Open Images

How to export and import data in Open Images format

The Open Images format is based on a large-scale, diverse dataset that contains object detection, object segmentation, visual relationship, and localized narratives annotations.

Its export data format is compatible with many object detection and segmentation models.

For more information, see:

Open Images export

For export of images:

  • Supported annotations: Bounding Boxes (detection), Tags (classification), Polygons (segmentation).

  • Supported attributes:

    • Tags: score must be defined for labels as text or number. The confidence level from 0 to 1.
    • Bounding boxes:
      score must be defined for labels as text or number. The confidence level from 0 to 1.
      occluded as both UI option and a separate attribute. Whether the object is occluded by another object.
      truncated must be defined for labels as checkbox. Whether the object extends beyond the boundary of the image.
      is_group_of must be defined for labels as checkbox. Whether the object represents a group of objects of the same class.
      is_depiction must be defined for labels as checkbox. Whether the object is a depiction (such as a drawing) rather than a real object.
      is_inside must be defined for labels as checkbox. Whether the object is seen from the inside.
    • Masks:
      box_id must be defined for labels as text. An identifier for the bounding box associated with the mask.
      predicted_iou must be defined for labels as text or number. Predicted IoU value with respect to the ground truth.

  • Tracks: Not supported.

The downloaded file is a .zip archive with the following structure:

└─ taskname.zip/
    ├── annotations/
    │   ├── bbox_labels_600_hierarchy.json
    │   ├── class-descriptions.csv
    |   ├── images.meta  # additional file with information about image sizes
    │   ├── <subset_name>-image_ids_and_rotation.csv
    │   ├── <subset_name>-annotations-bbox.csv
    │   ├── <subset_name>-annotations-human-imagelabels.csv
    │   └── <subset_name>-annotations-object-segmentation.csv
    ├── images/
    │   ├── subset1/
    │   │   ├── <image_name101.jpg>
    │   │   ├── <image_name102.jpg>
    │   │   └── ...
    │   ├── subset2/
    │   │   ├── <image_name201.jpg>
    │   │   ├── <image_name202.jpg>
    │   │   └── ...
    |   ├── ...
    └── masks/
        ├── subset1/
        │   ├── <mask_name101.png>
        │   ├── <mask_name102.png>
        │   └── ...
        ├── subset2/
        │   ├── <mask_name201.png>
        │   ├── <mask_name202.png>
        │   └── ...
        ├── ...

Open Images import

Uploaded file: a zip archive of the following structure:

└─ upload.zip/
    ├── annotations/
    │   ├── bbox_labels_600_hierarchy.json
    │   ├── class-descriptions.csv
    |   ├── images.meta  # optional, file with information about image sizes
    │   ├── <subset_name>-image_ids_and_rotation.csv
    │   ├── <subset_name>-annotations-bbox.csv
    │   ├── <subset_name>-annotations-human-imagelabels.csv
    │   └── <subset_name>-annotations-object-segmentation.csv
    └── masks/
        ├── subset1/
        │   ├── <mask_name101.png>
        │   ├── <mask_name102.png>
        │   └── ...
        ├── subset2/
        │   ├── <mask_name201.png>
        │   ├── <mask_name202.png>
        │   └── ...
        ├── ...

Image ids in the <subset_name>-image_ids_and_rotation.csv should match with image names in the task.