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.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.
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.