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The Import page allows you to bring existing annotations from external sources or previous projects into PixlHub. Whether you are migrating a dataset or utilizing pre-labeled data, the import system ensures that labels are correctly mapped to your current assets.

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Pre-Import Checklist

To ensure a successful data merge, follow these preparation steps:
  • Task Status: Change the status of the target tasks to “Prepare.” Annotations can only be imported into tasks that are in this stage to prevent overwriting active work.
  • Format Selection: Prepare your annotation files in one of the supported formats: PixlHub Native (JSON), COCO, or YOLO.

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Import Workflow

The system follows a four-step verification process to maintain data integrity:
  1. Upload: Drag and drop or click to upload your annotation file (Native JSON or COCO/YOLO formats).
  2. Review Validation: PixlHub automatically parses the file and provides a validation report. This shows how many labels were found and how they align with your current project.
  3. Conflict Check: Review any warnings regarding unmatched filenames or ID discrepancies.
  4. Confirm: Once the validation results are reviewed, click confirm to apply the annotations to your tasks.

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Supported Formats & Matching Logic

The accuracy of an import depends on the format and the underlying matching logic:
  • PixlHub Native Format (Recommended): This is the most accurate method. It uses Unique IDs to match annotations directly to the specific asset and label schema. It is highly recommended for project migrations or backups.
  • External Formats (COCO & YOLO): These formats rely on Filename Matching. While convenient for importing data from other platforms, this method is less precise as it can be affected by duplicate filenames across different folders.
Note: For the best accuracy and to avoid issues with duplicate filenames, use the PixlHub Native format whenever possible.

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Import History

Every import attempt is logged in the Import History table, providing a full audit trail of external data additions. This table includes:
  • Date & Format: When the import occurred and which file type was used.
  • Status: Whether the import was successful, failed, or completed with warnings.
  • Imported vs. Total: A comparison of the number of annotations successfully added versus the total found in the file.
  • Matched Tasks: The number of tasks that successfully received new labels.
  • Errors: A detailed count of any issues encountered, allowing for quick troubleshooting of file formatting or naming conflicts.