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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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87
docs/en/integrations/ibm-watsonx.md
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@ -321,3 +321,90 @@ We explored IBM Watsonx key features, and how to train a YOLOv8 model using IBM
For further details on usage, visit [IBM Watsonx official documentation](https://www.ibm.com/watsonx).
Also, be sure to check out the [Ultralytics integration guide page](./index.md), to learn more about different exciting integrations.
## FAQ
### How do I train a YOLOv8 model using IBM Watsonx?
To train a YOLOv8 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`.
3. **Load Data**: Use the Kaggle API to load your dataset into Watsonx.
4. **Preprocess Data**: Organize your dataset into the required directory structure and update the `.yaml` configuration file.
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).
### What are the key features of IBM Watsonx for AI model training?
IBM Watsonx offers several key features for AI model training:
- **Watsonx.ai**: Provides tools for AI development, including access to IBM-supported custom models and third-party models like Llama 3. It includes the Prompt Lab, Tuning Studio, and Flows Engine for comprehensive AI lifecycle management.
- **Watsonx.data**: Supports cloud and on-premises deployments, offering centralized data access, efficient query engines like Presto and Spark, and an AI-powered semantic layer.
- **Watsonx.governance**: Automates compliance, manages risk with alerts, and provides tools for detecting issues like bias and drift. It also includes dashboards and reporting tools for collaboration.
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?
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:
- **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).
### How can I preprocess my dataset for YOLOv8 training on IBM Watsonx?
To preprocess your dataset for YOLOv8 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.
3. **Run Preprocessing Script**: Use a Python script to reorganize your dataset and update the `.yaml` file accordingly.
Here's a sample script to organize your dataset:
```python
import os
import shutil
def organize_files(directory):
for subdir in ["train", "test", "val"]:
subdir_path = os.path.join(directory, subdir)
if not os.path.exists(subdir_path):
continue
images_dir = os.path.join(subdir_path, "images")
labels_dir = os.path.join(subdir_path, "labels")
os.makedirs(images_dir, exist_ok=True)
os.makedirs(labels_dir, exist_ok=True)
for filename in os.listdir(subdir_path):
if filename.endswith(".txt"):
shutil.move(os.path.join(subdir_path, filename), os.path.join(labels_dir, filename))
elif filename.endswith(".jpg") or filename.endswith(".png") or filename.endswith(".jpeg"):
shutil.move(os.path.join(subdir_path, filename), os.path.join(images_dir, filename))
if __name__ == "__main__":
directory = f"{work_dir}/trash_ICRA19/dataset"
organize_files(directory)
```
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?
Before you start training a YOLOv8 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.
- **Jupyter Notebook**: Set up a Jupyter Notebook environment within Watsonx.ai for coding and model training.
For more information on setting up your environment, visit our [Ultralytics Installation guide](../quickstart.md).

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docs/en/integrations/jupyterlab.md
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@ -108,3 +108,102 @@ We've explored how JupyterLab can be a powerful tool for experimenting with Ultr
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.
## FAQ
### How do I use JupyterLab to train a YOLOv8 model?
To train a YOLOv8 model using JupyterLab:
1. Install JupyterLab and the Ultralytics package:
```python
pip install jupyterlab ultralytics
```
2. Launch JupyterLab and open a new notebook.
3. Import the YOLO model and load a pretrained model:
```python
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
```
4. Train the model on your custom dataset:
```python
results = model.train(data="path/to/your/data.yaml", epochs=100, imgsz=640)
```
5. Visualize training results using JupyterLab's built-in plotting capabilities:
```python
%matplotlib inline
from ultralytics.utils.plotting import plot_results
plot_results(results)
```
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?
JupyterLab offers several features that make it ideal for YOLOv8 projects:
1. Interactive code execution: Test and debug YOLOv8 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.
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.
### How can I optimize YOLOv8 model performance using JupyterLab?
To optimize YOLOv8 model performance in JupyterLab:
1. Use the autobatch feature to determine the optimal batch size:
```python
from ultralytics.utils.autobatch import autobatch
optimal_batch_size = autobatch(model)
```
2. Implement [hyperparameter tuning](../guides/hyperparameter-tuning.md) using libraries like Ray Tune:
```python
from ultralytics.utils.tuner import run_ray_tune
best_results = run_ray_tune(model, data="path/to/data.yaml")
```
3. Visualize and analyze model metrics using JupyterLab's plotting capabilities:
```python
from ultralytics.utils.plotting import plot_results
plot_results(results.results_dict)
```
4. Experiment with different model architectures and [export formats](../modes/export.md) to find the best balance of speed and 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.
### How do I handle common issues when working with JupyterLab and YOLOv8?
When working with JupyterLab and YOLOv8, you might encounter some common issues. Here's how to handle them:
1. GPU memory issues:
- Use `torch.cuda.empty_cache()` to clear GPU memory between runs.
- Adjust batch size or image size to fit your GPU memory.
2. Package conflicts:
- Create a separate conda environment for your YOLOv8 projects to avoid conflicts.
- Use `!pip install package_name` in a notebook cell to install missing packages.
3. Kernel crashes:
- Restart the kernel and run cells one by one to identify the problematic code.

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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.