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New Meta Segment Anything Model 2 (SAM2) Docs page (#14794)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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docs/en/integrations/kaggle.md
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@ -90,3 +90,50 @@ We've seen how Kaggle can boost your YOLOv8 projects by providing free access to
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.
## FAQ
### How do I train a YOLOv8 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).
### What are the benefits of using Kaggle for YOLOv8 model training?
Kaggle offers several advantages for training YOLOv8 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](https://docs.ultralytics.com/integrations/).
### What common issues might I encounter when using Kaggle for YOLOv8, 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.
- **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 our [Common Issues guide](../guides/yolo-common-issues.md).
### Why should I choose Kaggle over other platforms like Google Colab for training YOLOv8 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.