---
title: Train YOLO26 Models on Kaggle
comments: true
description: Learn how to use Kaggle to train Ultralytics YOLO26 models with free GPU/TPU resources. Discover Kaggle's features, benefits, and best practices for efficient model development.
keywords: Kaggle, YOLO26, Ultralytics, machine learning, model training, GPU, TPU, cloud computing, data science, computer vision
---

# A Guide on Using Kaggle to Train Your YOLO26 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 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 YOLO26](https://github.com/ultralytics/ultralytics?tab=readme-ov-file) models. Kaggle Notebooks make using popular machine learning libraries and frameworks in your projects easy. This guide explores Kaggle's main features and shows how to train YOLO26 models on the platform.

## What is Kaggle?

Kaggle is a platform that brings together data scientists from around the world to collaborate, learn, and compete in solving real-world data science problems. Launched in 2010 by Anthony Goldbloom and Jeremy Howard and acquired by Google in 2017, Kaggle enables users to connect, discover and share datasets, use GPU-powered notebooks, and participate in data science competitions. The platform is designed to help both seasoned professionals and eager learners achieve their goals by offering robust tools and resources.

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.

## Installation

Before you can start training YOLO26 models on Kaggle, you need to ensure your notebook environment is properly configured. Follow these essential steps:

### Enable Internet Access

Kaggle notebooks require internet access to download packages and dependencies. To enable internet in your Kaggle notebook:

1. Open your Kaggle notebook
2. Click on the **Settings** panel on the right side of the notebook interface
3. Scroll down to the **Internet** section
4. Toggle the switch to **ON** to enable internet connectivity

**Note**: Internet access is required for installing the Ultralytics package and downloading pre-trained models or datasets. Without internet enabled, package installations will fail.

![Kaggle Notebook Internet Turn on](https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/kaggle_installation.avif)

### Installing Ultralytics

Once internet access is enabled, install the Ultralytics package by running the following command in a notebook cell:

```bash
!pip install ultralytics
```

For the latest development version, you can install directly from GitHub:

```bash
!pip install git+https://github.com/ultralytics/ultralytics.git
```

### Resolving Dependency Conflicts

During installation, you may encounter dependency conflicts, especially with packages like `opencv-python`, `numpy`, or `torch`. Here are common solutions:

#### Method 1: Force Reinstall with --upgrade

If you encounter conflicts with existing packages, force an upgrade:

```bash
!pip install --upgrade --force-reinstall ultralytics
```

#### Method 2: Use --no-deps and Install Dependencies Separately

If conflicts persist, install without dependencies first, then manually install required packages:

```bash
!pip install --no-deps ultralytics
!pip install torch torchvision opencv-python matplotlib pillow pyyaml requests
```

#### Method 3: Restart Kernel After Installation

Sometimes, you need to restart the kernel after installation to resolve import issues:

```bash
!pip install ultralytics
# Then click "Restart Kernel" from the notebook menu
```

#### Method 4: Use Specific Package Versions

If you encounter specific version conflicts, you can pin compatible versions:

```bash
!pip install ultralytics opencv-python==4.8.1.78 numpy==1.24.3
```

#### Common Error Solutions

**Error: "No module named 'ultralytics'"**

- Solution: Ensure internet is enabled and run the installation command again
- Restart the kernel after installation

**Error: "ERROR: pip's dependency resolver does not currently take into account..."**

- Solution: This is usually a warning and can be safely ignored. The installation typically succeeds despite the message
- Alternatively, use Method 2 above to install without dependency resolution

**Error: "ModuleNotFoundError" after installation**

- Solution: Restart the kernel using the restart button in the notebook interface
- Re-run the import statements in a new cell

### Verifying Installation

After installation, verify that Ultralytics is properly installed by running:

```python
import ultralytics

ultralytics.checks()
```

This will display system information and verify that all dependencies are correctly installed.

## Training YOLO26 Using Kaggle

Training YOLO26 models on Kaggle is simple and efficient, thanks to the platform's access to powerful GPUs.

To get started, access the [Kaggle YOLO26 Notebook](https://www.kaggle.com/code/glennjocherultralytics/ultralytics-yolo11-notebook). 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 YOLO26?](https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/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 [YOLO26 Model Training guide](../modes/train.md).

![Using kaggle for machine learning model training with a GPU](https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/using-kaggle-for-machine-learning-model-training-with-a-gpu.avif)

On the [official YOLO26 Kaggle notebook page](https://www.kaggle.com/code/glennjocherultralytics/ultralytics-yolo11-notebook), clicking the three dots in the upper right-hand corner reveals additional options.

![Overview of Options From the Official YOLO26 Kaggle Notebook Page](https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/overview-options-yolov8-kaggle-notebook.avif)

These options include:

- **View Versions**: Browse through different versions of the notebook to see changes over time and revert to previous versions if needed.
- **Copy API Command**: Get an API command to programmatically interact with the notebook, which is useful for automation and integration into workflows.
- **Open in Google Notebooks**: Open the notebook in Google's hosted notebook environment.
- **Open in Colab**: Launch the notebook in [Google Colab](./google-colab.md) for further editing and execution.
- **Follow Comments**: Subscribe to the comments section to get updates and engage with the community.
- **Download Code**: Download the entire notebook as a Jupyter (.ipynb) file for offline use or version control in your local environment.
- **Add to Collection**: Save the notebook to a collection within your Kaggle account for easy access and organization.
- **Bookmark**: Bookmark the notebook for quick access in the future.
- **Embed Notebook**: Get an embed link to include the notebook in blogs, websites, or documentation.

### Common Issues While Working with Kaggle

When working with Kaggle, you might encounter some common issues. Here are key points to help you navigate the platform:

- **Access to GPUs**: In your Kaggle notebooks, you can activate a GPU at any time, with usage allowed for up to 30 hours per week. Kaggle provides the NVIDIA Tesla P100 GPU with 16GB of memory and also offers the option of using a NVIDIA GPU T4 x2. Powerful hardware accelerates your machine-learning tasks, making model training and inference much faster.
- **Kaggle Kernels**: Kaggle Kernels are free Jupyter notebook servers that can integrate GPUs, allowing you to perform machine learning operations on cloud computers. You don't have to rely on your own computer's CPU, avoiding overload and freeing up your local resources.
- **Kaggle Datasets**: Kaggle datasets are free to download. However, it's important to check the license for each dataset to understand any usage restrictions. Some datasets may have limitations on academic publications or commercial use. You can download datasets directly to your Kaggle notebook or anywhere else via the [Kaggle API](https://www.kaggle.com/docs/api).
- **Saving and Committing Notebooks**: To save and commit a notebook on Kaggle, click "Save Version." This saves the current state of your notebook. Once the background kernel finishes generating the output files, you can access them from the Output tab on the main notebook page.
- **Collaboration**: Kaggle supports collaboration, but multiple users cannot edit a notebook simultaneously. Collaboration on Kaggle is asynchronous, meaning users can share and work on the same notebook at different times.
- **Reverting to a Previous Version**: If you need to revert to a previous version of your notebook, open the notebook and click on the three vertical dots in the top right corner to select "View Versions." Find the version you want to revert to, click on the "..." menu next to it, and select "Revert to Version." After the notebook reverts, click "Save Version" to commit the changes.

## Key Features of Kaggle

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](../datasets/index.md) on various topics. You can easily search and use these datasets in your projects, which is particularly handy for training and testing your YOLO26 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](https://www.ultralytics.com/glossary/tpu-tensor-processing-unit), which are beneficial for training complex machine learning models. This allows you to speed up processing and boost the performance of your YOLO26 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 YOLO26 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 YOLO26 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.
- **Notebook Scheduling**: Kaggle allows you to schedule your notebooks to run at specific times. You can automate repetitive tasks without manual intervention, such as training your model at regular intervals.

## Keep Learning about Kaggle

If you want to learn more about Kaggle, here are some helpful resources to guide you:

- [**Kaggle Learn**](https://www.kaggle.com/learn): Discover a variety of free, interactive tutorials on Kaggle Learn. These courses cover essential data science topics and provide hands-on experience to help you master new skills.
- [**Getting Started with Kaggle**](https://www.kaggle.com/code/alexisbcook/getting-started-with-kaggle): This comprehensive guide walks you through the basics of using Kaggle, from joining competitions to creating your first notebook. It's a great starting point for newcomers.
- [**Kaggle Medium Page**](https://medium.com/@kaggleteam): Explore tutorials, updates, and community contributions to Kaggle's Medium page. It's an excellent source for staying up-to-date with the latest trends and gaining deeper insights into data science.
- [**Train Ultralytics YOLO Models Using the Kaggle Integration**](https://www.ultralytics.com/blog/train-ultralytics-yolo-models-using-the-kaggle-integration): This blog post provides additional insights on how to leverage Kaggle specifically for Ultralytics YOLO models.

## Summary

We've seen how Kaggle can boost your YOLO26 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. The integration between Ultralytics YOLO26 and Kaggle creates a seamless environment for developing, training, and deploying state-of-the-art [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models without the need for expensive hardware.

For more details, visit [Kaggle's documentation](https://www.kaggle.com/docs).

Interested in more YOLO26 integrations? Check out the [Ultralytics integration guide](index.md) to explore additional tools and capabilities for your machine learning projects.

## FAQ

### How do I install Ultralytics YOLO26 on Kaggle?

To install Ultralytics YOLO26 on Kaggle:

1. **Enable Internet**: Go to Settings panel and turn ON the Internet toggle
2. **Install Package**: Run `!pip install ultralytics` in a notebook cell
3. **Verify Installation**: Run `import ultralytics; ultralytics.checks()` to confirm

If you encounter dependency conflicts, try `!pip install --upgrade --force-reinstall ultralytics` or restart the kernel after installation. For detailed troubleshooting, see the [Installation section](#installation) above.

### How do I train a YOLO26 model on Kaggle?

Training a YOLO26 model on Kaggle is straightforward. First, access the [Kaggle YOLO26 Notebook](https://www.kaggle.com/code/glennjocherultralytics/ultralytics-yolo11-notebook). 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 [YOLO26 Model Training guide](../modes/train.md).

### What are the benefits of using Kaggle for YOLO26 model training?

Kaggle offers several advantages for training YOLO26 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.
- **Community Collaboration**: Engage with a vast community of data scientists and machine learning enthusiasts.
- **Version Control**: Easily manage different versions of your notebooks and revert to previous versions if needed.

For more details, visit our [Ultralytics integration guide](index.md).

### What common issues might I encounter when using Kaggle for YOLO26, and how can I resolve them?

Common issues include:

- **Access to GPUs**: Ensure you activate a GPU in your notebook settings. Kaggle allows up to 30 hours of GPU usage per week.
- **Internet Not Enabled**: Make sure to enable internet in the Settings panel before installing packages.
- **Dependency Conflicts**: Use `!pip install --upgrade --force-reinstall ultralytics` or install without dependencies using `!pip install --no-deps ultralytics`.
- **Dataset Licenses**: Check the license of each dataset to understand usage restrictions.
- **Saving and Committing Notebooks**: Click "Save Version" to save your notebook's state and access output files from the Output tab.
- **Collaboration**: Kaggle supports asynchronous collaboration; multiple users cannot edit a notebook simultaneously.

For more troubleshooting tips, see the [Installation section](#installation) and our [Common Issues guide](../guides/yolo-common-issues.md).

### Why should I choose Kaggle over other platforms like Google Colab for training YOLO26 models?

Kaggle offers unique features that make it an excellent choice:

- **Public Notebooks**: Share your work with the community for feedback and collaboration.
- **Free Access to TPUs**: Speed up training with powerful TPUs without extra costs.
- **Comprehensive History**: Track changes over time with a detailed history of notebook commits.
- **Resource Availability**: Significant resources are provided for each notebook session, including 12 hours of execution time for CPU and GPU sessions.

For a comparison with Google Colab, refer to our [Google Colab guide](./google-colab.md).

### How can I revert to a previous version of my Kaggle notebook?

To revert to a previous version:

1. Open the notebook and click on the three vertical dots in the top right corner.
2. Select "View Versions."
3. Find the version you want to revert to, click on the "..." menu next to it, and select "Revert to Version."
4. Click "Save Version" to commit the changes.
