> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pixldata.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Exports

The **Export** page is where users extract their annotated data for machine learning model training, analysis, or third-party integration. PixlHub provides a transparent preview of the dataset and supports a wide array of industry-standard formats to ensure compatibility with any AI pipeline.

### **Export Summary & Analytics**

Before generating a file, PixlHub provides a real-time summary of the data included in the current selection. This helps managers verify the dataset's composition without needing to open the files:

* **Task Status Counts:** View the exact number of **Completed**, **Labeled**, and **Ready** tasks.
* **Annotation Types:** A breakdown of the geometry types included (e.g., Bounding Boxes, Polygons, Points).
* **Labels Distribution:** A detailed list showing how many instances of each class (e.g., "Pothole," "Vehicle") are present in the export.

### **Filtering Options**

To export specific subsets of data, users can apply a **Date Range Filter**. This allows for the extraction of only those tasks that were completed within a specific timeframe, making it easy to manage versioning or incremental model updates.

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### **Supported Export Formats**

PixlHub supports a diverse range of formats, including a **Schema Live Preview** that shows an example structure of the chosen format before the export starts:

* **JSON (Native):** The most comprehensive format. It supports all annotation types and nested attributes. Recommended for backups or migrating data between PixlHub projects.
* **COCO JSON:** The industry standard for object detection and segmentation tasks.
* **YOLO (Darknet):** Optimized for YOLO-based object detection models.
* **YOLOv8 Segmentation:** Specifically formatted for the latest segmentation models in the YOLO ecosystem.
* **Pascal VOC (XML):** A classic format widely used in academic and legacy computer vision projects.
* **CSV (Simple):** A tabular format for quick data analysis or spreadsheet-based reviews.
* **Custom Template:** Allows users to define their own structure to meet specific pipeline requirements.

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### **Advanced Export Options**

Users can fine-tune the contents of the export package using the following configuration toggles:

* **Include Asset Metadata:** Adds dimensions, batch information, and other asset-level details to the export.
* **Include Non-Reviewed Tasks:** By default, exports often focus on completed data. This option allows including tasks that are **Labeled** but have not yet passed the **Review** stage.
* **Include Review Metadata:** Includes information regarding the review process, such as the reviewer's name and the date of approval.
* **Organize by Train/Val/Test Split:** Automatically organizes the exported files into separate folders based on the split assignments defined in the **Tasks** page. This makes the data immediately ready for model training scripts.

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### **Export History**

Every export generated is stored in the **Export History** table. This allows team members to download previous versions of the dataset at any time, providing a clear audit trail of what data was used for which model version.