quantization that improve model speed and reduce memory usage.
@ -174,13 +174,13 @@ NCNN is versatile and supports various platforms:
If running models on a Raspberry Pi isn't fast enough, converting to the NCNN format could speed things up as detailed in our [Raspberry Pi Guide](../guides/raspberry-pi.md).
### How can I deploy Ultralytics YOLOv8 NCNN models on Android?
### How can I deploy Ultralytics YOLO11 NCNN models on Android?
To deploy your YOLOv8 models on Android:
To deploy your YOLO11 models on Android:
1. **Build for Android**: Follow the [NCNN Build for Android](https://github.com/Tencent/ncnn/wiki/how-to-build#build-for-android) guide.
2. **Integrate with Your App**: Use the NCNN Android SDK to integrate the exported model into your application for efficient on-device inference.
For step-by-step instructions, refer to our guide on [Deploying YOLOv8 NCNN Models](#deploying-exported-yolov8-ncnn-models).
For step-by-step instructions, refer to our guide on [Deploying YOLO11 NCNN Models](#deploying-exported-yolo11-ncnn-models).
For more advanced guides and use cases, visit the [Ultralytics documentation page](../guides/model-deployment-options.md).

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@ -1,14 +1,14 @@
---
comments: true
description: Enhance YOLOv8 performance using Neural Magic's DeepSparse Engine. Learn how to deploy and benchmark YOLOv8 models on CPUs for efficient object detection.
keywords: YOLOv8, DeepSparse, Neural Magic, model optimization, object detection, inference speed, CPU performance, sparsity, pruning, quantization
description: Enhance YOLO11 performance using Neural Magic's DeepSparse Engine. Learn how to deploy and benchmark YOLO11 models on CPUs for efficient object detection.
keywords: YOLO11, DeepSparse, Neural Magic, model optimization, object detection, inference speed, CPU performance, sparsity, pruning, quantization
---
# Optimizing YOLOv8 Inferences with Neural Magic's DeepSparse Engine
# Optimizing YOLO11 Inferences with Neural Magic's DeepSparse Engine
When deploying [object detection](https://www.ultralytics.com/glossary/object-detection) models like [Ultralytics YOLOv8](https://www.ultralytics.com/) on various hardware, you can bump into unique issues like optimization. This is where YOLOv8's integration with Neural Magic's DeepSparse Engine steps in. It transforms the way YOLOv8 models are executed and enables GPU-level performance directly on CPUs.
When deploying [object detection](https://www.ultralytics.com/glossary/object-detection) models like [Ultralytics YOLO11](https://www.ultralytics.com/) on various hardware, you can bump into unique issues like optimization. This is where YOLO11's integration with Neural Magic's DeepSparse Engine steps in. It transforms the way YOLO11 models are executed and enables GPU-level performance directly on CPUs.
This guide shows you how to deploy YOLOv8 using Neural Magic's DeepSparse, how to run inferences, and also how to benchmark performance to ensure it is optimized.
This guide shows you how to deploy YOLO11 using Neural Magic's DeepSparse, how to run inferences, and also how to benchmark performance to ensure it is optimized.
## Neural Magic's DeepSparse
@ -18,17 +18,17 @@ This guide shows you how to deploy YOLOv8 using Neural Magic's DeepSparse, how t
[Neural Magic's DeepSparse](https://neuralmagic.com/deepsparse/) is an inference run-time designed to optimize the execution of neural networks on CPUs. It applies advanced techniques like sparsity, pruning, and quantization to dramatically reduce computational demands while maintaining accuracy. DeepSparse offers an agile solution for efficient and scalable [neural network](https://www.ultralytics.com/glossary/neural-network-nn) execution across various devices.
## Benefits of Integrating Neural Magic's DeepSparse with YOLOv8
## Benefits of Integrating Neural Magic's DeepSparse with YOLO11
Before diving into how to deploy YOLOV8 using DeepSparse, let's understand the benefits of using DeepSparse. Some key advantages include:
- **Enhanced Inference Speed**: Achieves up to 525 FPS (on YOLOv8n), significantly speeding up YOLOv8's inference capabilities compared to traditional methods.
- **Enhanced Inference Speed**: Achieves up to 525 FPS (on YOLO11n), significantly speeding up YOLO11's inference capabilities compared to traditional methods.
<p align="center">
<img width="640" src="https://github.com/ultralytics/docs/releases/download/0/enhanced-inference-speed.avif" alt="Enhanced Inference Speed">
</p>
- **Optimized Model Efficiency**: Uses pruning and quantization to enhance YOLOv8's efficiency, reducing model size and computational requirements while maintaining [accuracy](https://www.ultralytics.com/glossary/accuracy).
- **Optimized Model Efficiency**: Uses pruning and quantization to enhance YOLO11's efficiency, reducing model size and computational requirements while maintaining [accuracy](https://www.ultralytics.com/glossary/accuracy).
<p align="center">
<img width="640" src="https://github.com/ultralytics/docs/releases/download/0/optimized-model-efficiency.avif" alt="Optimized Model Efficiency">
@ -36,9 +36,9 @@ Before diving into how to deploy YOLOV8 using DeepSparse, let's understand the b
- **High Performance on Standard CPUs**: Delivers GPU-like performance on CPUs, providing a more accessible and cost-effective option for various applications.
- **Streamlined Integration and Deployment**: Offers user-friendly tools for easy integration of YOLOv8 into applications, including image and video annotation features.
- **Streamlined Integration and Deployment**: Offers user-friendly tools for easy integration of YOLO11 into applications, including image and video annotation features.
- **Support for Various Model Types**: Compatible with both standard and sparsity-optimized YOLOv8 models, adding deployment flexibility.
- **Support for Various Model Types**: Compatible with both standard and sparsity-optimized YOLO11 models, adding deployment flexibility.
- **Cost-Effective and Scalable Solution**: Reduces operational expenses and offers scalable deployment of advanced object detection models.
@ -56,15 +56,15 @@ Neural Magic's Deep Sparse technology is inspired by the human brain's efficienc
For more details on how Neural Magic's DeepSparse technology work, check out [their blog post](https://neuralmagic.com/blog/how-neural-magics-deep-sparse-technology-works/).
## Creating A Sparse Version of YOLOv8 Trained on a Custom Dataset
## Creating A Sparse Version of YOLO11 Trained on a Custom Dataset
SparseZoo, an open-source model repository by Neural Magic, offers [a collection of pre-sparsified YOLOv8 model checkpoints](https://sparsezoo.neuralmagic.com/?modelSet=computer_vision&searchModels=yolo). With SparseML, seamlessly integrated with Ultralytics, users can effortlessly fine-tune these sparse checkpoints on their specific datasets using a straightforward command-line interface.
SparseZoo, an open-source model repository by Neural Magic, offers [a collection of pre-sparsified YOLO11 model checkpoints](https://sparsezoo.neuralmagic.com/?modelSet=computer_vision&searchModels=yolo). With SparseML, seamlessly integrated with Ultralytics, users can effortlessly fine-tune these sparse checkpoints on their specific datasets using a straightforward command-line interface.
Checkout [Neural Magic's SparseML YOLOv8 documentation](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov8) for more details.
Checkout [Neural Magic's SparseML YOLO11 documentation](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov8) for more details.
## Usage: Deploying YOLOV8 using DeepSparse
Deploying YOLOv8 with Neural Magic's DeepSparse involves a few straightforward steps. 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. Here's how you can get started.
Deploying YOLO11 with Neural Magic's DeepSparse involves a few straightforward steps. Before diving into the usage instructions, be sure to check out the range of [YOLO11 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements. Here's how you can get started.
### Step 1: Installation
@ -79,24 +79,24 @@ To install the required packages, run:
pip install deepsparse[yolov8]
```
### Step 2: Exporting YOLOv8 to ONNX Format
### Step 2: Exporting YOLO11 to ONNX Format
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:
DeepSparse Engine requires YOLO11 models in ONNX format. Exporting your model to this format is essential for compatibility with DeepSparse. Use the following command to export YOLO11 models:
!!! tip "Model Export"
=== "CLI"
```bash
# Export YOLOv8 model to ONNX format
yolo task=detect mode=export model=yolov8n.pt format=onnx opset=13
# Export YOLO11 model to ONNX format
yolo task=detect mode=export model=yolo11n.pt format=onnx opset=13
```
This command will save the `yolov8n.onnx` model to your disk.
This command will save the `yolo11n.onnx` model to your disk.
### Step 3: Deploying and Running Inferences
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:
With your YOLO11 model in ONNX format, you can deploy and run inferences using DeepSparse. This can be done easily with their intuitive Python API:
!!! tip "Deploying and Running Inferences"
@ -105,8 +105,8 @@ With your YOLOv8 model in ONNX format, you can deploy and run inferences using D
```python
from deepsparse import Pipeline
# Specify the path to your YOLOv8 ONNX model
model_path = "path/to/yolov8n.onnx"
# Specify the path to your YOLO11 ONNX model
model_path = "path/to/yolo11n.onnx"
# Set up the DeepSparse Pipeline
yolo_pipeline = Pipeline.create(task="yolov8", model_path=model_path)
@ -118,7 +118,7 @@ With your YOLOv8 model in ONNX format, you can deploy and run inferences using D
### Step 4: Benchmarking Performance
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:
It's important to check that your YOLO11 model is performing optimally on DeepSparse. You can benchmark your model's performance to analyze throughput and latency:
!!! tip "Benchmarking"
@ -126,12 +126,12 @@ It's important to check that your YOLOv8 model is performing optimally on DeepSp
```bash
# Benchmark performance
deepsparse.benchmark model_path="path/to/yolov8n.onnx" --scenario=sync --input_shapes="[1,3,640,640]"
deepsparse.benchmark model_path="path/to/yolo11n.onnx" --scenario=sync --input_shapes="[1,3,640,640]"
```
### Step 5: Additional Features
DeepSparse provides additional features for practical integration of YOLOv8 in applications, such as image annotation and dataset evaluation.
DeepSparse provides additional features for practical integration of YOLO11 in applications, such as image annotation and dataset evaluation.
!!! tip "Additional Features"
@ -139,10 +139,10 @@ DeepSparse provides additional features for practical integration of YOLOv8 in a
```bash
# For image annotation
deepsparse.yolov8.annotate --source "path/to/image.jpg" --model_filepath "path/to/yolov8n.onnx"
deepsparse.yolov8.annotate --source "path/to/image.jpg" --model_filepath "path/to/yolo11n.onnx"
# For evaluating model performance on a dataset
deepsparse.yolov8.eval --model_path "path/to/yolov8n.onnx"
deepsparse.yolov8.eval --model_path "path/to/yolo11n.onnx"
```
Running the annotate command processes your specified image, detecting objects, and saving the annotated image with bounding boxes and classifications. The annotated image will be stored in an annotation-results folder. This helps provide a visual representation of the model's detection capabilities.
@ -151,61 +151,61 @@ Running the annotate command processes your specified image, detecting objects,
<img width="640" src="https://github.com/ultralytics/docs/releases/download/0/image-annotation-feature.avif" alt="Image Annotation Feature">
</p>
After running the eval command, you will receive detailed output metrics such as [precision](https://www.ultralytics.com/glossary/precision), [recall](https://www.ultralytics.com/glossary/recall), and mAP (mean Average Precision). This provides a comprehensive view of your model's performance on the dataset. This functionality is particularly useful for fine-tuning and optimizing your YOLOv8 models for specific use cases, ensuring high accuracy and efficiency.
After running the eval command, you will receive detailed output metrics such as [precision](https://www.ultralytics.com/glossary/precision), [recall](https://www.ultralytics.com/glossary/recall), and mAP (mean Average Precision). This provides a comprehensive view of your model's performance on the dataset. This functionality is particularly useful for fine-tuning and optimizing your YOLO11 models for specific use cases, ensuring high accuracy and efficiency.
## Summary
This guide explored integrating Ultralytics' YOLOv8 with Neural Magic's DeepSparse Engine. It highlighted how this integration enhances YOLOv8's performance on CPU platforms, offering GPU-level efficiency and advanced neural network sparsity techniques.
This guide explored integrating Ultralytics' YOLO11 with Neural Magic's DeepSparse Engine. It highlighted how this integration enhances YOLO11's performance on CPU platforms, offering GPU-level efficiency and advanced neural network sparsity techniques.
For more detailed information and advanced usage, visit [Neural Magic's DeepSparse documentation](https://docs.neuralmagic.com/products/deepsparse/). Also, check out Neural Magic's documentation on the integration with YOLOv8 [here](https://github.com/neuralmagic/deepsparse/tree/main/src/deepsparse/yolov8#yolov8-inference-pipelines) and watch a great session on it [here](https://www.youtube.com/watch?v=qtJ7bdt52x8).
For more detailed information and advanced usage, visit [Neural Magic's DeepSparse documentation](https://docs.neuralmagic.com/products/deepsparse/). Also, check out Neural Magic's documentation on the integration with YOLO11 [here](https://github.com/neuralmagic/deepsparse/tree/main/src/deepsparse/yolov8#yolov8-inference-pipelines) and watch a great session on it [here](https://www.youtube.com/watch?v=qtJ7bdt52x8).
Additionally, for a broader understanding of various YOLOv8 integrations, visit the [Ultralytics integration guide page](../integrations/index.md), where you can discover a range of other exciting integration possibilities.
Additionally, for a broader understanding of various YOLO11 integrations, visit the [Ultralytics integration guide page](../integrations/index.md), where you can discover a range of other exciting integration possibilities.
## FAQ
### What is Neural Magic's DeepSparse Engine and how does it optimize YOLOv8 performance?
### What is Neural Magic's DeepSparse Engine and how does it optimize YOLO11 performance?
Neural Magic's DeepSparse Engine is an inference runtime designed to optimize the execution of neural networks on CPUs through advanced techniques such as sparsity, pruning, and quantization. By integrating DeepSparse with YOLOv8, you can achieve GPU-like performance on standard CPUs, significantly enhancing inference speed, model efficiency, and overall performance while maintaining accuracy. For more details, check out the [Neural Magic's DeepSparse section](#neural-magics-deepsparse).
Neural Magic's DeepSparse Engine is an inference runtime designed to optimize the execution of neural networks on CPUs through advanced techniques such as sparsity, pruning, and quantization. By integrating DeepSparse with YOLO11, you can achieve GPU-like performance on standard CPUs, significantly enhancing inference speed, model efficiency, and overall performance while maintaining accuracy. For more details, check out the [Neural Magic's DeepSparse section](#neural-magics-deepsparse).
### How can I install the needed packages to deploy YOLOv8 using Neural Magic's DeepSparse?
### How can I install the needed packages to deploy YOLO11 using Neural Magic's DeepSparse?
Installing the required packages for deploying YOLOv8 with Neural Magic's DeepSparse is straightforward. You can easily install them using the CLI. Here's the command you need to run:
Installing the required packages for deploying YOLO11 with Neural Magic's DeepSparse is straightforward. You can easily install them using the CLI. Here's the command you need to run:
```bash
pip install deepsparse[yolov8]
```
Once installed, follow the steps provided in the [Installation section](#step-1-installation) to set up your environment and start using DeepSparse with YOLOv8.
Once installed, follow the steps provided in the [Installation section](#step-1-installation) to set up your environment and start using DeepSparse with YOLO11.
### How do I convert YOLOv8 models to ONNX format for use with DeepSparse?
### How do I convert YOLO11 models to ONNX format for use with DeepSparse?
To convert YOLOv8 models to the ONNX format, which is required for compatibility with DeepSparse, you can use the following CLI command:
To convert YOLO11 models to the ONNX format, which is required for compatibility with DeepSparse, you can use the following CLI command:
```bash
yolo task=detect mode=export model=yolov8n.pt format=onnx opset=13
yolo task=detect mode=export model=yolo11n.pt format=onnx opset=13
```
This command will export your YOLOv8 model (`yolov8n.pt`) to a format (`yolov8n.onnx`) that can be utilized by the DeepSparse Engine. More information about model export can be found in the [Model Export section](#step-2-exporting-yolov8-to-onnx-format).
This command will export your YOLO11 model (`yolo11n.pt`) to a format (`yolo11n.onnx`) that can be utilized by the DeepSparse Engine. More information about model export can be found in the [Model Export section](#step-2-exporting-yolo11-to-onnx-format).
### How do I benchmark YOLOv8 performance on the DeepSparse Engine?
### How do I benchmark YOLO11 performance on the DeepSparse Engine?
Benchmarking YOLOv8 performance on DeepSparse helps you analyze throughput and latency to ensure your model is optimized. You can use the following CLI command to run a benchmark:
Benchmarking YOLO11 performance on DeepSparse helps you analyze throughput and latency to ensure your model is optimized. You can use the following CLI command to run a benchmark:
```bash
deepsparse.benchmark model_path="path/to/yolov8n.onnx" --scenario=sync --input_shapes="[1,3,640,640]"
deepsparse.benchmark model_path="path/to/yolo11n.onnx" --scenario=sync --input_shapes="[1,3,640,640]"
```
This command will provide you with vital performance metrics. For more details, see the [Benchmarking Performance section](#step-4-benchmarking-performance).
### Why should I use Neural Magic's DeepSparse with YOLOv8 for object detection tasks?
### Why should I use Neural Magic's DeepSparse with YOLO11 for object detection tasks?
Integrating Neural Magic's DeepSparse with YOLOv8 offers several benefits:
Integrating Neural Magic's DeepSparse with YOLO11 offers several benefits:
- **Enhanced Inference Speed:** Achieves up to 525 FPS, significantly speeding up YOLOv8's capabilities.
- **Enhanced Inference Speed:** Achieves up to 525 FPS, significantly speeding up YOLO11's capabilities.
- **Optimized Model Efficiency:** Uses sparsity, pruning, and quantization techniques to reduce model size and computational needs while maintaining accuracy.
- **High Performance on Standard CPUs:** Offers GPU-like performance on cost-effective CPU hardware.
- **Streamlined Integration:** User-friendly tools for easy deployment and integration.
- **Flexibility:** Supports both standard and sparsity-optimized YOLOv8 models.
- **Flexibility:** Supports both standard and sparsity-optimized YOLO11 models.
- **Cost-Effective:** Reduces operational expenses through efficient resource utilization.
For a deeper dive into these advantages, visit the [Benefits of Integrating Neural Magic's DeepSparse with YOLOv8 section](#benefits-of-integrating-neural-magics-deepsparse-with-yolov8).
For a deeper dive into these advantages, visit the [Benefits of Integrating Neural Magic's DeepSparse with YOLO11 section](#benefits-of-integrating-neural-magics-deepsparse-with-yolo11).

78
docs/en/integrations/onnx.md
View file

@ -1,14 +1,14 @@
---
comments: true
description: Learn how to export YOLOv8 models to ONNX format for flexible deployment across various platforms with enhanced performance.
keywords: YOLOv8, ONNX, model export, Ultralytics, ONNX Runtime, machine learning, model deployment, computer vision, deep learning
description: Learn how to export YOLO11 models to ONNX format for flexible deployment across various platforms with enhanced performance.
keywords: YOLO11, ONNX, model export, Ultralytics, ONNX Runtime, machine learning, model deployment, computer vision, deep learning
---
# ONNX Export for YOLOv8 Models
# ONNX Export for YOLO11 Models
Often, when deploying [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models, you'll need a model format that's both flexible and compatible with multiple platforms.
Exporting [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models to ONNX format streamlines deployment and ensures optimal performance across various environments. This guide will show you how to easily convert your YOLOv8 models to ONNX and enhance their scalability and effectiveness in real-world applications.
Exporting [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) models to ONNX format streamlines deployment and ensures optimal performance across various environments. This guide will show you how to easily convert your YOLO11 models to ONNX and enhance their scalability and effectiveness in real-world applications.
## ONNX and ONNX Runtime
@ -44,7 +44,7 @@ The ability of ONNX to handle various formats can be attributed to the following
## Common Usage of ONNX
Before we jump into how to export YOLOv8 models to the ONNX format, let's take a look at where ONNX models are usually used.
Before we jump into how to export YOLO11 models to the ONNX format, let's take a look at where ONNX models are usually used.
### CPU Deployment
@ -60,9 +60,9 @@ While ONNX models are commonly used on CPUs, they can also be deployed on the fo
- **Web Browsers**: ONNX can run directly in web browsers, powering interactive and dynamic web-based AI applications.
## Exporting YOLOv8 Models to ONNX
## Exporting YOLO11 Models to ONNX
You can expand model compatibility and deployment flexibility by converting YOLOv8 models to ONNX format.
You can expand model compatibility and deployment flexibility by converting YOLO11 models to ONNX format.
### Installation
@ -73,15 +73,15 @@ To install the required package, run:
=== "CLI"
```bash
# Install the required package for YOLOv8
# Install the required package for YOLO11
pip install ultralytics
```
For detailed instructions and best practices related to the installation process, check our [YOLOv8 Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
For detailed instructions and best practices related to the installation process, check our [YOLO11 Installation guide](../quickstart.md). While installing the required packages for YOLO11, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
### Usage
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.
Before diving into the usage instructions, be sure to check out the range of [YOLO11 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
!!! example "Usage"
@ -90,14 +90,14 @@ Before diving into the usage instructions, be sure to check out the range of [YO
```python
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO("yolov8n.pt")
# Load the YOLO11 model
model = YOLO("yolo11n.pt")
# Export the model to ONNX format
model.export(format="onnx") # creates 'yolov8n.onnx'
model.export(format="onnx") # creates 'yolo11n.onnx'
# Load the exported ONNX model
onnx_model = YOLO("yolov8n.onnx")
onnx_model = YOLO("yolo11n.onnx")
# Run inference
results = onnx_model("https://ultralytics.com/images/bus.jpg")
@ -106,18 +106,18 @@ Before diving into the usage instructions, be sure to check out the range of [YO
=== "CLI"
```bash
# Export a YOLOv8n PyTorch model to ONNX format
yolo export model=yolov8n.pt format=onnx # creates 'yolov8n.onnx'
# Export a YOLO11n PyTorch model to ONNX format
yolo export model=yolo11n.pt format=onnx # creates 'yolo11n.onnx'
# Run inference with the exported model
yolo predict model=yolov8n.onnx source='https://ultralytics.com/images/bus.jpg'
yolo predict model=yolo11n.onnx source='https://ultralytics.com/images/bus.jpg'
```
For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
## Deploying Exported YOLOv8 ONNX Models
## Deploying Exported YOLO11 ONNX Models
Once you've successfully exported your Ultralytics YOLOv8 models to ONNX format, the next step is deploying these models in various environments. For detailed instructions on deploying your ONNX models, take a look at the following resources:
Once you've successfully exported your Ultralytics YOLO11 models to ONNX format, the next step is deploying these models in various environments. For detailed instructions on deploying your ONNX models, take a look at the following resources:
- **[ONNX Runtime Python API Documentation](https://onnxruntime.ai/docs/api/python/api_summary.html)**: This guide provides essential information for loading and running ONNX models using ONNX Runtime.
@ -127,17 +127,17 @@ Once you've successfully exported your Ultralytics YOLOv8 models to ONNX format,
## Summary
In this guide, you've learned how to export Ultralytics YOLOv8 models to ONNX format to increase their interoperability and performance across various platforms. You were also introduced to the ONNX Runtime and ONNX deployment options.
In this guide, you've learned how to export Ultralytics YOLO11 models to ONNX format to increase their interoperability and performance across various platforms. You were also introduced to the ONNX Runtime and ONNX deployment options.
For further details on usage, visit the [ONNX official documentation](https://onnx.ai/onnx/intro/).
Also, if you'd like to know more about other Ultralytics YOLOv8 integrations, visit our [integration guide page](../integrations/index.md). You'll find plenty of useful resources and insights there.
Also, if you'd like to know more about other Ultralytics YOLO11 integrations, visit our [integration guide page](../integrations/index.md). You'll find plenty of useful resources and insights there.
## FAQ
### How do I export YOLOv8 models to ONNX format using Ultralytics?
### How do I export YOLO11 models to ONNX format using Ultralytics?
To export your YOLOv8 models to ONNX format using Ultralytics, follow these steps:
To export your YOLO11 models to ONNX format using Ultralytics, follow these steps:
!!! example "Usage"
@ -146,14 +146,14 @@ To export your YOLOv8 models to ONNX format using Ultralytics, follow these step
```python
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO("yolov8n.pt")
# Load the YOLO11 model
model = YOLO("yolo11n.pt")
# Export the model to ONNX format
model.export(format="onnx") # creates 'yolov8n.onnx'
model.export(format="onnx") # creates 'yolo11n.onnx'
# Load the exported ONNX model
onnx_model = YOLO("yolov8n.onnx")
onnx_model = YOLO("yolo11n.onnx")
# Run inference
results = onnx_model("https://ultralytics.com/images/bus.jpg")
@ -162,18 +162,18 @@ To export your YOLOv8 models to ONNX format using Ultralytics, follow these step
=== "CLI"
```bash
# Export a YOLOv8n PyTorch model to ONNX format
yolo export model=yolov8n.pt format=onnx # creates 'yolov8n.onnx'
# Export a YOLO11n PyTorch model to ONNX format
yolo export model=yolo11n.pt format=onnx # creates 'yolo11n.onnx'
# Run inference with the exported model
yolo predict model=yolov8n.onnx source='https://ultralytics.com/images/bus.jpg'
yolo predict model=yolo11n.onnx source='https://ultralytics.com/images/bus.jpg'
```
For more details, visit the [export documentation](../modes/export.md).
### What are the advantages of using ONNX Runtime for deploying YOLOv8 models?
### What are the advantages of using ONNX Runtime for deploying YOLO11 models?
Using ONNX Runtime for deploying YOLOv8 models offers several advantages:
Using ONNX Runtime for deploying YOLO11 models offers several advantages:
- **Cross-platform compatibility**: ONNX Runtime supports various platforms, such as Windows, macOS, and Linux, ensuring your models run smoothly across different environments.
- **Hardware acceleration**: ONNX Runtime can leverage hardware-specific optimizations for CPUs, GPUs, and dedicated accelerators, providing high-performance inference.
@ -181,9 +181,9 @@ Using ONNX Runtime for deploying YOLOv8 models offers several advantages:
Learn more by checking the [ONNX Runtime documentation](https://onnxruntime.ai/docs/api/python/api_summary.html).
### What deployment options are available for YOLOv8 models exported to ONNX?
### What deployment options are available for YOLO11 models exported to ONNX?
YOLOv8 models exported to ONNX can be deployed on various platforms including:
YOLO11 models exported to ONNX can be deployed on various platforms including:
- **CPUs**: Utilizing ONNX Runtime for optimized CPU inference.
- **GPUs**: Leveraging NVIDIA CUDA for high-performance GPU acceleration.
@ -192,19 +192,19 @@ YOLOv8 models exported to ONNX can be deployed on various platforms including:
For more information, explore our guide on [model deployment options](../guides/model-deployment-options.md).
### Why should I use ONNX format for Ultralytics YOLOv8 models?
### Why should I use ONNX format for Ultralytics YOLO11 models?
Using ONNX format for Ultralytics YOLOv8 models provides numerous benefits:
Using ONNX format for Ultralytics YOLO11 models provides numerous benefits:
- **Interoperability**: ONNX allows models to be transferred between different machine learning frameworks seamlessly.
- **Performance Optimization**: ONNX Runtime can enhance model performance by utilizing hardware-specific optimizations.
- **Flexibility**: ONNX supports various deployment environments, enabling you to use the same model on different platforms without modification.
Refer to the comprehensive guide on [exporting YOLOv8 models to ONNX](https://www.ultralytics.com/blog/export-and-optimize-a-yolov8-model-for-inference-on-openvino).
Refer to the comprehensive guide on [exporting YOLO11 models to ONNX](https://www.ultralytics.com/blog/export-and-optimize-a-yolov8-model-for-inference-on-openvino).
### How can I troubleshoot issues when exporting YOLOv8 models to ONNX?
### How can I troubleshoot issues when exporting YOLO11 models to ONNX?
When exporting YOLOv8 models to ONNX, you might encounter common issues such as mismatched dependencies or unsupported operations. To troubleshoot these problems:
When exporting YOLO11 models to ONNX, you might encounter common issues such as mismatched dependencies or unsupported operations. To troubleshoot these problems:
1. Verify that you have the correct version of required dependencies installed.
2. Check the official [ONNX documentation](https://onnx.ai/onnx/intro/) for supported operators and features.

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@ -1,12 +1,12 @@
---
comments: true
description: Learn how to export YOLOv8 models to PaddlePaddle format for enhanced performance, flexibility, and deployment across various platforms and devices.
keywords: YOLOv8, PaddlePaddle, export models, computer vision, deep learning, model deployment, performance optimization
description: Learn how to export YOLO11 models to PaddlePaddle format for enhanced performance, flexibility, and deployment across various platforms and devices.
keywords: YOLO11, PaddlePaddle, export models, computer vision, deep learning, model deployment, performance optimization
---
# How to Export to PaddlePaddle Format from YOLOv8 Models
# How to Export to PaddlePaddle Format from YOLO11 Models
Bridging the gap between developing and deploying [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models in real-world scenarios with varying conditions can be difficult. PaddlePaddle makes this process easier with its focus on flexibility, performance, and its capability for parallel processing in distributed environments. This means you can use your YOLOv8 computer vision models on a wide variety of devices and platforms, from smartphones to cloud-based servers.
Bridging the gap between developing and deploying [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models in real-world scenarios with varying conditions can be difficult. PaddlePaddle makes this process easier with its focus on flexibility, performance, and its capability for parallel processing in distributed environments. This means you can use your YOLO11 computer vision models on a wide variety of devices and platforms, from smartphones to cloud-based servers.
<p align="center">
<br>
@ -16,10 +16,10 @@ Bridging the gap between developing and deploying [computer vision](https://www.
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> How to Export Ultralytics YOLOv8 Models to PaddlePaddle Format | Key Features of PaddlePaddle Format
<strong>Watch:</strong> How to Export Ultralytics YOLO11 Models to PaddlePaddle Format | Key Features of PaddlePaddle Format
</p>
The ability to export to PaddlePaddle model format allows you to optimize your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models for use within the PaddlePaddle framework. PaddlePaddle is known for facilitating industrial deployments and is a good choice for deploying computer vision applications in real-world settings across various domains.
The ability to export to PaddlePaddle model format allows you to optimize your [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) models for use within the PaddlePaddle framework. PaddlePaddle is known for facilitating industrial deployments and is a good choice for deploying computer vision applications in real-world settings across various domains.
## Why should you export to PaddlePaddle?
@ -31,7 +31,7 @@ Developed by Baidu, [PaddlePaddle](https://www.paddlepaddle.org.cn/en) (**PA**ra
It offers tools and resources similar to popular frameworks like [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) and [PyTorch](https://www.ultralytics.com/glossary/pytorch), making it accessible for developers of all experience levels. From farming and factories to service businesses, PaddlePaddle's large developer community of over 4.77 million is helping create and deploy AI applications.
By exporting your Ultralytics YOLOv8 models to PaddlePaddle format, you can tap into PaddlePaddle's strengths in performance optimization. PaddlePaddle prioritizes efficient model execution and reduced memory usage. As a result, your YOLOv8 models can potentially achieve even better performance, delivering top-notch results in practical scenarios.
By exporting your Ultralytics YOLO11 models to PaddlePaddle format, you can tap into PaddlePaddle's strengths in performance optimization. PaddlePaddle prioritizes efficient model execution and reduced memory usage. As a result, your YOLO11 models can potentially achieve even better performance, delivering top-notch results in practical scenarios.
## Key Features of PaddlePaddle Models
@ -45,7 +45,7 @@ PaddlePaddle models offer a range of key features that contribute to their flexi
## Deployment Options in PaddlePaddle
Before diving into the code for exporting YOLOv8 models to PaddlePaddle, let's take a look at the different deployment scenarios in which PaddlePaddle models excel.
Before diving into the code for exporting YOLO11 models to PaddlePaddle, let's take a look at the different deployment scenarios in which PaddlePaddle models excel.
PaddlePaddle provides a range of options, each offering a distinct balance of ease of use, flexibility, and performance:
@ -57,9 +57,9 @@ PaddlePaddle provides a range of options, each offering a distinct balance of ea
- **Paddle.js**: Paddle.js enables you to deploy PaddlePaddle models directly within web browsers. Paddle.js can either load a pre-trained model or transform a model from [paddle-hub](https://github.com/PaddlePaddle/PaddleHub) with model transforming tools provided by Paddle.js. It can run in browsers that support WebGL/WebGPU/WebAssembly.
## Export to PaddlePaddle: Converting Your YOLOv8 Model
## Export to PaddlePaddle: Converting Your YOLO11 Model
Converting YOLOv8 models to the PaddlePaddle format can improve execution flexibility and optimize performance for various deployment scenarios.
Converting YOLO11 models to the PaddlePaddle format can improve execution flexibility and optimize performance for various deployment scenarios.
### Installation
@ -70,15 +70,15 @@ To install the required package, run:
=== "CLI"
```bash
# Install the required package for YOLOv8
# Install the required package for YOLO11
pip install ultralytics
```
For detailed instructions and best practices related to the installation process, check our [Ultralytics Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
For detailed instructions and best practices related to the installation process, check our [Ultralytics Installation guide](../quickstart.md). While installing the required packages for YOLO11, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
### Usage
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).
Before diving into the usage instructions, it's important to note that while all [Ultralytics YOLO11 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"
@ -87,14 +87,14 @@ Before diving into the usage instructions, it's important to note that while all
```python
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO("yolov8n.pt")
# Load the YOLO11 model
model = YOLO("yolo11n.pt")
# Export the model to PaddlePaddle format
model.export(format="paddle") # creates '/yolov8n_paddle_model'
model.export(format="paddle") # creates '/yolo11n_paddle_model'
# Load the exported PaddlePaddle model
paddle_model = YOLO("./yolov8n_paddle_model")
paddle_model = YOLO("./yolo11n_paddle_model")
# Run inference
results = paddle_model("https://ultralytics.com/images/bus.jpg")
@ -103,18 +103,18 @@ Before diving into the usage instructions, it's important to note that while all
=== "CLI"
```bash
# Export a YOLOv8n PyTorch model to PaddlePaddle format
yolo export model=yolov8n.pt format=paddle # creates '/yolov8n_paddle_model'
# Export a YOLO11n PyTorch model to PaddlePaddle format
yolo export model=yolo11n.pt format=paddle # creates '/yolo11n_paddle_model'
# Run inference with the exported model
yolo predict model='./yolov8n_paddle_model' source='https://ultralytics.com/images/bus.jpg'
yolo predict model='./yolo11n_paddle_model' source='https://ultralytics.com/images/bus.jpg'
```
For more details about supported export options, visit the [Ultralytics documentation page on deployment options](../guides/model-deployment-options.md).
## Deploying Exported YOLOv8 PaddlePaddle Models
## Deploying Exported YOLO11 PaddlePaddle Models
After successfully exporting your Ultralytics YOLOv8 models to PaddlePaddle format, you can now deploy them. The primary and recommended first step for running a PaddlePaddle model is to use the YOLO("./model_paddle_model") method, as outlined in the previous usage code snippet.
After successfully exporting your Ultralytics YOLO11 models to PaddlePaddle format, you can now deploy them. The primary and recommended first step for running a PaddlePaddle model is to use the YOLO("./model_paddle_model") method, as outlined in the previous usage code snippet.
However, for in-depth instructions on deploying your PaddlePaddle models in various other settings, take a look at the following resources:
@ -126,17 +126,17 @@ However, for in-depth instructions on deploying your PaddlePaddle models in vari
## Summary
In this guide, we explored the process of exporting Ultralytics YOLOv8 models to the PaddlePaddle format. By following these steps, you can leverage PaddlePaddle's strengths in diverse deployment scenarios, optimizing your models for different hardware and software environments.
In this guide, we explored the process of exporting Ultralytics YOLO11 models to the PaddlePaddle format. By following these steps, you can leverage PaddlePaddle's strengths in diverse deployment scenarios, optimizing your models for different hardware and software environments.
For further details on usage, visit the [PaddlePaddle official documentation](https://www.paddlepaddle.org.cn/documentation/docs/en/guides/index_en.html)
Want to explore more ways to integrate your Ultralytics YOLOv8 models? Our [integration guide page](index.md) explores various options, equipping you with valuable resources and insights.
Want to explore more ways to integrate your Ultralytics YOLO11 models? Our [integration guide page](index.md) explores various options, equipping you with valuable resources and insights.
## FAQ
### How do I export Ultralytics YOLOv8 models to PaddlePaddle format?
### How do I export Ultralytics YOLO11 models to PaddlePaddle format?
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:
Exporting Ultralytics YOLO11 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:
!!! example "Usage"
@ -145,14 +145,14 @@ Exporting Ultralytics YOLOv8 models to PaddlePaddle format is straightforward. Y
```python
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO("yolov8n.pt")
# Load the YOLO11 model
model = YOLO("yolo11n.pt")
# Export the model to PaddlePaddle format
model.export(format="paddle") # creates '/yolov8n_paddle_model'
model.export(format="paddle") # creates '/yolo11n_paddle_model'
# Load the exported PaddlePaddle model
paddle_model = YOLO("./yolov8n_paddle_model")
paddle_model = YOLO("./yolo11n_paddle_model")
# Run inference
results = paddle_model("https://ultralytics.com/images/bus.jpg")
@ -161,11 +161,11 @@ Exporting Ultralytics YOLOv8 models to PaddlePaddle format is straightforward. Y
=== "CLI"
```bash
# Export a YOLOv8n PyTorch model to PaddlePaddle format
yolo export model=yolov8n.pt format=paddle # creates '/yolov8n_paddle_model'
# Export a YOLO11n PyTorch model to PaddlePaddle format
yolo export model=yolo11n.pt format=paddle # creates '/yolo11n_paddle_model'
# Run inference with the exported model
yolo predict model='./yolov8n_paddle_model' source='https://ultralytics.com/images/bus.jpg'
yolo predict model='./yolo11n_paddle_model' source='https://ultralytics.com/images/bus.jpg'
```
For more detailed setup and troubleshooting, check the [Ultralytics Installation Guide](../quickstart.md) and [Common Issues Guide](../guides/yolo-common-issues.md).
@ -179,17 +179,17 @@ PaddlePaddle offers several key advantages for model deployment:
- **Operator Fusion**: By merging compatible operations, it reduces computational overhead.
- **Quantization Techniques**: Supports both post-training and quantization-aware training, enabling lower-[precision](https://www.ultralytics.com/glossary/precision) data representations for improved performance.
You can achieve enhanced results by exporting your Ultralytics YOLOv8 models to PaddlePaddle, ensuring flexibility and high performance across various applications and hardware platforms. Learn more about PaddlePaddle's features [here](https://www.paddlepaddle.org.cn/en).
You can achieve enhanced results by exporting your Ultralytics YOLO11 models to PaddlePaddle, ensuring flexibility and high performance across various applications and hardware platforms. Learn more about PaddlePaddle's features [here](https://www.paddlepaddle.org.cn/en).
### Why should I choose PaddlePaddle for deploying my YOLOv8 models?
### Why should I choose PaddlePaddle for deploying my YOLO11 models?
PaddlePaddle, developed by Baidu, is optimized for industrial and commercial AI deployments. Its large developer community and robust framework provide extensive tools similar to TensorFlow and PyTorch. By exporting your YOLOv8 models to PaddlePaddle, you leverage:
PaddlePaddle, developed by Baidu, is optimized for industrial and commercial AI deployments. Its large developer community and robust framework provide extensive tools similar to TensorFlow and PyTorch. By exporting your YOLO11 models to PaddlePaddle, you leverage:
- **Enhanced Performance**: Optimal execution speed and reduced memory footprint.
- **Flexibility**: Wide compatibility with various devices from smartphones to cloud servers.
- **Scalability**: Efficient parallel processing capabilities for distributed environments.
These features make PaddlePaddle a compelling choice for deploying YOLOv8 models in production settings.
These features make PaddlePaddle a compelling choice for deploying YOLO11 models in production settings.
### How does PaddlePaddle improve model performance over other frameworks?
@ -199,9 +199,9 @@ PaddlePaddle employs several advanced techniques to optimize model performance:
- **Operator Fusion**: Combines compatible operations to minimize memory transfer and increase inference speed.
- **Quantization**: Reduces model size and increases efficiency using lower-precision data while maintaining [accuracy](https://www.ultralytics.com/glossary/accuracy).
These techniques prioritize efficient model execution, making PaddlePaddle an excellent option for deploying high-performance YOLOv8 models. For more on optimization, see the [PaddlePaddle official documentation](https://www.paddlepaddle.org.cn/documentation/docs/en/guides/index_en.html).
These techniques prioritize efficient model execution, making PaddlePaddle an excellent option for deploying high-performance YOLO11 models. For more on optimization, see the [PaddlePaddle official documentation](https://www.paddlepaddle.org.cn/documentation/docs/en/guides/index_en.html).
### What deployment options does PaddlePaddle offer for YOLOv8 models?
### What deployment options does PaddlePaddle offer for YOLO11 models?
PaddlePaddle provides flexible deployment options:

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@ -1,14 +1,14 @@
---
comments: true
description: Simplify YOLOv8 training with Paperspace Gradient's all-in-one MLOps platform. Access GPUs, automate workflows, and deploy with ease.
keywords: YOLOv8, Paperspace Gradient, MLOps, machine learning, training, GPUs, Jupyter notebooks, model deployment, AI, cloud platform
description: Simplify YOLO11 training with Paperspace Gradient's all-in-one MLOps platform. Access GPUs, automate workflows, and deploy with ease.
keywords: YOLO11, Paperspace Gradient, MLOps, machine learning, training, GPUs, Jupyter notebooks, model deployment, AI, cloud platform
---
# YOLOv8 Model Training Made Simple with Paperspace Gradient
# YOLO11 Model Training Made Simple with Paperspace Gradient
Training computer vision models like [YOLOv8](https://github.com/ultralytics/ultralytics) can be complicated. It involves managing large datasets, using different types of computer hardware like GPUs, TPUs, and CPUs, and making sure data flows smoothly during the training process. Typically, developers end up spending a lot of time managing their computer systems and environments. It can be frustrating when you just want to focus on building the best model.
Training computer vision models like [YOLO11](https://github.com/ultralytics/ultralytics) can be complicated. It involves managing large datasets, using different types of computer hardware like GPUs, TPUs, and CPUs, and making sure data flows smoothly during the training process. Typically, developers end up spending a lot of time managing their computer systems and environments. It can be frustrating when you just want to focus on building the best model.
This is where a platform like Paperspace Gradient can make things simpler. Paperspace Gradient is a MLOps platform that lets you build, train, and deploy [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) models all in one place. With Gradient, developers can focus on training their YOLOv8 models without the hassle of managing infrastructure and environments.
This is where a platform like Paperspace Gradient can make things simpler. Paperspace Gradient is a MLOps platform that lets you build, train, and deploy [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) models all in one place. With Gradient, developers can focus on training their YOLO11 models without the hassle of managing infrastructure and environments.
## Paperspace
@ -28,15 +28,15 @@ Paperspace Gradient is a suite of tools designed to make working with AI and mac
Within its toolkit, it includes support for Google's TPUs via a job runner, comprehensive support for Jupyter notebooks and containers, and new programming language integrations. Its focus on language integration particularly stands out, allowing users to easily adapt their existing Python projects to use the most advanced GPU infrastructure available.
## Training YOLOv8 Using Paperspace Gradient
## Training YOLO11 Using Paperspace Gradient
Paperspace Gradient makes training a YOLOv8 model possible with a few clicks. Thanks to the integration, you can access the [Paperspace console](https://console.paperspace.com/github/ultralytics/ultralytics) and start training your model immediately. For a detailed understanding of the model training process and best practices, refer to our [YOLOv8 Model Training guide](../modes/train.md).
Paperspace Gradient makes training a YOLO11 model possible with a few clicks. Thanks to the integration, you can access the [Paperspace console](https://console.paperspace.com/github/ultralytics/ultralytics) and start training your model immediately. For a detailed understanding of the model training process and best practices, refer to our [YOLO11 Model Training guide](../modes/train.md).
Sign in and then click on the “Start Machine” button shown in the image below. In a few seconds, a managed GPU environment will start up, and then you can run the notebook's cells.
![Training YOLOv8 Using Paperspace Gradient](https://github.com/ultralytics/docs/releases/download/0/start-machine-button.avif)
![Training YOLO11 Using Paperspace Gradient](https://github.com/ultralytics/docs/releases/download/0/start-machine-button.avif)
Explore more capabilities of YOLOv8 and Paperspace Gradient in a discussion with Glenn Jocher, Ultralytics founder, and James Skelton from Paperspace. Watch the discussion below.
Explore more capabilities of YOLO11 and Paperspace Gradient in a discussion with Glenn Jocher, Ultralytics founder, and James Skelton from Paperspace. Watch the discussion below.
<p align="center">
<br>
@ -46,14 +46,14 @@ Explore more capabilities of YOLOv8 and Paperspace Gradient in a discussion with
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Ultralytics Live Session 7: It's All About the Environment: Optimizing YOLOv8 Training With Gradient
<strong>Watch:</strong> Ultralytics Live Session 7: It's All About the Environment: Optimizing YOLO11 Training With Gradient
</p>
## Key Features of Paperspace Gradient
As you explore the Paperspace console, you'll see how each step of the machine-learning workflow is supported and enhanced. Here are some things to look out for:
- **One-Click Notebooks:** Gradient provides pre-configured Jupyter Notebooks specifically tailored for YOLOv8, eliminating the need for environment setup and dependency management. Simply choose the desired notebook and start experimenting immediately.
- **One-Click Notebooks:** Gradient provides pre-configured Jupyter Notebooks specifically tailored for YOLO11, eliminating the need for environment setup and dependency management. Simply choose the desired notebook and start experimenting immediately.
- **Hardware Flexibility:** Choose from a range of machine types with varying CPU, GPU, and TPU configurations to suit your training needs and budget. Gradient handles all the backend setup, allowing you to focus on model development.
@ -61,13 +61,13 @@ As you explore the Paperspace console, you'll see how each step of the machine-l
- **Dataset Management:** Efficiently manage your datasets directly within Gradient. Upload, version, and pre-process data with ease, streamlining the data preparation phase of your project.
- **Model Serving:** Deploy your trained YOLOv8 models as REST APIs with just a few clicks. Gradient handles the infrastructure, allowing you to easily integrate your [object detection](https://www.ultralytics.com/glossary/object-detection) models into your applications.
- **Model Serving:** Deploy your trained YOLO11 models as REST APIs with just a few clicks. Gradient handles the infrastructure, allowing you to easily integrate your [object detection](https://www.ultralytics.com/glossary/object-detection) models into your applications.
- **Real-time Monitoring:** Monitor the performance and health of your deployed models through Gradient's intuitive dashboard. Gain insights into inference speed, resource utilization, and potential errors.
## Why Should You Use Gradient for Your YOLOv8 Projects?
## Why Should You Use Gradient for Your YOLO11 Projects?
While many options are available for training, deploying, and evaluating YOLOv8 models, the integration with Paperspace Gradient offers a unique set of advantages that separates it from other solutions. Let's explore what makes this integration unique:
While many options are available for training, deploying, and evaluating YOLO11 models, the integration with Paperspace Gradient offers a unique set of advantages that separates it from other solutions. Let's explore what makes this integration unique:
- **Enhanced Collaboration:** Shared workspaces and version control facilitate seamless teamwork and ensure reproducibility, allowing your team to work together effectively and maintain a clear history of your project.
@ -79,37 +79,37 @@ While many options are available for training, deploying, and evaluating YOLOv8
## Summary
This guide explored the Paperspace Gradient integration for training YOLOv8 models. Gradient provides the tools and infrastructure to accelerate your AI development journey from effortless model training and evaluation to streamlined deployment options.
This guide explored the Paperspace Gradient integration for training YOLO11 models. Gradient provides the tools and infrastructure to accelerate your AI development journey from effortless model training and evaluation to streamlined deployment options.
For further exploration, visit [PaperSpace's official documentation](https://docs.digitalocean.com/products/paperspace/).
Also, visit the [Ultralytics integration guide page](index.md) to learn more about different YOLOv8 integrations. It's full of insights and tips to take your [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) projects to the next level.
Also, visit the [Ultralytics integration guide page](index.md) to learn more about different YOLO11 integrations. It's full of insights and tips to take your [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) projects to the next level.
## FAQ
### How do I train a YOLOv8 model using Paperspace Gradient?
### How do I train a YOLO11 model using Paperspace Gradient?
Training a YOLOv8 model with Paperspace Gradient is straightforward and efficient. First, sign in to the [Paperspace console](https://console.paperspace.com/github/ultralytics/ultralytics). Next, click the “Start Machine” button to initiate a managed GPU environment. Once the environment is ready, you can run the notebook's cells to start training your YOLOv8 model. For detailed instructions, refer to our [YOLOv8 Model Training guide](../modes/train.md).
Training a YOLO11 model with Paperspace Gradient is straightforward and efficient. First, sign in to the [Paperspace console](https://console.paperspace.com/github/ultralytics/ultralytics). Next, click the “Start Machine” button to initiate a managed GPU environment. Once the environment is ready, you can run the notebook's cells to start training your YOLO11 model. For detailed instructions, refer to our [YOLO11 Model Training guide](../modes/train.md).
### What are the advantages of using Paperspace Gradient for YOLOv8 projects?
### What are the advantages of using Paperspace Gradient for YOLO11 projects?
Paperspace Gradient offers several unique advantages for training and deploying YOLOv8 models:
Paperspace Gradient offers several unique advantages for training and deploying YOLO11 models:
- **Hardware Flexibility:** Choose from various CPU, GPU, and TPU configurations.
- **One-Click Notebooks:** Use pre-configured Jupyter Notebooks for YOLOv8 without worrying about environment setup.
- **One-Click Notebooks:** Use pre-configured Jupyter Notebooks for YOLO11 without worrying about environment setup.
- **Experiment Tracking:** Automatic tracking of hyperparameters, metrics, and code changes.
- **Dataset Management:** Efficiently manage your datasets within Gradient.
- **Model Serving:** Deploy models as REST APIs easily.
- **Real-time Monitoring:** Monitor model performance and resource utilization through a dashboard.
### Why should I choose Ultralytics YOLOv8 over other object detection models?
### Why should I choose Ultralytics YOLO11 over other object detection models?
Ultralytics YOLOv8 stands out for its real-time object detection capabilities and high [accuracy](https://www.ultralytics.com/glossary/accuracy). Its seamless integration with platforms like Paperspace Gradient enhances productivity by simplifying the training and deployment process. YOLOv8 supports various use cases, from security systems to retail inventory management. Explore more about YOLOv8's advantages [here](https://www.ultralytics.com/yolo).
Ultralytics YOLO11 stands out for its real-time object detection capabilities and high [accuracy](https://www.ultralytics.com/glossary/accuracy). Its seamless integration with platforms like Paperspace Gradient enhances productivity by simplifying the training and deployment process. YOLO11 supports various use cases, from security systems to retail inventory management. Explore more about YOLO11's advantages [here](https://www.ultralytics.com/yolo).
### Can I deploy my YOLOv8 model on edge devices using Paperspace Gradient?
### Can I deploy my YOLO11 model on edge devices using Paperspace Gradient?
Yes, you can deploy YOLOv8 models on edge devices using Paperspace Gradient. The platform supports various deployment formats like TFLite and Edge TPU, which are optimized for edge devices. After training your model on Gradient, refer to our [export guide](../modes/export.md) for instructions on converting your model to the desired format.
Yes, you can deploy YOLO11 models on edge devices using Paperspace Gradient. The platform supports various deployment formats like TFLite and Edge TPU, which are optimized for edge devices. After training your model on Gradient, refer to our [export guide](../modes/export.md) for instructions on converting your model to the desired format.
### How does experiment tracking in Paperspace Gradient help improve YOLOv8 training?
### How does experiment tracking in Paperspace Gradient help improve YOLO11 training?
Experiment tracking in Paperspace Gradient streamlines the model development process by automatically logging hyperparameters, metrics, and code changes. This allows you to easily compare different training runs, identify optimal configurations, and reproduce successful experiments.

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@ -1,16 +1,16 @@
---
comments: true
description: Optimize YOLOv8 model performance with Ray Tune. Learn efficient hyperparameter tuning using advanced search strategies, parallelism, and early stopping.
keywords: YOLOv8, Ray Tune, hyperparameter tuning, model optimization, machine learning, deep learning, AI, Ultralytics, Weights & Biases
description: Optimize YOLO11 model performance with Ray Tune. Learn efficient hyperparameter tuning using advanced search strategies, parallelism, and early stopping.
keywords: YOLO11, Ray Tune, hyperparameter tuning, model optimization, machine learning, deep learning, AI, Ultralytics, Weights & Biases
---
# Efficient [Hyperparameter Tuning](https://www.ultralytics.com/glossary/hyperparameter-tuning) with Ray Tune and YOLOv8
# Efficient [Hyperparameter Tuning](https://www.ultralytics.com/glossary/hyperparameter-tuning) with Ray Tune and YOLO11
Hyperparameter tuning is vital in achieving peak model performance by discovering the optimal set of hyperparameters. This involves running trials with different hyperparameters and evaluating each trial's performance.
## Accelerate Tuning with Ultralytics YOLOv8 and Ray Tune
## Accelerate Tuning with Ultralytics YOLO11 and Ray Tune
[Ultralytics YOLOv8](https://www.ultralytics.com/) incorporates Ray Tune for hyperparameter tuning, streamlining the optimization of YOLOv8 model hyperparameters. With Ray Tune, you can utilize advanced search strategies, parallelism, and early stopping to expedite the tuning process.
[Ultralytics YOLO11](https://www.ultralytics.com/) incorporates Ray Tune for hyperparameter tuning, streamlining the optimization of YOLO11 model hyperparameters. With Ray Tune, you can utilize advanced search strategies, parallelism, and early stopping to expedite the tuning process.
### Ray Tune
@ -18,11 +18,11 @@ Hyperparameter tuning is vital in achieving peak model performance by discoverin
<img width="640" src="https://github.com/ultralytics/docs/releases/download/0/ray-tune-overview.avif" alt="Ray Tune Overview">
</p>
[Ray Tune](https://docs.ray.io/en/latest/tune/index.html) is a hyperparameter tuning library designed for efficiency and flexibility. It supports various search strategies, parallelism, and early stopping strategies, and seamlessly integrates with popular [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) frameworks, including Ultralytics YOLOv8.
[Ray Tune](https://docs.ray.io/en/latest/tune/index.html) is a hyperparameter tuning library designed for efficiency and flexibility. It supports various search strategies, parallelism, and early stopping strategies, and seamlessly integrates with popular [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) frameworks, including Ultralytics YOLO11.
### Integration with Weights & Biases
YOLOv8 also allows optional integration with [Weights & Biases](https://wandb.ai/site) for monitoring the tuning process.
YOLO11 also allows optional integration with [Weights & Biases](https://wandb.ai/site) for monitoring the tuning process.
## Installation
@ -49,21 +49,21 @@ To install the required packages, run:
```python
from ultralytics import YOLO
# Load a YOLOv8n model
model = YOLO("yolov8n.pt")
# Load a YOLO11n model
model = YOLO("yolo11n.pt")
# Start tuning hyperparameters for YOLOv8n training on the COCO8 dataset
# Start tuning hyperparameters for YOLO11n training on the COCO8 dataset
result_grid = model.tune(data="coco8.yaml", use_ray=True)
```
## `tune()` Method Parameters
The `tune()` method in YOLOv8 provides an easy-to-use interface for hyperparameter tuning with Ray Tune. It accepts several arguments that allow you to customize the tuning process. Below is a detailed explanation of each parameter:
The `tune()` method in YOLO11 provides an easy-to-use interface for hyperparameter tuning with Ray Tune. It accepts several arguments that allow you to customize the tuning process. Below is a detailed explanation of each parameter:
| Parameter | Type | Description | Default Value |
| --------------- | ---------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------- |
| `data` | `str` | The dataset configuration file (in YAML format) to run the tuner on. This file should specify the training and [validation data](https://www.ultralytics.com/glossary/validation-data) paths, as well as other dataset-specific settings. | |
| `space` | `dict, optional` | A dictionary defining the hyperparameter search space for Ray Tune. Each key corresponds to a hyperparameter name, and the value specifies the range of values to explore during tuning. If not provided, YOLOv8 uses a default search space with various hyperparameters. | |
| `space` | `dict, optional` | A dictionary defining the hyperparameter search space for Ray Tune. Each key corresponds to a hyperparameter name, and the value specifies the range of values to explore during tuning. If not provided, YOLO11 uses a default search space with various hyperparameters. | |
| `grace_period` | `int, optional` | The grace period in [epochs](https://www.ultralytics.com/glossary/epoch) for the [ASHA scheduler](https://docs.ray.io/en/latest/tune/api/schedulers.html) in Ray Tune. The scheduler will not terminate any trial before this number of epochs, allowing the model to have some minimum training before making a decision on early stopping. | 10 |
| `gpu_per_trial` | `int, optional` | The number of GPUs to allocate per trial during tuning. This helps manage GPU usage, particularly in multi-GPU environments. If not provided, the tuner will use all available GPUs. | None |
| `iterations` | `int, optional` | The maximum number of trials to run during tuning. This parameter helps control the total number of hyperparameter combinations tested, ensuring the tuning process does not run indefinitely. | 10 |
@ -73,7 +73,7 @@ By customizing these parameters, you can fine-tune the hyperparameter optimizati
## Default Search Space Description
The following table lists the default search space parameters for hyperparameter tuning in YOLOv8 with Ray Tune. Each parameter has a specific value range defined by `tune.uniform()`.
The following table lists the default search space parameters for hyperparameter tuning in YOLO11 with Ray Tune. Each parameter has a specific value range defined by `tune.uniform()`.
| Parameter | Value Range | Description |
| ----------------- | -------------------------- | --------------------------------------------------------------------------- |
@ -101,7 +101,7 @@ The following table lists the default search space parameters for hyperparameter
## Custom Search Space Example
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.
In this example, we demonstrate how to use a custom search space for hyperparameter tuning with Ray Tune and YOLO11. By providing a custom search space, you can focus the tuning process on specific hyperparameters of interest.
!!! example "Usage"
@ -109,7 +109,7 @@ In this example, we demonstrate how to use a custom search space for hyperparame
from ultralytics import YOLO
# Define a YOLO model
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
# Run Ray Tune on the model
result_grid = model.tune(
@ -120,7 +120,7 @@ In this example, we demonstrate how to use a custom search space for hyperparame
)
```
In the code snippet above, we create a YOLO model with the "yolov8n.pt" pretrained weights. Then, we call the `tune()` method, specifying the dataset configuration with "coco8.yaml". We provide a custom search space for the initial learning rate `lr0` using a dictionary with the key "lr0" and the value `tune.uniform(1e-5, 1e-1)`. Finally, we pass additional training arguments, such as the number of epochs directly to the tune method as `epochs=50`.
In the code snippet above, we create a YOLO model with the "yolo11n.pt" pretrained weights. Then, we call the `tune()` method, specifying the dataset configuration with "coco8.yaml". We provide a custom search space for the initial learning rate `lr0` using a dictionary with the key "lr0" and the value `tune.uniform(1e-5, 1e-1)`. Finally, we pass additional training arguments, such as the number of epochs directly to the tune method as `epochs=50`.
## Processing Ray Tune Results
@ -186,9 +186,9 @@ Explore further by looking into Ray Tune's [Analyze Results](https://docs.ray.io
## FAQ
### How do I tune the hyperparameters of my YOLOv8 model using Ray Tune?
### How do I tune the hyperparameters of my YOLO11 model using Ray Tune?
To tune the hyperparameters of your Ultralytics YOLOv8 model using Ray Tune, follow these steps:
To tune the hyperparameters of your Ultralytics YOLO11 model using Ray Tune, follow these steps:
1. **Install the required packages:**
@ -197,13 +197,13 @@ To tune the hyperparameters of your Ultralytics YOLOv8 model using Ray Tune, fol
pip install wandb # optional for logging
```
2. **Load your YOLOv8 model and start tuning:**
2. **Load your YOLO11 model and start tuning:**
```python
from ultralytics import YOLO
# Load a YOLOv8 model
model = YOLO("yolov8n.pt")
# Load a YOLO11 model
model = YOLO("yolo11n.pt")
# Start tuning with the COCO8 dataset
result_grid = model.tune(data="coco8.yaml", use_ray=True)
@ -211,9 +211,9 @@ To tune the hyperparameters of your Ultralytics YOLOv8 model using Ray Tune, fol
This utilizes Ray Tune's advanced search strategies and parallelism to efficiently optimize your model's hyperparameters. For more information, check out the [Ray Tune documentation](https://docs.ray.io/en/latest/tune/index.html).
### What are the default hyperparameters for YOLOv8 tuning with Ray Tune?
### What are the default hyperparameters for YOLO11 tuning with Ray Tune?
Ultralytics YOLOv8 uses the following default hyperparameters for tuning with Ray Tune:
Ultralytics YOLO11 uses the following default hyperparameters for tuning with Ray Tune:
| Parameter | Value Range | Description |
| --------------- | -------------------------- | ------------------------------ |
@ -229,9 +229,9 @@ Ultralytics YOLOv8 uses the following default hyperparameters for tuning with Ra
These hyperparameters can be customized to suit your specific needs. For a complete list and more details, refer to the [Hyperparameter Tuning](../guides/hyperparameter-tuning.md) guide.
### How can I integrate Weights & Biases with my YOLOv8 model tuning?
### How can I integrate Weights & Biases with my YOLO11 model tuning?
To integrate Weights & Biases (W&B) with your Ultralytics YOLOv8 tuning process:
To integrate Weights & Biases (W&B) with your Ultralytics YOLO11 tuning process:
1. **Install W&B:**
@ -249,7 +249,7 @@ To integrate Weights & Biases (W&B) with your Ultralytics YOLOv8 tuning process:
wandb.init(project="YOLO-Tuning", entity="your-entity")
# Load YOLO model
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
# Tune hyperparameters
result_grid = model.tune(data="coco8.yaml", use_ray=True)
@ -257,7 +257,7 @@ To integrate Weights & Biases (W&B) with your Ultralytics YOLOv8 tuning process:
This setup will allow you to monitor the tuning process, track hyperparameter configurations, and visualize results in W&B.
### Why should I use Ray Tune for hyperparameter optimization with YOLOv8?
### Why should I use Ray Tune for hyperparameter optimization with YOLO11?
Ray Tune offers numerous advantages for hyperparameter optimization:
@ -265,18 +265,18 @@ Ray Tune offers numerous advantages for hyperparameter optimization:
- **Parallelism:** Supports parallel execution of multiple trials, significantly speeding up the tuning process.
- **Early Stopping:** Employs strategies like ASHA to terminate under-performing trials early, saving computational resources.
Ray Tune seamlessly integrates with Ultralytics YOLOv8, providing an easy-to-use interface for tuning hyperparameters effectively. To get started, check out the [Efficient Hyperparameter Tuning with Ray Tune and YOLOv8](../guides/hyperparameter-tuning.md) guide.
Ray Tune seamlessly integrates with Ultralytics YOLO11, providing an easy-to-use interface for tuning hyperparameters effectively. To get started, check out the [Efficient Hyperparameter Tuning with Ray Tune and YOLO11](../guides/hyperparameter-tuning.md) guide.
### How can I define a custom search space for YOLOv8 hyperparameter tuning?
### How can I define a custom search space for YOLO11 hyperparameter tuning?
To define a custom search space for your YOLOv8 hyperparameter tuning with Ray Tune:
To define a custom search space for your YOLO11 hyperparameter tuning with Ray Tune:
```python
from ray import tune
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
search_space = {"lr0": tune.uniform(1e-5, 1e-1), "momentum": tune.uniform(0.6, 0.98)}
result_grid = model.tune(data="coco8.yaml", space=search_space, use_ray=True)
```

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View file

@ -1,7 +1,7 @@
---
comments: true
description: Learn how to gather, label, and deploy data for custom YOLOv8 models using Roboflow's powerful tools. Optimize your computer vision pipeline effortlessly.
keywords: Roboflow, YOLOv8, data labeling, computer vision, model training, model deployment, dataset management, automated image annotation, AI tools
description: Learn how to gather, label, and deploy data for custom YOLO11 models using Roboflow's powerful tools. Optimize your computer vision pipeline effortlessly.
keywords: Roboflow, YOLO11, data labeling, computer vision, model training, model deployment, dataset management, automated image annotation, AI tools
---
# Roboflow
@ -17,17 +17,17 @@ keywords: Roboflow, YOLOv8, data labeling, computer vision, model training, mode
For more details see [Ultralytics Licensing](https://www.ultralytics.com/license).
In this guide, we are going to showcase how to find, label, and organize data for use in training a custom Ultralytics YOLOv8 model. Use the table of contents below to jump directly to a specific section:
In this guide, we are going to showcase how to find, label, and organize data for use in training a custom Ultralytics YOLO11 model. Use the table of contents below to jump directly to a specific section:
- Gather data for training a custom YOLOv8 model
- Upload, convert and label data for YOLOv8 format
- Gather data for training a custom YOLO11 model
- Upload, convert and label data for YOLO11 format
- Pre-process and augment data for model robustness
- Dataset management for [YOLOv8](../models/yolov8.md)
- Dataset management for [YOLO11](../models/yolov8.md)
- Export data in 40+ formats for model training
- Upload custom YOLOv8 model weights for testing and deployment
- Gather Data for Training a Custom YOLOv8 Model
- Upload custom YOLO11 model weights for testing and deployment
- Gather Data for Training a Custom YOLO11 Model
Roboflow provides two services that can help you collect data for YOLOv8 models: [Universe](https://universe.roboflow.com/?ref=ultralytics) and [Collect](https://github.com/roboflow/roboflow-collect?ref=ultralytics).
Roboflow provides two services that can help you collect data for YOLO11 models: [Universe](https://universe.roboflow.com/?ref=ultralytics) and [Collect](https://github.com/roboflow/roboflow-collect?ref=ultralytics).
Universe is an online repository with over 250,000 vision datasets totalling over 100 million images.
@ -41,21 +41,21 @@ With a [free Roboflow account](https://app.roboflow.com/?ref=ultralytics), you c
<img src="https://github.com/ultralytics/docs/releases/download/0/roboflow-universe-dataset-export.avif" alt="Roboflow Universe dataset export" width="800">
</p>
For YOLOv8, select "YOLOv8" as the export format:
For YOLO11, select "YOLO11" as the export format:
<p align="center">
<img src="https://github.com/ultralytics/docs/releases/download/0/roboflow-universe-dataset-export-1.avif" alt="Roboflow Universe dataset export" width="800">
</p>
Universe also has a page that aggregates all [public fine-tuned YOLOv8 models uploaded to Roboflow](https://universe.roboflow.com/search?q=model%3Ayolov8&ref=ultralytics). You can use this page to explore pre-trained models you can use for testing or [for automated data labeling](https://docs.roboflow.com/annotate/use-roboflow-annotate/model-assisted-labeling?ref=ultralytics) or to prototype with [Roboflow inference](https://github.com/roboflow/inference?ref=ultralytics).
Universe also has a page that aggregates all [public fine-tuned YOLO11 models uploaded to Roboflow](https://universe.roboflow.com/search?q=model%3Ayolov8&ref=ultralytics). You can use this page to explore pre-trained models you can use for testing or [for automated data labeling](https://docs.roboflow.com/annotate/use-roboflow-annotate/model-assisted-labeling?ref=ultralytics) or to prototype with [Roboflow inference](https://github.com/roboflow/inference?ref=ultralytics).
If you want to gather images yourself, try [Collect](https://github.com/roboflow/roboflow-collect), an open source project that allows you to automatically gather images using a webcam on the edge. You can use text or image prompts with Collect to instruct what data should be collected, allowing you to capture only the useful data you need to build your vision model.
## Upload, Convert and Label Data for YOLOv8 Format
## Upload, Convert and Label Data for YOLO11 Format
[Roboflow Annotate](https://docs.roboflow.com/annotate/use-roboflow-annotate?ref=ultralytics) is an online annotation tool for use in labeling images for [object detection](https://www.ultralytics.com/glossary/object-detection), classification, and segmentation.
To label data for a YOLOv8 object detection, [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), or classification model, first create a project in Roboflow.
To label data for a YOLO11 object detection, [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), or classification model, first create a project in Roboflow.
<p align="center">
<img src="https://github.com/ultralytics/docs/releases/download/0/create-roboflow-project.avif" alt="Create a Roboflow project" width="400">
@ -95,7 +95,7 @@ You can also add tags to images from the Tags panel in the sidebar. You can appl
<img src="https://github.com/ultralytics/docs/releases/download/0/adding-tags-to-image.avif" alt="Adding tags to an image in Roboflow" width="300">
</p>
Models hosted on Roboflow can be used with Label Assist, an automated annotation tool that uses your YOLOv8 model to recommend annotations. To use Label Assist, first upload a YOLOv8 model to Roboflow (see instructions later in the guide). Then, click the magic wand icon in the left sidebar and select your model for use in Label Assist.
Models hosted on Roboflow can be used with Label Assist, an automated annotation tool that uses your YOLO11 model to recommend annotations. To use Label Assist, first upload a YOLO11 model to Roboflow (see instructions later in the guide). Then, click the magic wand icon in the left sidebar and select your model for use in Label Assist.
Choose a model, then click "Continue" to enable Label Assist:
@ -109,7 +109,7 @@ When you open new images for annotation, Label Assist will trigger and recommend
<img src="https://github.com/ultralytics/docs/releases/download/0/rf-label-assist.avif" alt="ALabel Assist recommending an annotation" width="800">
</p>
## Dataset Management for YOLOv8
## Dataset Management for YOLO11
Roboflow provides a suite of tools for understanding computer vision datasets.
@ -157,13 +157,13 @@ When your dataset version has been generated, you can export your data into a ra
<img src="https://github.com/ultralytics/docs/releases/download/0/exporting-dataset.avif" alt="Exporting a dataset" width="800">
</p>
You are now ready to train YOLOv8 on a custom dataset. Follow this [written guide](https://blog.roboflow.com/how-to-train-yolov8-on-a-custom-dataset/?ref=ultralytics) and [YouTube video](https://www.youtube.com/watch?v=wuZtUMEiKWY) for step-by-step instructions or refer to the [Ultralytics documentation](../modes/train.md).
You are now ready to train YOLO11 on a custom dataset. Follow this [written guide](https://blog.roboflow.com/how-to-train-yolov8-on-a-custom-dataset/?ref=ultralytics) and [YouTube video](https://www.youtube.com/watch?v=wuZtUMEiKWY) for step-by-step instructions or refer to the [Ultralytics documentation](../modes/train.md).
## Upload Custom YOLOv8 Model Weights for Testing and Deployment
## Upload Custom YOLO11 Model Weights for Testing and Deployment
Roboflow offers an infinitely scalable API for deployed models and SDKs for use with NVIDIA Jetsons, Luxonis OAKs, Raspberry Pis, GPU-based devices, and more.
You can deploy YOLOv8 models by uploading YOLOv8 weights to Roboflow. You can do this in a few lines of Python code. Create a new Python file and add the following code:
You can deploy YOLO11 models by uploading YOLO11 weights to Roboflow. You can do this in a few lines of Python code. Create a new Python file and add the following code:
```python
import roboflow # install with 'pip install roboflow'
@ -190,7 +190,7 @@ To test your model and find deployment instructions for supported SDKs, go to th
You can also use your uploaded model as a [labeling assistant](https://docs.roboflow.com/annotate/use-roboflow-annotate/model-assisted-labeling?ref=ultralytics). This feature uses your trained model to recommend annotations on images uploaded to Roboflow.
## How to Evaluate YOLOv8 Models
## How to Evaluate YOLO11 Models
Roboflow provides a range of features for use in evaluating models.
@ -224,17 +224,17 @@ You can use Vector Analysis to:
## Learning Resources
Want to learn more about using Roboflow for creating YOLOv8 models? The following resources may be helpful in your work.
Want to learn more about using Roboflow for creating YOLO11 models? The following resources may be helpful in your work.
- [Train YOLOv8 on a Custom Dataset](https://github.com/roboflow/notebooks/blob/main/notebooks/train-yolov8-object-detection-on-custom-dataset.ipynb): Follow our interactive notebook that shows you how to train a YOLOv8 model on a custom dataset.
- [Autodistill](https://docs.autodistill.com/): Use large foundation vision models to label data for specific models. You can label images for use in training YOLOv8 classification, detection, and segmentation models with Autodistill.
- [Train YOLO11 on a Custom Dataset](https://github.com/roboflow/notebooks/blob/main/notebooks/train-yolov8-object-detection-on-custom-dataset.ipynb): Follow our interactive notebook that shows you how to train a YOLO11 model on a custom dataset.
- [Autodistill](https://docs.autodistill.com/): Use large foundation vision models to label data for specific models. You can label images for use in training YOLO11 classification, detection, and segmentation models with Autodistill.
- [Supervision](https://supervision.roboflow.com/?ref=ultralytics): A Python package with helpful utilities for use in working with computer vision models. You can use supervision to filter detections, compute confusion matrices, and more, all in a few lines of Python code.
- [Roboflow Blog](https://blog.roboflow.com/?ref=ultralytics): The Roboflow Blog features over 500 articles on computer vision, covering topics from how to train a YOLOv8 model to annotation best practices.
- [Roboflow YouTube channel](https://www.youtube.com/@Roboflow): Browse dozens of in-depth computer vision guides on our YouTube channel, covering topics from training YOLOv8 models to automated image labeling.
- [Roboflow Blog](https://blog.roboflow.com/?ref=ultralytics): The Roboflow Blog features over 500 articles on computer vision, covering topics from how to train a YOLO11 model to annotation best practices.
- [Roboflow YouTube channel](https://www.youtube.com/@Roboflow): Browse dozens of in-depth computer vision guides on our YouTube channel, covering topics from training YOLO11 models to automated image labeling.
## Project Showcase
Below are a few of the many pieces of feedback we have received for using YOLOv8 and Roboflow together to create computer vision models.
Below are a few of the many pieces of feedback we have received for using YOLO11 and Roboflow together to create computer vision models.
<p align="center">
<img src="https://github.com/ultralytics/docs/releases/download/0/rf-showcase-1.avif" alt="Showcase image" width="500">
@ -244,26 +244,26 @@ Below are a few of the many pieces of feedback we have received for using YOLOv8
## FAQ
### How do I label data for YOLOv8 models using Roboflow?
### How do I label data for YOLO11 models using Roboflow?
Labeling data for YOLOv8 models using Roboflow is straightforward with Roboflow Annotate. First, create a project on Roboflow and upload your images. After uploading, select the batch of images and click "Start Annotating." You can use the `B` key for bounding boxes or the `P` key for polygons. For faster annotation, use the SAM-based label assistant by clicking the cursor icon in the sidebar. Detailed steps can be found [here](#upload-convert-and-label-data-for-yolov8-format).
Labeling data for YOLO11 models using Roboflow is straightforward with Roboflow Annotate. First, create a project on Roboflow and upload your images. After uploading, select the batch of images and click "Start Annotating." You can use the `B` key for bounding boxes or the `P` key for polygons. For faster annotation, use the SAM-based label assistant by clicking the cursor icon in the sidebar. Detailed steps can be found [here](#upload-convert-and-label-data-for-yolo11-format).
### What services does Roboflow offer for collecting YOLOv8 [training data](https://www.ultralytics.com/glossary/training-data)?
### What services does Roboflow offer for collecting YOLO11 [training data](https://www.ultralytics.com/glossary/training-data)?
Roboflow provides two key services for collecting YOLOv8 training data: [Universe](https://universe.roboflow.com/?ref=ultralytics) and [Collect](https://github.com/roboflow/roboflow-collect?ref=ultralytics). Universe offers access to over 250,000 vision datasets, while Collect helps you gather images using a webcam and automated prompts.
Roboflow provides two key services for collecting YOLO11 training data: [Universe](https://universe.roboflow.com/?ref=ultralytics) and [Collect](https://github.com/roboflow/roboflow-collect?ref=ultralytics). Universe offers access to over 250,000 vision datasets, while Collect helps you gather images using a webcam and automated prompts.
### How can I manage and analyze my YOLOv8 dataset using Roboflow?
### How can I manage and analyze my YOLO11 dataset using Roboflow?
Roboflow offers robust dataset management tools, including dataset search, tagging, and Health Check. Use the search feature to find images based on text descriptions or tags. Health Check provides insights into dataset quality, showing class balance, image sizes, and annotation heatmaps. This helps optimize dataset performance before training YOLOv8 models. Detailed information can be found [here](#dataset-management-for-yolov8).
Roboflow offers robust dataset management tools, including dataset search, tagging, and Health Check. Use the search feature to find images based on text descriptions or tags. Health Check provides insights into dataset quality, showing class balance, image sizes, and annotation heatmaps. This helps optimize dataset performance before training YOLO11 models. Detailed information can be found [here](#dataset-management-for-yolo11).
### How do I export my YOLOv8 dataset from Roboflow?
### How do I export my YOLO11 dataset from Roboflow?
To export your YOLOv8 dataset from Roboflow, you need to create a dataset version. Click "Versions" in the sidebar, then "Create New Version" and apply any desired augmentations. Once the version is generated, click "Export Dataset" and choose the YOLOv8 format. Follow this process [here](#export-data-in-40-formats-for-model-training).
To export your YOLO11 dataset from Roboflow, you need to create a dataset version. Click "Versions" in the sidebar, then "Create New Version" and apply any desired augmentations. Once the version is generated, click "Export Dataset" and choose the YOLO11 format. Follow this process [here](#export-data-in-40-formats-for-model-training).
### How can I integrate and deploy YOLOv8 models with Roboflow?
### How can I integrate and deploy YOLO11 models with Roboflow?
Integrate and deploy YOLOv8 models on Roboflow by uploading your YOLOv8 weights through a few lines of Python code. Use the provided script to authenticate and upload your model, which will create an API for deployment. For details on the script and further instructions, see [this section](#upload-custom-yolov8-model-weights-for-testing-and-deployment).
Integrate and deploy YOLO11 models on Roboflow by uploading your YOLO11 weights through a few lines of Python code. Use the provided script to authenticate and upload your model, which will create an API for deployment. For details on the script and further instructions, see [this section](#upload-custom-yolo11-model-weights-for-testing-and-deployment).
### What tools does Roboflow provide for evaluating YOLOv8 models?
### What tools does Roboflow provide for evaluating YOLO11 models?
Roboflow offers model evaluation tools, including a confusion matrix and vector analysis plots. Access these tools from the "View Detailed Evaluation" button on your model page. These features help identify model performance issues and find areas for improvement. For more information, refer to [this section](#how-to-evaluate-yolov8-models).
Roboflow offers model evaluation tools, including a confusion matrix and vector analysis plots. Access these tools from the "View Detailed Evaluation" button on your model page. These features help identify model performance issues and find areas for improvement. For more information, refer to [this section](#how-to-evaluate-yolo11-models).

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@ -1,14 +1,14 @@
---
comments: true
description: Learn how to integrate YOLOv8 with TensorBoard for real-time visual insights into your model's training metrics, performance graphs, and debugging workflows.
keywords: YOLOv8, TensorBoard, model training, visualization, machine learning, deep learning, Ultralytics, training metrics, performance analysis
description: Learn how to integrate YOLO11 with TensorBoard for real-time visual insights into your model's training metrics, performance graphs, and debugging workflows.
keywords: YOLO11, TensorBoard, model training, visualization, machine learning, deep learning, Ultralytics, training metrics, performance analysis
---
# Gain Visual Insights with YOLOv8's Integration with TensorBoard
# Gain Visual Insights with YOLO11's Integration with TensorBoard
Understanding and fine-tuning [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models like [Ultralytics' YOLOv8](https://www.ultralytics.com/) becomes more straightforward when you take a closer look at their training processes. Model training visualization helps with getting insights into the model's learning patterns, performance metrics, and overall behavior. YOLOv8's integration with TensorBoard makes this process of visualization and analysis easier and enables more efficient and informed adjustments to the model.
Understanding and fine-tuning [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models like [Ultralytics' YOLO11](https://www.ultralytics.com/) becomes more straightforward when you take a closer look at their training processes. Model training visualization helps with getting insights into the model's learning patterns, performance metrics, and overall behavior. YOLO11's integration with TensorBoard makes this process of visualization and analysis easier and enables more efficient and informed adjustments to the model.
This guide covers how to use TensorBoard with YOLOv8. You'll learn about various visualizations, from tracking metrics to analyzing model graphs. These tools will help you understand your YOLOv8 model's performance better.
This guide covers how to use TensorBoard with YOLO11. You'll learn about various visualizations, from tracking metrics to analyzing model graphs. These tools will help you understand your YOLO11 model's performance better.
## TensorBoard
@ -18,9 +18,9 @@ This guide covers how to use TensorBoard with YOLOv8. You'll learn about various
[TensorBoard](https://www.tensorflow.org/tensorboard), [TensorFlow](https://www.ultralytics.com/glossary/tensorflow)'s visualization toolkit, is essential for [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) experimentation. TensorBoard features a range of visualization tools, crucial for monitoring machine learning models. These tools include tracking key metrics like loss and accuracy, visualizing model graphs, and viewing histograms of weights and biases over time. It also provides capabilities for projecting [embeddings](https://www.ultralytics.com/glossary/embeddings) to lower-dimensional spaces and displaying multimedia data.
## YOLOv8 Training with TensorBoard
## YOLO11 Training with TensorBoard
Using TensorBoard while training YOLOv8 models is straightforward and offers significant benefits.
Using TensorBoard while training YOLO11 models is straightforward and offers significant benefits.
## Installation
@ -31,13 +31,13 @@ To install the required package, run:
=== "CLI"
```bash
# Install the required package for YOLOv8 and Tensorboard
# Install the required package for YOLO11 and Tensorboard
pip install ultralytics
```
TensorBoard is conveniently pre-installed with YOLOv8, eliminating the need for additional setup for visualization purposes.
TensorBoard is conveniently pre-installed with YOLO11, eliminating the need for additional setup for visualization purposes.
For detailed instructions and best practices related to the installation process, be sure to check our [YOLOv8 Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
For detailed instructions and best practices related to the installation process, be sure to check our [YOLO11 Installation guide](../quickstart.md). While installing the required packages for YOLO11, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
## Configuring TensorBoard for Google Colab
@ -54,7 +54,7 @@ When using Google Colab, it's important to set up TensorBoard before starting yo
## Usage
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.
Before diving into the usage instructions, be sure to check out the range of [YOLO11 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
!!! example "Usage"
@ -64,7 +64,7 @@ Before diving into the usage instructions, be sure to check out the range of [YO
from ultralytics import YOLO
# Load a pre-trained model
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
# Train the model
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
@ -76,17 +76,17 @@ Upon running the usage code snippet above, you can expect the following output:
TensorBoard: Start with 'tensorboard --logdir path_to_your_tensorboard_logs', view at http://localhost:6006/
```
This output indicates that TensorBoard is now actively monitoring your YOLOv8 training session. You can access the TensorBoard dashboard by visiting the provided URL (http://localhost:6006/) to view real-time training metrics and model performance. For users working in Google Colab, the TensorBoard will be displayed in the same cell where you executed the TensorBoard configuration commands.
This output indicates that TensorBoard is now actively monitoring your YOLO11 training session. You can access the TensorBoard dashboard by visiting the provided URL (http://localhost:6006/) to view real-time training metrics and model performance. For users working in Google Colab, the TensorBoard will be displayed in the same cell where you executed the TensorBoard configuration commands.
For more information related to the model training process, be sure to check our [YOLOv8 Model Training guide](../modes/train.md). If you are interested in learning more about logging, checkpoints, plotting, and file management, read our [usage guide on configuration](../usage/cfg.md).
For more information related to the model training process, be sure to check our [YOLO11 Model Training guide](../modes/train.md). If you are interested in learning more about logging, checkpoints, plotting, and file management, read our [usage guide on configuration](../usage/cfg.md).
## Understanding Your TensorBoard for YOLOv8 Training
## Understanding Your TensorBoard for YOLO11 Training
Now, let's focus on understanding the various features and components of TensorBoard in the context of YOLOv8 training. The three key sections of the TensorBoard are Time Series, Scalars, and Graphs.
Now, let's focus on understanding the various features and components of TensorBoard in the context of YOLO11 training. The three key sections of the TensorBoard are Time Series, Scalars, and Graphs.
### Time Series
The Time Series feature in the TensorBoard offers a dynamic and detailed perspective of various training metrics over time for YOLOv8 models. It focuses on the progression and trends of metrics across training epochs. Here's an example of what you can expect to see.
The Time Series feature in the TensorBoard offers a dynamic and detailed perspective of various training metrics over time for YOLO11 models. It focuses on the progression and trends of metrics across training epochs. Here's an example of what you can expect to see.
![image](https://github.com/ultralytics/docs/releases/download/0/time-series-tensorboard-yolov8.avif)
@ -100,13 +100,13 @@ The Time Series feature in the TensorBoard offers a dynamic and detailed perspec
- **In-Depth Analysis**: Time Series provides an in-depth analysis of each metric. For instance, different learning rate segments are shown, offering insights into how adjustments in learning rate impact the model's learning curve.
#### Importance of Time Series in YOLOv8 Training
#### Importance of Time Series in YOLO11 Training
The Time Series section is essential for a thorough analysis of the YOLOv8 model's training progress. It lets you track the metrics in real time to promptly identify and solve issues. It also offers a detailed view of each metrics progression, which is crucial for fine-tuning the model and enhancing its performance.
The Time Series section is essential for a thorough analysis of the YOLO11 model's training progress. It lets you track the metrics in real time to promptly identify and solve issues. It also offers a detailed view of each metrics progression, which is crucial for fine-tuning the model and enhancing its performance.
### Scalars
Scalars in the TensorBoard are crucial for plotting and analyzing simple metrics like loss and accuracy during the training of YOLOv8 models. They offer a clear and concise view of how these metrics evolve with each training [epoch](https://www.ultralytics.com/glossary/epoch), providing insights into the model's learning effectiveness and stability. Here's an example of what you can expect to see.
Scalars in the TensorBoard are crucial for plotting and analyzing simple metrics like loss and accuracy during the training of YOLO11 models. They offer a clear and concise view of how these metrics evolve with each training [epoch](https://www.ultralytics.com/glossary/epoch), providing insights into the model's learning effectiveness and stability. Here's an example of what you can expect to see.
![image](https://github.com/ultralytics/docs/releases/download/0/scalars-metrics-tensorboard.avif)
@ -130,7 +130,7 @@ Scalars in the TensorBoard are crucial for plotting and analyzing simple metrics
#### Importance of Monitoring Scalars
Observing scalar metrics is crucial for fine-tuning the YOLOv8 model. Variations in these metrics, such as spikes or irregular patterns in loss graphs, can highlight potential issues such as [overfitting](https://www.ultralytics.com/glossary/overfitting), [underfitting](https://www.ultralytics.com/glossary/underfitting), or inappropriate learning rate settings. By closely monitoring these scalars, you can make informed decisions to optimize the training process, ensuring that the model learns effectively and achieves the desired performance.
Observing scalar metrics is crucial for fine-tuning the YOLO11 model. Variations in these metrics, such as spikes or irregular patterns in loss graphs, can highlight potential issues such as [overfitting](https://www.ultralytics.com/glossary/overfitting), [underfitting](https://www.ultralytics.com/glossary/underfitting), or inappropriate learning rate settings. By closely monitoring these scalars, you can make informed decisions to optimize the training process, ensuring that the model learns effectively and achieves the desired performance.
### Difference Between Scalars and Time Series
@ -138,15 +138,15 @@ While both Scalars and Time Series in TensorBoard are used for tracking metrics,
### Graphs
The Graphs section of the TensorBoard visualizes the computational graph of the YOLOv8 model, showing how operations and data flow within the model. It's a powerful tool for understanding the model's structure, ensuring that all layers are connected correctly, and for identifying any potential bottlenecks in data flow. Here's an example of what you can expect to see.
The Graphs section of the TensorBoard visualizes the computational graph of the YOLO11 model, showing how operations and data flow within the model. It's a powerful tool for understanding the model's structure, ensuring that all layers are connected correctly, and for identifying any potential bottlenecks in data flow. Here's an example of what you can expect to see.
![image](https://github.com/ultralytics/docs/releases/download/0/tensorboard-yolov8-computational-graph.avif)
Graphs are particularly useful for debugging the model, especially in complex architectures typical in [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models like YOLOv8. They help in verifying layer connections and the overall design of the model.
Graphs are particularly useful for debugging the model, especially in complex architectures typical in [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models like YOLO11. They help in verifying layer connections and the overall design of the model.
## Summary
This guide aims to help you use TensorBoard with YOLOv8 for visualization and analysis of machine learning model training. It focuses on explaining how key TensorBoard features can provide insights into training metrics and model performance during YOLOv8 training sessions.
This guide aims to help you use TensorBoard with YOLO11 for visualization and analysis of machine learning model training. It focuses on explaining how key TensorBoard features can provide insights into training metrics and model performance during YOLO11 training sessions.
For a more detailed exploration of these features and effective utilization strategies, you can refer to TensorFlow's official [TensorBoard documentation](https://www.tensorflow.org/tensorboard/get_started) and their [GitHub repository](https://github.com/tensorflow/tensorboard).
@ -154,29 +154,29 @@ Want to learn more about the various integrations of Ultralytics? Check out the
## FAQ
### What benefits does using TensorBoard with YOLOv8 offer?
### What benefits does using TensorBoard with YOLO11 offer?
Using TensorBoard with YOLOv8 provides several visualization tools essential for efficient model training:
Using TensorBoard with YOLO11 provides several visualization tools essential for efficient model training:
- **Real-Time Metrics Tracking:** Track key metrics such as loss, accuracy, precision, and recall live.
- **Model Graph Visualization:** Understand and debug the model architecture by visualizing computational graphs.
- **Embedding Visualization:** Project embeddings to lower-dimensional spaces for better insight.
These tools enable you to make informed adjustments to enhance your YOLOv8 model's performance. For more details on TensorBoard features, check out the TensorFlow [TensorBoard guide](https://www.tensorflow.org/tensorboard/get_started).
These tools enable you to make informed adjustments to enhance your YOLO11 model's performance. For more details on TensorBoard features, check out the TensorFlow [TensorBoard guide](https://www.tensorflow.org/tensorboard/get_started).
### How can I monitor training metrics using TensorBoard when training a YOLOv8 model?
### How can I monitor training metrics using TensorBoard when training a YOLO11 model?
To monitor training metrics while training a YOLOv8 model with TensorBoard, follow these steps:
To monitor training metrics while training a YOLO11 model with TensorBoard, follow these steps:
1. **Install TensorBoard and YOLOv8:** Run `pip install ultralytics` which includes TensorBoard.
2. **Configure TensorBoard Logging:** During the training process, YOLOv8 logs metrics to a specified log directory.
1. **Install TensorBoard and YOLO11:** Run `pip install ultralytics` which includes TensorBoard.
2. **Configure TensorBoard Logging:** During the training process, YOLO11 logs metrics to a specified log directory.
3. **Start TensorBoard:** Launch TensorBoard using the command `tensorboard --logdir path/to/your/tensorboard/logs`.
The TensorBoard dashboard, accessible via [http://localhost:6006/](http://localhost:6006/), provides real-time insights into various training metrics. For a deeper dive into training configurations, visit our [YOLOv8 Configuration guide](../usage/cfg.md).
The TensorBoard dashboard, accessible via [http://localhost:6006/](http://localhost:6006/), provides real-time insights into various training metrics. For a deeper dive into training configurations, visit our [YOLO11 Configuration guide](../usage/cfg.md).
### What kind of metrics can I visualize with TensorBoard when training YOLOv8 models?
### What kind of metrics can I visualize with TensorBoard when training YOLO11 models?
When training YOLOv8 models, TensorBoard allows you to visualize an array of important metrics including:
When training YOLO11 models, TensorBoard allows you to visualize an array of important metrics including:
- **Loss (Training and Validation):** Indicates how well the model is performing during training and validation.
- **Accuracy/Precision/[Recall](https://www.ultralytics.com/glossary/recall):** Key performance metrics to evaluate detection accuracy.
@ -185,9 +185,9 @@ When training YOLOv8 models, TensorBoard allows you to visualize an array of imp
These visualizations are essential for tracking model performance and making necessary optimizations. For more information on these metrics, refer to our [Performance Metrics guide](../guides/yolo-performance-metrics.md).
### Can I use TensorBoard in a Google Colab environment for training YOLOv8?
### Can I use TensorBoard in a Google Colab environment for training YOLO11?
Yes, you can use TensorBoard in a Google Colab environment to train YOLOv8 models. Here's a quick setup:
Yes, you can use TensorBoard in a Google Colab environment to train YOLO11 models. Here's a quick setup:
!!! example "Configure TensorBoard for Google Colab"
@ -198,16 +198,16 @@ Yes, you can use TensorBoard in a Google Colab environment to train YOLOv8 model
%tensorboard --logdir path/to/runs
```
Then, run the YOLOv8 training script:
Then, run the YOLO11 training script:
```python
from ultralytics import YOLO
# Load a pre-trained model
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
# Train the model
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
```
TensorBoard will visualize the training progress within Colab, providing real-time insights into metrics like loss and accuracy. For additional details on configuring YOLOv8 training, see our detailed [YOLOv8 Installation guide](../quickstart.md).
TensorBoard will visualize the training progress within Colab, providing real-time insights into metrics like loss and accuracy. For additional details on configuring YOLO11 training, see our detailed [YOLO11 Installation guide](../quickstart.md).

90
docs/en/integrations/tf-graphdef.md
View file

@ -1,14 +1,14 @@
---
comments: true
description: Learn how to export YOLOv8 models to the TF GraphDef format for seamless deployment on various platforms, including mobile and web.
keywords: YOLOv8, export, TensorFlow, GraphDef, model deployment, TensorFlow Serving, TensorFlow Lite, TensorFlow.js, machine learning, AI, computer vision
description: Learn how to export YOLO11 models to the TF GraphDef format for seamless deployment on various platforms, including mobile and web.
keywords: YOLO11, export, TensorFlow, GraphDef, model deployment, TensorFlow Serving, TensorFlow Lite, TensorFlow.js, machine learning, AI, computer vision
---
# How to Export to TF GraphDef from YOLOv8 for Deployment
# How to Export to TF GraphDef from YOLO11 for Deployment
When you are deploying cutting-edge [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models, like YOLOv8, in different environments, you might run into compatibility issues. Google's [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) GraphDef, or TF GraphDef, offers a solution by providing a serialized, platform-independent representation of your model. Using the TF GraphDef model format, you can deploy your YOLOv8 model in environments where the complete TensorFlow ecosystem may not be available, such as mobile devices or specialized hardware.
When you are deploying cutting-edge [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models, like YOLO11, in different environments, you might run into compatibility issues. Google's [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) GraphDef, or TF GraphDef, offers a solution by providing a serialized, platform-independent representation of your model. Using the TF GraphDef model format, you can deploy your YOLO11 model in environments where the complete TensorFlow ecosystem may not be available, such as mobile devices or specialized hardware.
In this guide, we'll walk you step by step through how to export your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models to the TF GraphDef model format. By converting your model, you can streamline deployment and use YOLOv8's computer vision capabilities in a broader range of applications and platforms.
In this guide, we'll walk you step by step through how to export your [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) models to the TF GraphDef model format. By converting your model, you can streamline deployment and use YOLO11's computer vision capabilities in a broader range of applications and platforms.
<p align="center">
<img width="640" src="https://github.com/ultralytics/docs/releases/download/0/tensorflow-graphdef.avif" alt="TensorFlow GraphDef">
@ -16,11 +16,11 @@ In this guide, we'll walk you step by step through how to export your [Ultralyti
## Why Should You Export to TF GraphDef?
TF GraphDef is a powerful component of the TensorFlow ecosystem that was developed by Google. It can be used to optimize and deploy models like YOLOv8. Exporting to TF GraphDef lets us move models from research to real-world applications. It allows models to run in environments without the full TensorFlow framework.
TF GraphDef is a powerful component of the TensorFlow ecosystem that was developed by Google. It can be used to optimize and deploy models like YOLO11. Exporting to TF GraphDef lets us move models from research to real-world applications. It allows models to run in environments without the full TensorFlow framework.
The GraphDef format represents the model as a serialized computation graph. This enables various optimization techniques like constant folding, quantization, and graph transformations. These optimizations ensure efficient execution, reduced memory usage, and faster inference speeds.
GraphDef models can use hardware accelerators such as GPUs, TPUs, and AI chips, unlocking significant performance gains for the YOLOv8 inference pipeline. The TF GraphDef format creates a self-contained package with the model and its dependencies, simplifying deployment and integration into diverse systems.
GraphDef models can use hardware accelerators such as GPUs, TPUs, and AI chips, unlocking significant performance gains for the YOLO11 inference pipeline. The TF GraphDef format creates a self-contained package with the model and its dependencies, simplifying deployment and integration into diverse systems.
## Key Features of TF GraphDef Models
@ -38,7 +38,7 @@ Here's a look at its key characteristics:
## Deployment Options with TF GraphDef
Before we dive into the process of exporting YOLOv8 models to TF GraphDef, let's take a look at some typical deployment situations where this format is used.
Before we dive into the process of exporting YOLO11 models to TF GraphDef, let's take a look at some typical deployment situations where this format is used.
Here's how you can deploy with TF GraphDef efficiently across various platforms.
@ -46,13 +46,13 @@ Here's how you can deploy with TF GraphDef efficiently across various platforms.
- **Mobile and Embedded Devices:** With tools like TensorFlow Lite, you can convert TF GraphDef models into formats optimized for smartphones, tablets, and various embedded devices. Your models can then be used for on-device inference, where execution is done locally, often providing performance gains and offline capabilities.
- **Web Browsers:** TensorFlow.js enables the deployment of TF GraphDef models directly within web browsers. It paves the way for real-time object detection applications running on the client side, using the capabilities of YOLOv8 through JavaScript.
- **Web Browsers:** TensorFlow.js enables the deployment of TF GraphDef models directly within web browsers. It paves the way for real-time object detection applications running on the client side, using the capabilities of YOLO11 through JavaScript.
- **Specialized Hardware:** TF GraphDef's platform-agnostic nature allows it to target custom hardware, such as accelerators and TPUs (Tensor Processing Units). These devices can provide performance advantages for computationally intensive models.
## Exporting YOLOv8 Models to TF GraphDef
## Exporting YOLO11 Models to TF GraphDef
You can convert your YOLOv8 object detection model to the TF GraphDef format, which is compatible with various systems, to improve its performance across platforms.
You can convert your YOLO11 object detection model to the TF GraphDef format, which is compatible with various systems, to improve its performance across platforms.
### Installation
@ -63,15 +63,15 @@ To install the required package, run:
=== "CLI"
```bash
# Install the required package for YOLOv8
# Install the required package for YOLO11
pip install ultralytics
```
For detailed instructions and best practices related to the installation process, check our [Ultralytics Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
For detailed instructions and best practices related to the installation process, check our [Ultralytics Installation guide](../quickstart.md). While installing the required packages for YOLO11, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
### Usage
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).
Before diving into the usage instructions, it's important to note that while all [Ultralytics YOLO11 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"
@ -80,14 +80,14 @@ Before diving into the usage instructions, it's important to note that while all
```python
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO("yolov8n.pt")
# Load the YOLO11 model
model = YOLO("yolo11n.pt")
# Export the model to TF GraphDef format
model.export(format="pb") # creates 'yolov8n.pb'
model.export(format="pb") # creates 'yolo11n.pb'
# Load the exported TF GraphDef model
tf_graphdef_model = YOLO("yolov8n.pb")
tf_graphdef_model = YOLO("yolo11n.pb")
# Run inference
results = tf_graphdef_model("https://ultralytics.com/images/bus.jpg")
@ -96,18 +96,18 @@ Before diving into the usage instructions, it's important to note that while all
=== "CLI"
```bash
# Export a YOLOv8n PyTorch model to TF GraphDef format
yolo export model=yolov8n.pt format=pb # creates 'yolov8n.pb'
# Export a YOLO11n PyTorch model to TF GraphDef format
yolo export model=yolo11n.pt format=pb # creates 'yolo11n.pb'
# Run inference with the exported model
yolo predict model='yolov8n.pb' source='https://ultralytics.com/images/bus.jpg'
yolo predict model='yolo11n.pb' source='https://ultralytics.com/images/bus.jpg'
```
For more details about supported export options, visit the [Ultralytics documentation page on deployment options](../guides/model-deployment-options.md).
## Deploying Exported YOLOv8 TF GraphDef Models
## Deploying Exported YOLO11 TF GraphDef Models
Once you've exported your YOLOv8 model to the TF GraphDef format, the next step is deployment. The primary and recommended first step for running a TF GraphDef model is to use the YOLO("model.pb") method, as previously shown in the usage code snippet.
Once you've exported your YOLO11 model to the TF GraphDef format, the next step is deployment. The primary and recommended first step for running a TF GraphDef model is to use the YOLO("model.pb") method, as previously shown in the usage code snippet.
However, for more information on deploying your TF GraphDef models, take a look at the following resources:
@ -119,17 +119,17 @@ However, for more information on deploying your TF GraphDef models, take a look
## Summary
In this guide, we explored how to export Ultralytics YOLOv8 models to the TF GraphDef format. By doing this, you can flexibly deploy your optimized YOLOv8 models in different environments.
In this guide, we explored how to export Ultralytics YOLO11 models to the TF GraphDef format. By doing this, you can flexibly deploy your optimized YOLO11 models in different environments.
For further details on usage, visit the [TF GraphDef official documentation](https://www.tensorflow.org/api_docs/python/tf/Graph).
For more information on integrating Ultralytics YOLOv8 with other platforms and frameworks, don't forget to check out our [integration guide page](index.md). It has great resources and insights to help you make the most of YOLOv8 in your projects.
For more information on integrating Ultralytics YOLO11 with other platforms and frameworks, don't forget to check out our [integration guide page](index.md). It has great resources and insights to help you make the most of YOLO11 in your projects.
## FAQ
### How do I export a YOLOv8 model to TF GraphDef format?
### How do I export a YOLO11 model to TF GraphDef format?
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:
Ultralytics YOLO11 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 YOLO11 model to TF GraphDef, follow these steps:
!!! example "Usage"
@ -138,14 +138,14 @@ Ultralytics YOLOv8 models can be exported to TensorFlow GraphDef (TF GraphDef) f
```python
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO("yolov8n.pt")
# Load the YOLO11 model
model = YOLO("yolo11n.pt")
# Export the model to TF GraphDef format
model.export(format="pb") # creates 'yolov8n.pb'
model.export(format="pb") # creates 'yolo11n.pb'
# Load the exported TF GraphDef model
tf_graphdef_model = YOLO("yolov8n.pb")
tf_graphdef_model = YOLO("yolo11n.pb")
# Run inference
results = tf_graphdef_model("https://ultralytics.com/images/bus.jpg")
@ -154,18 +154,18 @@ Ultralytics YOLOv8 models can be exported to TensorFlow GraphDef (TF GraphDef) f
=== "CLI"
```bash
# Export a YOLOv8n PyTorch model to TF GraphDef format
yolo export model="yolov8n.pt" format="pb" # creates 'yolov8n.pb'
# Export a YOLO11n PyTorch model to TF GraphDef format
yolo export model="yolo11n.pt" format="pb" # creates 'yolo11n.pb'
# Run inference with the exported model
yolo predict model="yolov8n.pb" source="https://ultralytics.com/images/bus.jpg"
yolo predict model="yolo11n.pb" source="https://ultralytics.com/images/bus.jpg"
```
For more information on different export options, visit the [Ultralytics documentation on model export](../modes/export.md).
### What are the benefits of using TF GraphDef for YOLOv8 model deployment?
### What are the benefits of using TF GraphDef for YOLO11 model deployment?
Exporting YOLOv8 models to the TF GraphDef format offers multiple advantages, including:
Exporting YOLO11 models to the TF GraphDef format offers multiple advantages, including:
1. **Platform Independence**: TF GraphDef provides a platform-independent format, allowing models to be deployed across various environments including mobile and web browsers.
2. **Optimizations**: The format enables several optimizations, such as constant folding, quantization, and graph transformations, which enhance execution efficiency and reduce memory usage.
@ -173,19 +173,19 @@ Exporting YOLOv8 models to the TF GraphDef format offers multiple advantages, in
Read more about the benefits in the [TF GraphDef section](#why-should-you-export-to-tf-graphdef) of our documentation.
### Why should I use Ultralytics YOLOv8 over other [object detection](https://www.ultralytics.com/glossary/object-detection) models?
### Why should I use Ultralytics YOLO11 over other [object detection](https://www.ultralytics.com/glossary/object-detection) models?
Ultralytics YOLOv8 offers numerous advantages compared to other models like YOLOv5 and YOLOv7. Some key benefits include:
Ultralytics YOLO11 offers numerous advantages compared to other models like YOLOv5 and YOLOv7. Some key benefits include:
1. **State-of-the-Art Performance**: YOLOv8 provides exceptional speed and [accuracy](https://www.ultralytics.com/glossary/accuracy) for real-time object detection, segmentation, and classification.
1. **State-of-the-Art Performance**: YOLO11 provides exceptional speed and [accuracy](https://www.ultralytics.com/glossary/accuracy) for real-time object detection, segmentation, and classification.
2. **Ease of Use**: Features a user-friendly API for model training, validation, prediction, and export, making it accessible for both beginners and experts.
3. **Broad Compatibility**: Supports multiple export formats including ONNX, TensorRT, CoreML, and TensorFlow, for versatile deployment options.
Explore further details in our [introduction to YOLOv8](https://docs.ultralytics.com/models/yolov8/).
Explore further details in our [introduction to YOLO11](https://docs.ultralytics.com/models/yolov8/).
### How can I deploy a YOLOv8 model on specialized hardware using TF GraphDef?
### How can I deploy a YOLO11 model on specialized hardware using TF GraphDef?
Once a YOLOv8 model is exported to TF GraphDef format, you can deploy it across various specialized hardware platforms. Typical deployment scenarios include:
Once a YOLO11 model is exported to TF GraphDef format, you can deploy it across various specialized hardware platforms. Typical deployment scenarios include:
- **TensorFlow Serving**: Use TensorFlow Serving for scalable model deployment in production environments. It supports model management and efficient serving.
- **Mobile Devices**: Convert TF GraphDef models to TensorFlow Lite, optimized for mobile and embedded devices, enabling on-device inference.
@ -194,11 +194,11 @@ Once a YOLOv8 model is exported to TF GraphDef format, you can deploy it across
Check the [deployment options](#deployment-options-with-tf-graphdef) section for detailed information.
### Where can I find solutions for common issues while exporting YOLOv8 models?
### Where can I find solutions for common issues while exporting YOLO11 models?
For troubleshooting common issues with exporting YOLOv8 models, Ultralytics provides comprehensive guides and resources. If you encounter problems during installation or model export, refer to:
For troubleshooting common issues with exporting YOLO11 models, Ultralytics provides comprehensive guides and resources. If you encounter problems during installation or model export, refer to:
- **[Common Issues Guide](../guides/yolo-common-issues.md)**: Offers solutions to frequently faced problems.
- **[Installation Guide](../quickstart.md)**: Step-by-step instructions for setting up the required packages.
These resources should help you resolve most issues related to YOLOv8 model export and deployment.
These resources should help you resolve most issues related to YOLO11 model export and deployment.

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@ -1,14 +1,14 @@
---
comments: true
description: Learn how to export Ultralytics YOLOv8 models to TensorFlow SavedModel format for easy deployment across various platforms and environments.
keywords: YOLOv8, TF SavedModel, Ultralytics, TensorFlow, model export, model deployment, machine learning, AI
description: Learn how to export Ultralytics YOLO11 models to TensorFlow SavedModel format for easy deployment across various platforms and environments.
keywords: YOLO11, TF SavedModel, Ultralytics, TensorFlow, model export, model deployment, machine learning, AI
---
# Understand How to Export to TF SavedModel Format From YOLOv8
# Understand How to Export to TF SavedModel Format From YOLO11
Deploying [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) models can be challenging. However, using an efficient and flexible model format can make your job easier. TF SavedModel is an open-source machine-learning framework used by TensorFlow to load machine-learning models in a consistent way. It is like a suitcase for TensorFlow models, making them easy to carry and use on different devices and systems.
Learning how to export to TF SavedModel from [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models can help you deploy models easily across different platforms and environments. In this guide, we'll walk through how to convert your models to the TF SavedModel format, simplifying the process of running inferences with your models on different devices.
Learning how to export to TF SavedModel from [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) models can help you deploy models easily across different platforms and environments. In this guide, we'll walk through how to convert your models to the TF SavedModel format, simplifying the process of running inferences with your models on different devices.
## Why Should You Export to TF SavedModel?
@ -32,7 +32,7 @@ Here are the key features that make TF SavedModel a great option for AI develope
## Deployment Options with TF SavedModel
Before we dive into the process of exporting YOLOv8 models to the TF SavedModel format, let's explore some typical deployment scenarios where this format is used.
Before we dive into the process of exporting YOLO11 models to the TF SavedModel format, let's explore some typical deployment scenarios where this format is used.
TF SavedModel provides a range of options to deploy your machine learning models:
@ -44,9 +44,9 @@ TF SavedModel provides a range of options to deploy your machine learning models
- **TensorFlow Runtime:** TensorFlow Runtime (`tfrt`) is a high-performance runtime for executing [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) graphs. It provides lower-level APIs for loading and running TF SavedModels in C++ environments. TensorFlow Runtime offers better performance compared to the standard TensorFlow runtime. It is suitable for deployment scenarios that require low-latency inference and tight integration with existing C++ codebases.
## Exporting YOLOv8 Models to TF SavedModel
## Exporting YOLO11 Models to TF SavedModel
By exporting YOLOv8 models to the TF SavedModel format, you enhance their adaptability and ease of deployment across various platforms.
By exporting YOLO11 models to the TF SavedModel format, you enhance their adaptability and ease of deployment across various platforms.
### Installation
@ -57,15 +57,15 @@ To install the required package, run:
=== "CLI"
```bash
# Install the required package for YOLOv8
# Install the required package for YOLO11
pip install ultralytics
```
For detailed instructions and best practices related to the installation process, check our [Ultralytics Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
For detailed instructions and best practices related to the installation process, check our [Ultralytics Installation guide](../quickstart.md). While installing the required packages for YOLO11, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
### Usage
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).
Before diving into the usage instructions, it's important to note that while all [Ultralytics YOLO11 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"
@ -74,14 +74,14 @@ Before diving into the usage instructions, it's important to note that while all
```python
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO("yolov8n.pt")
# Load the YOLO11 model
model = YOLO("yolo11n.pt")
# Export the model to TF SavedModel format
model.export(format="saved_model") # creates '/yolov8n_saved_model'
model.export(format="saved_model") # creates '/yolo11n_saved_model'
# Load the exported TF SavedModel model
tf_savedmodel_model = YOLO("./yolov8n_saved_model")
tf_savedmodel_model = YOLO("./yolo11n_saved_model")
# Run inference
results = tf_savedmodel_model("https://ultralytics.com/images/bus.jpg")
@ -90,18 +90,18 @@ Before diving into the usage instructions, it's important to note that while all
=== "CLI"
```bash
# Export a YOLOv8n PyTorch model to TF SavedModel format
yolo export model=yolov8n.pt format=saved_model # creates '/yolov8n_saved_model'
# Export a YOLO11n PyTorch model to TF SavedModel format
yolo export model=yolo11n.pt format=saved_model # creates '/yolo11n_saved_model'
# Run inference with the exported model
yolo predict model='./yolov8n_saved_model' source='https://ultralytics.com/images/bus.jpg'
yolo predict model='./yolo11n_saved_model' source='https://ultralytics.com/images/bus.jpg'
```
For more details about supported export options, visit the [Ultralytics documentation page on deployment options](../guides/model-deployment-options.md).
## Deploying Exported YOLOv8 TF SavedModel Models
## Deploying Exported YOLO11 TF SavedModel Models
Now that you have exported your YOLOv8 model to the TF SavedModel format, the next step is to deploy it. The primary and recommended first step for running a TF GraphDef model is to use the YOLO("./yolov8n_saved_model") method, as previously shown in the usage code snippet.
Now that you have exported your YOLO11 model to the TF SavedModel format, the next step is to deploy it. The primary and recommended first step for running a TF GraphDef model is to use the YOLO("./yolo11n_saved_model") method, as previously shown in the usage code snippet.
However, for in-depth instructions on deploying your TF SavedModel models, take a look at the following resources:
@ -113,11 +113,11 @@ However, for in-depth instructions on deploying your TF SavedModel models, take
## Summary
In this guide, we explored how to export Ultralytics YOLOv8 models to the TF SavedModel format. By exporting to TF SavedModel, you gain the flexibility to optimize, deploy, and scale your YOLOv8 models on a wide range of platforms.
In this guide, we explored how to export Ultralytics YOLO11 models to the TF SavedModel format. By exporting to TF SavedModel, you gain the flexibility to optimize, deploy, and scale your YOLO11 models on a wide range of platforms.
For further details on usage, visit the [TF SavedModel official documentation](https://www.tensorflow.org/guide/saved_model).
For more information on integrating Ultralytics YOLOv8 with other platforms and frameworks, don't forget to check out our [integration guide page](index.md). It's packed with great resources to help you make the most of YOLOv8 in your projects.
For more information on integrating Ultralytics YOLO11 with other platforms and frameworks, don't forget to check out our [integration guide page](index.md). It's packed with great resources to help you make the most of YOLO11 in your projects.
## FAQ
@ -125,32 +125,32 @@ 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 YOLO11 to TF SavedModel"
=== "Python"
```python
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO("yolov8n.pt")
# Load the YOLO11 model
model = YOLO("yolo11n.pt")
# Export the model to TF SavedModel format
model.export(format="saved_model") # creates '/yolov8n_saved_model'
model.export(format="saved_model") # creates '/yolo11n_saved_model'
# Load the exported TF SavedModel for inference
tf_savedmodel_model = YOLO("./yolov8n_saved_model")
tf_savedmodel_model = YOLO("./yolo11n_saved_model")
results = tf_savedmodel_model("https://ultralytics.com/images/bus.jpg")
```
=== "CLI"
```bash
# Export the YOLOv8 model to TF SavedModel format
yolo export model=yolov8n.pt format=saved_model # creates '/yolov8n_saved_model'
# Export the YOLO11 model to TF SavedModel format
yolo export model=yolo11n.pt format=saved_model # creates '/yolo11n_saved_model'
# Run inference with the exported model
yolo predict model='./yolov8n_saved_model' source='https://ultralytics.com/images/bus.jpg'
yolo predict model='./yolo11n_saved_model' source='https://ultralytics.com/images/bus.jpg'
```
Refer to the [Ultralytics Export documentation](../modes/export.md) for more details.
@ -176,9 +176,9 @@ TF SavedModel can be deployed in various environments, including:
For detailed deployment options, visit the official guides on [deploying TensorFlow models](https://www.tensorflow.org/tfx/guide/serving).
### How can I install the necessary packages to export YOLOv8 models?
### How can I install the necessary packages to export YOLO11 models?
To export YOLOv8 models, you need to install the `ultralytics` package. Run the following command in your terminal:
To export YOLO11 models, you need to install the `ultralytics` package. Run the following command in your terminal:
```bash
pip install ultralytics

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@ -1,14 +1,14 @@
---
comments: true
description: Convert your Ultralytics YOLOv8 models to TensorFlow.js for high-speed, local object detection. Learn how to optimize ML models for browser and Node.js apps.
keywords: YOLOv8, TensorFlow.js, TF.js, model export, machine learning, object detection, browser ML, Node.js, Ultralytics, YOLO, export models
description: Convert your Ultralytics YOLO11 models to TensorFlow.js for high-speed, local object detection. Learn how to optimize ML models for browser and Node.js apps.
keywords: YOLO11, TensorFlow.js, TF.js, model export, machine learning, object detection, browser ML, Node.js, Ultralytics, YOLO, export models
---
# Export to TF.js Model Format From a YOLOv8 Model Format
# Export to TF.js Model Format From a YOLO11 Model Format
Deploying [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) models directly in the browser or on Node.js can be tricky. You'll need to make sure your model format is optimized for faster performance so that the model can be used to run interactive applications locally on the user's device. The TensorFlow.js, or TF.js, model format is designed to use minimal power while delivering fast performance.
The 'export to TF.js model format' feature allows you to optimize your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models for high-speed and locally-run [object detection](https://www.ultralytics.com/glossary/object-detection) inference. In this guide, we'll walk you through converting your models to the TF.js format, making it easier for your models to perform well on various local browsers and Node.js applications.
The 'export to TF.js model format' feature allows you to optimize your [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) models for high-speed and locally-run [object detection](https://www.ultralytics.com/glossary/object-detection) inference. In this guide, we'll walk you through converting your models to the TF.js format, making it easier for your models to perform well on various local browsers and Node.js applications.
## Why Should You Export to TF.js?
@ -32,7 +32,7 @@ Here are the key features that make TF.js a powerful tool for developers:
## Deployment Options with TensorFlow.js
Before we dive into the process of exporting YOLOv8 models to the TF.js format, let's explore some typical deployment scenarios where this format is used.
Before we dive into the process of exporting YOLO11 models to the TF.js format, let's explore some typical deployment scenarios where this format is used.
TF.js provides a range of options to deploy your machine learning models:
@ -42,9 +42,9 @@ TF.js provides a range of options to deploy your machine learning models:
- **Chrome Extensions:** An interesting deployment scenario is the creation of Chrome extensions with TensorFlow.js. For instance, you can develop an extension that allows users to right-click on an image within any webpage to classify it using a pre-trained ML model. TensorFlow.js can be integrated into everyday web browsing experiences to provide immediate insights or augmentations based on machine learning.
## Exporting YOLOv8 Models to TensorFlow.js
## Exporting YOLO11 Models to TensorFlow.js
You can expand model compatibility and deployment flexibility by converting YOLOv8 models to TF.js.
You can expand model compatibility and deployment flexibility by converting YOLO11 models to TF.js.
### Installation
@ -55,15 +55,15 @@ To install the required package, run:
=== "CLI"
```bash
# Install the required package for YOLOv8
# Install the required package for YOLO11
pip install ultralytics
```
For detailed instructions and best practices related to the installation process, check our [Ultralytics Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
For detailed instructions and best practices related to the installation process, check our [Ultralytics Installation guide](../quickstart.md). While installing the required packages for YOLO11, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
### Usage
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).
Before diving into the usage instructions, it's important to note that while all [Ultralytics YOLO11 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"
@ -72,14 +72,14 @@ Before diving into the usage instructions, it's important to note that while all
```python
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO("yolov8n.pt")
# Load the YOLO11 model
model = YOLO("yolo11n.pt")
# Export the model to TF.js format
model.export(format="tfjs") # creates '/yolov8n_web_model'
model.export(format="tfjs") # creates '/yolo11n_web_model'
# Load the exported TF.js model
tfjs_model = YOLO("./yolov8n_web_model")
tfjs_model = YOLO("./yolo11n_web_model")
# Run inference
results = tfjs_model("https://ultralytics.com/images/bus.jpg")
@ -88,18 +88,18 @@ Before diving into the usage instructions, it's important to note that while all
=== "CLI"
```bash
# Export a YOLOv8n PyTorch model to TF.js format
yolo export model=yolov8n.pt format=tfjs # creates '/yolov8n_web_model'
# Export a YOLO11n PyTorch model to TF.js format
yolo export model=yolo11n.pt format=tfjs # creates '/yolo11n_web_model'
# Run inference with the exported model
yolo predict model='./yolov8n_web_model' source='https://ultralytics.com/images/bus.jpg'
yolo predict model='./yolo11n_web_model' source='https://ultralytics.com/images/bus.jpg'
```
For more details about supported export options, visit the [Ultralytics documentation page on deployment options](../guides/model-deployment-options.md).
## Deploying Exported YOLOv8 TensorFlow.js Models
## Deploying Exported YOLO11 TensorFlow.js Models
Now that you have exported your YOLOv8 model to the TF.js format, the next step is to deploy it. The primary and recommended first step for running a TF.js is to use the YOLO("./yolov8n_web_model") method, as previously shown in the usage code snippet.
Now that you have exported your YOLO11 model to the TF.js format, the next step is to deploy it. The primary and recommended first step for running a TF.js is to use the `YOLO("./yolo11n_web_model")` method, as previously shown in the usage code snippet.
However, for in-depth instructions on deploying your TF.js models, take a look at the following resources:
@ -111,17 +111,17 @@ However, for in-depth instructions on deploying your TF.js models, take a look a
## Summary
In this guide, we learned how to export Ultralytics YOLOv8 models to the TensorFlow.js format. By exporting to TF.js, you gain the flexibility to optimize, deploy, and scale your YOLOv8 models on a wide range of platforms.
In this guide, we learned how to export Ultralytics YOLO11 models to the TensorFlow.js format. By exporting to TF.js, you gain the flexibility to optimize, deploy, and scale your YOLO11 models on a wide range of platforms.
For further details on usage, visit the [TensorFlow.js official documentation](https://www.tensorflow.org/js/guide).
For more information on integrating Ultralytics YOLOv8 with other platforms and frameworks, don't forget to check out our [integration guide page](index.md). It's packed with great resources to help you make the most of YOLOv8 in your projects.
For more information on integrating Ultralytics YOLO11 with other platforms and frameworks, don't forget to check out our [integration guide page](index.md). It's packed with great resources to help you make the most of YOLO11 in your projects.
## FAQ
### How do I export Ultralytics YOLOv8 models to TensorFlow.js format?
### How do I export Ultralytics YOLO11 models to TensorFlow.js format?
Exporting Ultralytics YOLOv8 models to TensorFlow.js (TF.js) format is straightforward. You can follow these steps:
Exporting Ultralytics YOLO11 models to TensorFlow.js (TF.js) format is straightforward. You can follow these steps:
!!! example "Usage"
@ -130,14 +130,14 @@ Exporting Ultralytics YOLOv8 models to TensorFlow.js (TF.js) format is straightf
```python
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO("yolov8n.pt")
# Load the YOLO11 model
model = YOLO("yolo11n.pt")
# Export the model to TF.js format
model.export(format="tfjs") # creates '/yolov8n_web_model'
model.export(format="tfjs") # creates '/yolo11n_web_model'
# Load the exported TF.js model
tfjs_model = YOLO("./yolov8n_web_model")
tfjs_model = YOLO("./yolo11n_web_model")
# Run inference
results = tfjs_model("https://ultralytics.com/images/bus.jpg")
@ -146,18 +146,18 @@ Exporting Ultralytics YOLOv8 models to TensorFlow.js (TF.js) format is straightf
=== "CLI"
```bash
# Export a YOLOv8n PyTorch model to TF.js format
yolo export model=yolov8n.pt format=tfjs # creates '/yolov8n_web_model'
# Export a YOLO11n PyTorch model to TF.js format
yolo export model=yolo11n.pt format=tfjs # creates '/yolo11n_web_model'
# Run inference with the exported model
yolo predict model='./yolov8n_web_model' source='https://ultralytics.com/images/bus.jpg'
yolo predict model='./yolo11n_web_model' source='https://ultralytics.com/images/bus.jpg'
```
For more details about supported export options, visit the [Ultralytics documentation page on deployment options](../guides/model-deployment-options.md).
### Why should I export my YOLOv8 models to TensorFlow.js?
### Why should I export my YOLO11 models to TensorFlow.js?
Exporting YOLOv8 models to TensorFlow.js offers several advantages, including:
Exporting YOLO11 models to TensorFlow.js offers several advantages, including:
1. **Local Execution:** Models can run directly in the browser or Node.js, reducing latency and enhancing user experience.
2. **Cross-Platform Support:** TF.js supports multiple environments, allowing flexibility in deployment.
@ -177,7 +177,7 @@ TensorFlow.js is specifically designed for efficient execution of ML models in b
Interested in learning more about TF.js? Check out the [official TensorFlow.js guide](https://www.tensorflow.org/js/guide).
### What are the key features of TensorFlow.js for deploying YOLOv8 models?
### What are the key features of TensorFlow.js for deploying YOLO11 models?
Key features of TensorFlow.js include:
@ -185,10 +185,10 @@ Key features of TensorFlow.js include:
- **Multiple Backends:** Supports CPU, WebGL for GPU acceleration, WebAssembly (WASM), and WebGPU for advanced operations.
- **Offline Capabilities:** Models can run directly in the browser without internet connectivity, making it ideal for developing responsive web applications.
For deployment scenarios and more in-depth information, see our section on [Deployment Options with TensorFlow.js](#deploying-exported-yolov8-tensorflowjs-models).
For deployment scenarios and more in-depth information, see our section on [Deployment Options with TensorFlow.js](#deploying-exported-yolo11-tensorflowjs-models).
### Can I deploy a YOLOv8 model on server-side Node.js applications using TensorFlow.js?
### Can I deploy a YOLO11 model on server-side Node.js applications using TensorFlow.js?
Yes, TensorFlow.js allows the deployment of YOLOv8 models on Node.js environments. This enables server-side machine learning applications that benefit from the processing power of a server and access to server-side data. Typical use cases include real-time data processing and machine learning pipelines on backend servers.
Yes, TensorFlow.js allows the deployment of YOLO11 models on Node.js environments. This enables server-side machine learning applications that benefit from the processing power of a server and access to server-side data. Typical use cases include real-time data processing and machine learning pipelines on backend servers.
To get started with Node.js deployment, refer to the [Run TensorFlow.js in Node.js](https://www.tensorflow.org/js/guide/nodejs) guide from TensorFlow.

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@ -1,10 +1,10 @@
---
comments: true
description: Learn how to convert YOLOv8 models to TFLite for edge device deployment. Optimize performance and ensure seamless execution on various platforms.
keywords: YOLOv8, TFLite, model export, TensorFlow Lite, edge devices, deployment, Ultralytics, machine learning, on-device inference, model optimization
description: Learn how to convert YOLO11 models to TFLite for edge device deployment. Optimize performance and ensure seamless execution on various platforms.
keywords: YOLO11, TFLite, model export, TensorFlow Lite, edge devices, deployment, Ultralytics, machine learning, on-device inference, model optimization
---
# A Guide on YOLOv8 Model Export to TFLite for Deployment
# A Guide on YOLO11 Model Export to TFLite for Deployment
<p align="center">
<img width="75%" src="https://github.com/ultralytics/docs/releases/download/0/tflite-logo.avif" alt="TFLite Logo">
@ -12,7 +12,7 @@ keywords: YOLOv8, TFLite, model export, TensorFlow Lite, edge devices, deploymen
Deploying [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models on edge devices or embedded devices requires a format that can ensure seamless performance.
The TensorFlow Lite or TFLite export format allows you to optimize your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models for tasks like [object detection](https://www.ultralytics.com/glossary/object-detection) and [image classification](https://www.ultralytics.com/glossary/image-classification) in edge device-based applications. In this guide, we'll walk through the steps for converting your models to the TFLite format, making it easier for your models to perform well on various edge devices.
The TensorFlow Lite or TFLite export format allows you to optimize your [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) models for tasks like [object detection](https://www.ultralytics.com/glossary/object-detection) and [image classification](https://www.ultralytics.com/glossary/image-classification) in edge device-based applications. In this guide, we'll walk through the steps for converting your models to the TFLite format, making it easier for your models to perform well on various edge devices.
## Why should you export to TFLite?
@ -34,7 +34,7 @@ TFLite models offer a wide range of key features that enable on-device machine l
## Deployment Options in TFLite
Before we look at the code for exporting YOLOv8 models to the TFLite format, let's understand how TFLite models are normally used.
Before we look at the code for exporting YOLO11 models to the TFLite format, let's understand how TFLite models are normally used.
TFLite offers various on-device deployment options for machine learning models, including:
@ -48,7 +48,7 @@ TFLite offers various on-device deployment options for machine learning models,
- **Deploying with Microcontrollers**: TFLite models can also be deployed on microcontrollers and other devices with only a few kilobytes of memory. The core runtime just fits in 16 KB on an Arm Cortex M3 and can run many basic models. It doesn't require operating system support, any standard C or C++ libraries, or dynamic memory allocation.
## Export to TFLite: Converting Your YOLOv8 Model
## Export to TFLite: Converting Your YOLO11 Model
You can improve on-device model execution efficiency and optimize performance by converting them to TFLite format.
@ -61,15 +61,15 @@ To install the required packages, run:
=== "CLI"
```bash
# Install the required package for YOLOv8
# Install the required package for YOLO11
pip install ultralytics
```
For detailed instructions and best practices related to the installation process, check our [Ultralytics Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
For detailed instructions and best practices related to the installation process, check our [Ultralytics Installation guide](../quickstart.md). While installing the required packages for YOLO11, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
### Usage
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).
Before diving into the usage instructions, it's important to note that while all [Ultralytics YOLO11 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"
@ -78,14 +78,14 @@ Before diving into the usage instructions, it's important to note that while all
```python
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO("yolov8n.pt")
# Load the YOLO11 model
model = YOLO("yolo11n.pt")
# Export the model to TFLite format
model.export(format="tflite") # creates 'yolov8n_float32.tflite'
model.export(format="tflite") # creates 'yolo11n_float32.tflite'
# Load the exported TFLite model
tflite_model = YOLO("yolov8n_float32.tflite")
tflite_model = YOLO("yolo11n_float32.tflite")
# Run inference
results = tflite_model("https://ultralytics.com/images/bus.jpg")
@ -94,18 +94,18 @@ Before diving into the usage instructions, it's important to note that while all
=== "CLI"
```bash
# Export a YOLOv8n PyTorch model to TFLite format
yolo export model=yolov8n.pt format=tflite # creates 'yolov8n_float32.tflite'
# Export a YOLO11n PyTorch model to TFLite format
yolo export model=yolo11n.pt format=tflite # creates 'yolo11n_float32.tflite'
# Run inference with the exported model
yolo predict model='yolov8n_float32.tflite' source='https://ultralytics.com/images/bus.jpg'
yolo predict model='yolo11n_float32.tflite' source='https://ultralytics.com/images/bus.jpg'
```
For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
## Deploying Exported YOLOv8 TFLite Models
## Deploying Exported YOLO11 TFLite Models
After successfully exporting your Ultralytics YOLOv8 models to TFLite format, you can now deploy them. The primary and recommended first step for running a TFLite model is to utilize the YOLO("model.tflite") method, as outlined in the previous usage code snippet. However, for in-depth instructions on deploying your TFLite models in various other settings, take a look at the following resources:
After successfully exporting your Ultralytics YOLO11 models to TFLite format, you can now deploy them. The primary and recommended first step for running a TFLite model is to utilize the YOLO("model.tflite") method, as outlined in the previous usage code snippet. However, for in-depth instructions on deploying your TFLite models in various other settings, take a look at the following resources:
- **[Android](https://ai.google.dev/edge/litert/android)**: A quick start guide for integrating [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) Lite into Android applications, providing easy-to-follow steps for setting up and running [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) models.
@ -115,17 +115,17 @@ After successfully exporting your Ultralytics YOLOv8 models to TFLite format, yo
## Summary
In this guide, we focused on how to export to TFLite format. By converting your Ultralytics YOLOv8 models to TFLite model format, you can improve the efficiency and speed of YOLOv8 models, making them more effective and suitable for [edge computing](https://www.ultralytics.com/glossary/edge-computing) environments.
In this guide, we focused on how to export to TFLite format. By converting your Ultralytics YOLO11 models to TFLite model format, you can improve the efficiency and speed of YOLO11 models, making them more effective and suitable for [edge computing](https://www.ultralytics.com/glossary/edge-computing) environments.
For further details on usage, visit the [TFLite official documentation](https://ai.google.dev/edge/litert).
Also, if you're curious about other Ultralytics YOLOv8 integrations, make sure to check out our [integration guide page](../integrations/index.md). You'll find tons of helpful info and insights waiting for you there.
Also, if you're curious about other Ultralytics YOLO11 integrations, make sure to check out our [integration guide page](../integrations/index.md). You'll find tons of helpful info and insights waiting for you there.
## FAQ
### How do I export a YOLOv8 model to TFLite format?
### How do I export a YOLO11 model to TFLite format?
To export a YOLOv8 model to TFLite format, you can use the Ultralytics library. First, install the required package using:
To export a YOLO11 model to TFLite format, you can use the Ultralytics library. First, install the required package using:
```bash
pip install ultralytics
@ -136,24 +136,24 @@ Then, use the following code snippet to export your model:
```python
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO("yolov8n.pt")
# Load the YOLO11 model
model = YOLO("yolo11n.pt")
# Export the model to TFLite format
model.export(format="tflite") # creates 'yolov8n_float32.tflite'
model.export(format="tflite") # creates 'yolo11n_float32.tflite'
```
For CLI users, you can achieve this with:
```bash
yolo export model=yolov8n.pt format=tflite # creates 'yolov8n_float32.tflite'
yolo export model=yolo11n.pt format=tflite # creates 'yolo11n_float32.tflite'
```
For more details, visit the [Ultralytics export guide](../modes/export.md).
### What are the benefits of using TensorFlow Lite for YOLOv8 [model deployment](https://www.ultralytics.com/glossary/model-deployment)?
### What are the benefits of using TensorFlow Lite for YOLO11 [model deployment](https://www.ultralytics.com/glossary/model-deployment)?
TensorFlow Lite (TFLite) is an open-source [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) framework designed for on-device inference, making it ideal for deploying YOLOv8 models on mobile, embedded, and IoT devices. Key benefits include:
TensorFlow Lite (TFLite) is an open-source [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) framework designed for on-device inference, making it ideal for deploying YOLO11 models on mobile, embedded, and IoT devices. Key benefits include:
- **On-device optimization**: Minimize latency and enhance privacy by processing data locally.
- **Platform compatibility**: Supports Android, iOS, embedded Linux, and MCU.
@ -161,33 +161,33 @@ TensorFlow Lite (TFLite) is an open-source [deep learning](https://www.ultralyti
To learn more, check out the [TFLite guide](https://ai.google.dev/edge/litert).
### Is it possible to run YOLOv8 TFLite models on Raspberry Pi?
### Is it possible to run YOLO11 TFLite models on Raspberry Pi?
Yes, you can run YOLOv8 TFLite models on Raspberry Pi to improve inference speeds. First, export your model to TFLite format as explained [here](#how-do-i-export-a-yolov8-model-to-tflite-format). Then, use a tool like TensorFlow Lite Interpreter to execute the model on your Raspberry Pi.
Yes, you can run YOLO11 TFLite models on Raspberry Pi to improve inference speeds. First, export your model to TFLite format as explained [here](#how-do-i-export-a-yolo11-model-to-tflite-format). Then, use a tool like TensorFlow Lite Interpreter to execute the model on your Raspberry Pi.
For further optimizations, you might consider using [Coral Edge TPU](https://coral.withgoogle.com/). For detailed steps, refer to our [Raspberry Pi deployment guide](../guides/raspberry-pi.md).
### Can I use TFLite models on microcontrollers for YOLOv8 predictions?
### Can I use TFLite models on microcontrollers for YOLO11 predictions?
Yes, TFLite supports deployment on microcontrollers with limited resources. TFLite's core runtime requires only 16 KB of memory on an Arm Cortex M3 and can run basic YOLOv8 models. This makes it suitable for deployment on devices with minimal computational power and memory.
Yes, TFLite supports deployment on microcontrollers with limited resources. TFLite's core runtime requires only 16 KB of memory on an Arm Cortex M3 and can run basic YOLO11 models. This makes it suitable for deployment on devices with minimal computational power and memory.
To get started, visit the [TFLite Micro for Microcontrollers guide](https://ai.google.dev/edge/litert/microcontrollers/overview).
### What platforms are compatible with TFLite exported YOLOv8 models?
### What platforms are compatible with TFLite exported YOLO11 models?
TensorFlow Lite provides extensive platform compatibility, allowing you to deploy YOLOv8 models on a wide range of devices, including:
TensorFlow Lite provides extensive platform compatibility, allowing you to deploy YOLO11 models on a wide range of devices, including:
- **Android and iOS**: Native support through TFLite Android and iOS libraries.
- **Embedded Linux**: Ideal for single-board computers such as Raspberry Pi.
- **Microcontrollers**: Suitable for MCUs with constrained resources.
For more information on deployment options, see our detailed [deployment guide](#deploying-exported-yolov8-tflite-models).
For more information on deployment options, see our detailed [deployment guide](#deploying-exported-yolo11-tflite-models).
### How do I troubleshoot common issues during YOLOv8 model export to TFLite?
### How do I troubleshoot common issues during YOLO11 model export to TFLite?
If you encounter errors while exporting YOLOv8 models to TFLite, common solutions include:
If you encounter errors while exporting YOLO11 models to TFLite, common solutions include:
- **Check package compatibility**: Ensure you're using compatible versions of Ultralytics and TensorFlow. Refer to our [installation guide](../quickstart.md).
- **Model support**: Verify that the specific YOLOv8 model supports TFLite export by checking [here](../modes/export.md).
- **Model support**: Verify that the specific YOLO11 model supports TFLite export by checking [here](../modes/export.md).
For additional troubleshooting tips, visit our [Common Issues guide](../guides/yolo-common-issues.md).

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@ -1,22 +1,22 @@
---
comments: true
description: Learn how to export Ultralytics YOLOv8 models to TorchScript for flexible, cross-platform deployment. Boost performance and utilize in various environments.
keywords: YOLOv8, TorchScript, model export, Ultralytics, PyTorch, deep learning, AI deployment, cross-platform, performance optimization
description: Learn how to export Ultralytics YOLO11 models to TorchScript for flexible, cross-platform deployment. Boost performance and utilize in various environments.
keywords: YOLO11, TorchScript, model export, Ultralytics, PyTorch, deep learning, AI deployment, cross-platform, performance optimization
---
# YOLOv8 Model Export to TorchScript for Quick Deployment
# YOLO11 Model Export to TorchScript for Quick Deployment
Deploying [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models across different environments, including embedded systems, web browsers, or platforms with limited Python support, requires a flexible and portable solution. TorchScript focuses on portability and the ability to run models in environments where the entire Python framework is unavailable. This makes it ideal for scenarios where you need to deploy your computer vision capabilities across various devices or platforms.
Export to Torchscript to serialize your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models for cross-platform compatibility and streamlined deployment. In this guide, we'll show you how to export your YOLOv8 models to the TorchScript format, making it easier for you to use them across a wider range of applications.
Export to Torchscript to serialize your [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) models for cross-platform compatibility and streamlined deployment. In this guide, we'll show you how to export your YOLO11 models to the TorchScript format, making it easier for you to use them across a wider range of applications.
## Why should you export to TorchScript?
![Torchscript Overview](https://github.com/ultralytics/docs/releases/download/0/torchscript-overview.avif)
Developed by the creators of PyTorch, TorchScript is a powerful tool for optimizing and deploying PyTorch models across a variety of platforms. Exporting YOLOv8 models to [TorchScript](https://pytorch.org/docs/stable/jit.html) is crucial for moving from research to real-world applications. TorchScript, part of the PyTorch framework, helps make this transition smoother by allowing PyTorch models to be used in environments that don't support Python.
Developed by the creators of PyTorch, TorchScript is a powerful tool for optimizing and deploying PyTorch models across a variety of platforms. Exporting YOLO11 models to [TorchScript](https://pytorch.org/docs/stable/jit.html) is crucial for moving from research to real-world applications. TorchScript, part of the PyTorch framework, helps make this transition smoother by allowing PyTorch models to be used in environments that don't support Python.
The process involves two techniques: tracing and scripting. Tracing records operations during model execution, while scripting allows for the definition of models using a subset of Python. These techniques ensure that models like YOLOv8 can still work their magic even outside their usual Python environment.
The process involves two techniques: tracing and scripting. Tracing records operations during model execution, while scripting allows for the definition of models using a subset of Python. These techniques ensure that models like YOLO11 can still work their magic even outside their usual Python environment.
![TorchScript Script and Trace](https://github.com/ultralytics/docs/releases/download/0/torchscript-script-and-trace.avif)
@ -42,7 +42,7 @@ Here are the key features that make TorchScript a valuable tool for developers:
## Deployment Options in TorchScript
Before we look at the code for exporting YOLOv8 models to the TorchScript format, let's understand where TorchScript models are normally used.
Before we look at the code for exporting YOLO11 models to the TorchScript format, let's understand where TorchScript models are normally used.
TorchScript offers various deployment options for [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) models, such as:
@ -52,9 +52,9 @@ TorchScript offers various deployment options for [machine learning](https://www
- **Cloud Deployment**: TorchScript models can be deployed to cloud-based servers using solutions like TorchServe. It provides features like model versioning, batching, and metrics monitoring for scalable deployment in production environments. Cloud deployment with TorchScript can make your models accessible via APIs or other web services.
## Export to TorchScript: Converting Your YOLOv8 Model
## Export to TorchScript: Converting Your YOLO11 Model
Exporting YOLOv8 models to TorchScript makes it easier to use them in different places and helps them run faster and more efficiently. This is great for anyone looking to use deep learning models more effectively in real-world applications.
Exporting YOLO11 models to TorchScript makes it easier to use them in different places and helps them run faster and more efficiently. This is great for anyone looking to use deep learning models more effectively in real-world applications.
### Installation
@ -65,15 +65,15 @@ To install the required package, run:
=== "CLI"
```bash
# Install the required package for YOLOv8
# Install the required package for YOLO11
pip install ultralytics
```
For detailed instructions and best practices related to the installation process, check our [Ultralytics Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
For detailed instructions and best practices related to the installation process, check our [Ultralytics Installation guide](../quickstart.md). While installing the required packages for YOLO11, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
### Usage
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).
Before diving into the usage instructions, it's important to note that while all [Ultralytics YOLO11 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"
@ -82,14 +82,14 @@ Before diving into the usage instructions, it's important to note that while all
```python
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO("yolov8n.pt")
# Load the YOLO11 model
model = YOLO("yolo11n.pt")
# Export the model to TorchScript format
model.export(format="torchscript") # creates 'yolov8n.torchscript'
model.export(format="torchscript") # creates 'yolo11n.torchscript'
# Load the exported TorchScript model
torchscript_model = YOLO("yolov8n.torchscript")
torchscript_model = YOLO("yolo11n.torchscript")
# Run inference
results = torchscript_model("https://ultralytics.com/images/bus.jpg")
@ -98,18 +98,18 @@ Before diving into the usage instructions, it's important to note that while all
=== "CLI"
```bash
# Export a YOLOv8n PyTorch model to TorchScript format
yolo export model=yolov8n.pt format=torchscript # creates 'yolov8n.torchscript'
# Export a YOLO11n PyTorch model to TorchScript format
yolo export model=yolo11n.pt format=torchscript # creates 'yolo11n.torchscript'
# Run inference with the exported model
yolo predict model=yolov8n.torchscript source='https://ultralytics.com/images/bus.jpg'
yolo predict model=yolo11n.torchscript source='https://ultralytics.com/images/bus.jpg'
```
For more details about the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
## Deploying Exported YOLOv8 TorchScript Models
## Deploying Exported YOLO11 TorchScript Models
After successfully exporting your Ultralytics YOLOv8 models to TorchScript format, you can now deploy them. The primary and recommended first step for running a TorchScript model is to utilize the YOLO("model.torchscript") method, as outlined in the previous usage code snippet. However, for in-depth instructions on deploying your TorchScript models in various other settings, take a look at the following resources:
After successfully exporting your Ultralytics YOLO11 models to TorchScript format, you can now deploy them. The primary and recommended first step for running a TorchScript model is to utilize the YOLO("model.torchscript") method, as outlined in the previous usage code snippet. However, for in-depth instructions on deploying your TorchScript models in various other settings, take a look at the following resources:
- **[Explore Mobile Deployment](https://pytorch.org/mobile/home/)**: The [PyTorch](https://www.ultralytics.com/glossary/pytorch) Mobile Documentation provides comprehensive guidelines for deploying models on mobile devices, ensuring your applications are efficient and responsive.
@ -119,21 +119,21 @@ After successfully exporting your Ultralytics YOLOv8 models to TorchScript forma
## Summary
In this guide, we explored the process of exporting Ultralytics YOLOv8 models to the TorchScript format. By following the provided instructions, you can optimize YOLOv8 models for performance and gain the flexibility to deploy them across various platforms and environments.
In this guide, we explored the process of exporting Ultralytics YOLO11 models to the TorchScript format. By following the provided instructions, you can optimize YOLO11 models for performance and gain the flexibility to deploy them across various platforms and environments.
For further details on usage, visit [TorchScript's official documentation](https://pytorch.org/docs/stable/jit.html).
Also, if you'd like to know more about other Ultralytics YOLOv8 integrations, visit our [integration guide page](../integrations/index.md). You'll find plenty of useful resources and insights there.
Also, if you'd like to know more about other Ultralytics YOLO11 integrations, visit our [integration guide page](../integrations/index.md). You'll find plenty of useful resources and insights there.
## FAQ
### What is Ultralytics YOLOv8 model export to TorchScript?
### What is Ultralytics YOLO11 model export to TorchScript?
Exporting an Ultralytics YOLOv8 model to TorchScript allows for flexible, cross-platform deployment. TorchScript, a part of the PyTorch ecosystem, facilitates the serialization of models, which can then be executed in environments that lack Python support. This makes it ideal for deploying models on embedded systems, C++ environments, mobile applications, and even web browsers. Exporting to TorchScript enables efficient performance and wider applicability of your YOLOv8 models across diverse platforms.
Exporting an Ultralytics YOLO11 model to TorchScript allows for flexible, cross-platform deployment. TorchScript, a part of the PyTorch ecosystem, facilitates the serialization of models, which can then be executed in environments that lack Python support. This makes it ideal for deploying models on embedded systems, C++ environments, mobile applications, and even web browsers. Exporting to TorchScript enables efficient performance and wider applicability of your YOLO11 models across diverse platforms.
### How can I export my YOLOv8 model to TorchScript using Ultralytics?
### How can I export my YOLO11 model to TorchScript using Ultralytics?
To export a YOLOv8 model to TorchScript, you can use the following example code:
To export a YOLO11 model to TorchScript, you can use the following example code:
!!! example "Usage"
@ -142,14 +142,14 @@ To export a YOLOv8 model to TorchScript, you can use the following example code:
```python
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO("yolov8n.pt")
# Load the YOLO11 model
model = YOLO("yolo11n.pt")
# Export the model to TorchScript format
model.export(format="torchscript") # creates 'yolov8n.torchscript'
model.export(format="torchscript") # creates 'yolo11n.torchscript'
# Load the exported TorchScript model
torchscript_model = YOLO("yolov8n.torchscript")
torchscript_model = YOLO("yolo11n.torchscript")
# Run inference
results = torchscript_model("https://ultralytics.com/images/bus.jpg")
@ -158,18 +158,18 @@ To export a YOLOv8 model to TorchScript, you can use the following example code:
=== "CLI"
```bash
# Export a YOLOv8n PyTorch model to TorchScript format
yolo export model=yolov8n.pt format=torchscript # creates 'yolov8n.torchscript'
# Export a YOLO11n PyTorch model to TorchScript format
yolo export model=yolo11n.pt format=torchscript # creates 'yolo11n.torchscript'
# Run inference with the exported model
yolo predict model=yolov8n.torchscript source='https://ultralytics.com/images/bus.jpg'
yolo predict model=yolo11n.torchscript source='https://ultralytics.com/images/bus.jpg'
```
For more details about the export process, refer to the [Ultralytics documentation on exporting](../modes/export.md).
### Why should I use TorchScript for deploying YOLOv8 models?
### Why should I use TorchScript for deploying YOLO11 models?
Using TorchScript for deploying YOLOv8 models offers several advantages:
Using TorchScript for deploying YOLO11 models offers several advantages:
- **Portability**: Exported models can run in environments without the need for Python, such as C++ applications, embedded systems, or mobile devices.
- **Optimization**: TorchScript supports static graph execution and Just-In-Time (JIT) compilation, which can optimize model performance.
@ -178,24 +178,24 @@ Using TorchScript for deploying YOLOv8 models offers several advantages:
For more insights into deployment, visit the [PyTorch Mobile Documentation](https://pytorch.org/mobile/home/), [TorchServe Documentation](https://pytorch.org/serve/getting_started.html), and [C++ Deployment Guide](https://pytorch.org/tutorials/advanced/cpp_export.html).
### What are the installation steps for exporting YOLOv8 models to TorchScript?
### What are the installation steps for exporting YOLO11 models to TorchScript?
To install the required package for exporting YOLOv8 models, use the following command:
To install the required package for exporting YOLO11 models, use the following command:
!!! tip "Installation"
=== "CLI"
```bash
# Install the required package for YOLOv8
# Install the required package for YOLO11
pip install ultralytics
```
For detailed instructions, visit the [Ultralytics Installation guide](../quickstart.md). If any issues arise during installation, consult the [Common Issues guide](../guides/yolo-common-issues.md).
### How do I deploy my exported TorchScript YOLOv8 models?
### How do I deploy my exported TorchScript YOLO11 models?
After exporting YOLOv8 models to the TorchScript format, you can deploy them across a variety of platforms:
After exporting YOLO11 models to the TorchScript format, you can deploy them across a variety of platforms:
- **C++ API**: Ideal for low-overhead, highly efficient production environments.
- **Mobile Deployment**: Use [PyTorch Mobile](https://pytorch.org/mobile/home/) for iOS and Android applications.

2
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@ -134,7 +134,7 @@ The `ultra.examples` snippets are to useful for anyone looking to learn how to g
```python
from ultralytics import ASSETS, YOLO
model = YOLO("yolov8n.pt", task="detect")
model = YOLO("yolo11n.pt", task="detect")
results = model(source=ASSETS / "bus.jpg")
for result in results:

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@ -1,12 +1,12 @@
---
comments: true
description: Learn how to enhance YOLOv8 experiment tracking and visualization with Weights & Biases for better model performance and management.
keywords: YOLOv8, Weights & Biases, model training, experiment tracking, Ultralytics, machine learning, computer vision, model visualization
description: Learn how to enhance YOLO11 experiment tracking and visualization with Weights & Biases for better model performance and management.
keywords: YOLO11, Weights & Biases, model training, experiment tracking, Ultralytics, machine learning, computer vision, model visualization
---
# Enhancing YOLOv8 Experiment Tracking and Visualization with Weights & Biases
# Enhancing YOLO11 Experiment Tracking and Visualization with Weights & Biases
[Object detection](https://www.ultralytics.com/glossary/object-detection) models like [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) have become integral to many [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) applications. However, training, evaluating, and deploying these complex models introduces several challenges. Tracking key training metrics, comparing model variants, analyzing model behavior, and detecting issues require substantial instrumentation and experiment management.
[Object detection](https://www.ultralytics.com/glossary/object-detection) models like [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) have become integral to many [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) applications. However, training, evaluating, and deploying these complex models introduces several challenges. Tracking key training metrics, comparing model variants, analyzing model behavior, and detecting issues require substantial instrumentation and experiment management.
<p align="center">
<br>
@ -16,10 +16,10 @@ keywords: YOLOv8, Weights & Biases, model training, experiment tracking, Ultraly
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> How to use Ultralytics YOLOv8 with Weights and Biases
<strong>Watch:</strong> How to use Ultralytics YOLO11 with Weights and Biases
</p>
This guide showcases Ultralytics YOLOv8 integration with Weights & Biases' for enhanced experiment tracking, model-checkpointing, and visualization of model performance. It also includes instructions for setting up the integration, training, fine-tuning, and visualizing results using Weights & Biases' interactive features.
This guide showcases Ultralytics YOLO11 integration with Weights & Biases' for enhanced experiment tracking, model-checkpointing, and visualization of model performance. It also includes instructions for setting up the integration, training, fine-tuning, and visualizing results using Weights & Biases' interactive features.
## Weights & Biases
@ -29,9 +29,9 @@ This guide showcases Ultralytics YOLOv8 integration with Weights & Biases' for e
[Weights & Biases](https://wandb.ai/site) is a cutting-edge MLOps platform designed for tracking, visualizing, and managing [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) experiments. It features automatic logging of training metrics for full experiment reproducibility, an interactive UI for streamlined data analysis, and efficient model management tools for deploying across various environments.
## YOLOv8 Training with Weights & Biases
## YOLO11 Training with Weights & Biases
You can use Weights & Biases to bring efficiency and automation to your YOLOv8 training process.
You can use Weights & Biases to bring efficiency and automation to your YOLO11 training process.
## Installation
@ -42,11 +42,11 @@ To install the required packages, run:
=== "CLI"
```bash
# Install the required packages for YOLOv8 and Weights & Biases
# Install the required packages for YOLO11 and Weights & Biases
pip install --upgrade ultralytics==8.0.186 wandb
```
For detailed instructions and best practices related to the installation process, be sure to check our [YOLOv8 Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
For detailed instructions and best practices related to the installation process, be sure to check our [YOLO11 Installation guide](../quickstart.md). While installing the required packages for YOLO11, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
## Configuring Weights & Biases
@ -66,11 +66,11 @@ Start by initializing the Weights & Biases environment in your workspace. You ca
Navigate to the Weights & Biases authorization page to create and retrieve your API key. Use this key to authenticate your environment with W&B.
## Usage: Training YOLOv8 with Weights & Biases
## Usage: Training YOLO11 with Weights & Biases
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.
Before diving into the usage instructions for YOLO11 model training with Weights & Biases, be sure to check out the range of [YOLO11 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 YOLO11 with Weights & Biases"
=== "Python"
@ -84,7 +84,7 @@ Before diving into the usage instructions for YOLOv8 model training with Weights
wandb.init(project="ultralytics", job_type="training")
# Load a YOLO model
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
# Add W&B Callback for Ultralytics
add_wandb_callback(model, enable_model_checkpointing=True)
@ -108,7 +108,7 @@ Let's understand the steps showcased in the usage code snippet above.
- **Step 1: Initialize a Weights & Biases Run**: Start by initializing a Weights & Biases run, specifying the project name and the job type. This run will track and manage the training and validation processes of your model.
- **Step 2: Define the YOLOv8 Model and Dataset**: Specify the model variant and the dataset you wish to use. The YOLO model is then initialized with the specified model file.
- **Step 2: Define the YOLO11 Model and Dataset**: Specify the model variant and the dataset you wish to use. The YOLO model is then initialized with the specified model file.
- **Step 3: Add Weights & Biases Callback for Ultralytics**: This step is crucial as it enables the automatic logging of training metrics and validation results to Weights & Biases, providing a detailed view of the model's performance.
@ -132,13 +132,13 @@ Upon running the usage code snippet above, you can expect the following key outp
### Viewing the Weights & Biases Dashboard
After running the usage code snippet, you can access the Weights & Biases (W&B) dashboard through the provided link in the output. This dashboard offers a comprehensive view of your model's training process with YOLOv8.
After running the usage code snippet, you can access the Weights & Biases (W&B) dashboard through the provided link in the output. This dashboard offers a comprehensive view of your model's training process with YOLO11.
## Key Features of the Weights & Biases Dashboard
- **Real-Time Metrics Tracking**: Observe metrics like loss, accuracy, and validation scores as they evolve during the training, offering immediate insights for model tuning. [See how experiments are tracked using Weights & Biases](https://imgur.com/D6NVnmN).
- **Hyperparameter Optimization**: Weights & Biases aids in fine-tuning critical parameters such as [learning rate](https://www.ultralytics.com/glossary/learning-rate), batch size, and more, enhancing the performance of YOLOv8.
- **Hyperparameter Optimization**: Weights & Biases aids in fine-tuning critical parameters such as [learning rate](https://www.ultralytics.com/glossary/learning-rate), batch size, and more, enhancing the performance of YOLO11.
- **Comparative Analysis**: The platform allows side-by-side comparisons of different training runs, essential for assessing the impact of various model configurations.
@ -150,11 +150,11 @@ After running the usage code snippet, you can access the Weights & Biases (W&B)
- **Viewing Inference Results with Image Overlay**: Visualize the prediction results on images using interactive overlays in Weights & Biases, providing a clear and detailed view of model performance on real-world data. For more detailed information on Weights & Biases' image overlay capabilities, check out this [link](https://docs.wandb.ai/guides/track/log/media/#image-overlays). [See how Weights & Biases' image overlays helps visualize model inferences](https://imgur.com/a/UTSiufs).
By using these features, you can effectively track, analyze, and optimize your YOLOv8 model's training, ensuring the best possible performance and efficiency.
By using these features, you can effectively track, analyze, and optimize your YOLO11 model's training, ensuring the best possible performance and efficiency.
## Summary
This guide helped you explore Ultralytics' YOLOv8 integration with Weights & Biases. It illustrates the ability of this integration to efficiently track and visualize model training and prediction results.
This guide helped you explore Ultralytics' YOLO11 integration with Weights & Biases. It illustrates the ability of this integration to efficiently track and visualize model training and prediction results.
For further details on usage, visit [Weights & Biases' official documentation](https://docs.wandb.ai/guides/integrations/ultralytics/).
@ -162,19 +162,19 @@ Also, be sure to check out the [Ultralytics integration guide page](../integrati
## FAQ
### How do I install the required packages for YOLOv8 and Weights & Biases?
### How do I install the required packages for YOLO11 and Weights & Biases?
To install the required packages for YOLOv8 and Weights & Biases, open your command line interface and run:
To install the required packages for YOLO11 and Weights & Biases, open your command line interface and run:
```bash
pip install --upgrade ultralytics==8.0.186 wandb
```
For further guidance on installation steps, refer to our [YOLOv8 Installation guide](../quickstart.md). If you encounter issues, consult the [Common Issues guide](../guides/yolo-common-issues.md) for troubleshooting tips.
For further guidance on installation steps, refer to our [YOLO11 Installation guide](../quickstart.md). If you encounter issues, consult the [Common Issues guide](../guides/yolo-common-issues.md) for troubleshooting tips.
### What are the benefits of integrating Ultralytics YOLOv8 with Weights & Biases?
### What are the benefits of integrating Ultralytics YOLO11 with Weights & Biases?
Integrating Ultralytics YOLOv8 with Weights & Biases offers several benefits including:
Integrating Ultralytics YOLO11 with Weights & Biases offers several benefits including:
- **Real-Time Metrics Tracking:** Observe metric changes during training for immediate insights.
- **Hyperparameter Optimization:** Improve model performance by fine-tuning learning rate, [batch size](https://www.ultralytics.com/glossary/batch-size), etc.
@ -184,9 +184,9 @@ Integrating Ultralytics YOLOv8 with Weights & Biases offers several benefits inc
Explore these features in detail in the Weights & Biases Dashboard section above.
### How can I configure Weights & Biases for YOLOv8 training?
### How can I configure Weights & Biases for YOLO11 training?
To configure Weights & Biases for YOLOv8 training, follow these steps:
To configure Weights & Biases for YOLO11 training, follow these steps:
1. Run the command to initialize Weights & Biases:
```bash
@ -198,9 +198,9 @@ To configure Weights & Biases for YOLOv8 training, follow these steps:
Detailed setup instructions can be found in the Configuring Weights & Biases section above.
### How do I train a YOLOv8 model using Weights & Biases?
### How do I train a YOLO11 model using Weights & Biases?
For training a YOLOv8 model using Weights & Biases, use the following steps in a Python script:
For training a YOLO11 model using Weights & Biases, use the following steps in a Python script:
```python
import wandb
@ -212,7 +212,7 @@ from ultralytics import YOLO
wandb.init(project="ultralytics", job_type="training")
# Load a YOLO model
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
# Add W&B Callback for Ultralytics
add_wandb_callback(model, enable_model_checkpointing=True)
@ -232,9 +232,9 @@ wandb.finish()
This script initializes Weights & Biases, sets up the model, trains it, and logs results. For more details, visit the Usage section above.
### Why should I use Ultralytics YOLOv8 with Weights & Biases over other platforms?
### Why should I use Ultralytics YOLO11 with Weights & Biases over other platforms?
Ultralytics YOLOv8 integrated with Weights & Biases offers several unique advantages:
Ultralytics YOLO11 integrated with Weights & Biases offers several unique advantages:
- **High Efficiency:** Real-time tracking of training metrics and performance optimization.
- **Scalability:** Easily manage large-scale training jobs with robust resource monitoring and utilization tools.