Tensor Relationships

Understanding the correct data layout for successful visualization.

Understanding the Relationships Between Hub Tensors

Indexing

Hub datasets and their tensors are indexed like ds[index] or ds.tensor_name[index], and data at the same index are assumed to be related. For example, a bounding_box at index 100 is assumed to apply to the image at index 100.

Relationships Between Tensors

For datasets with multiple tensors, it is important to follow the conventions below in order for the visualizer to correctly infer how tensors are related.

By default, in the absence of groups, the visualizer assumes that all tensors are related to each other.

This works well for simple use cases. For example, it is correct to assume that the images, labels, and boxes tensors are related in the dataset below:

ds
-> images (htype = image)
-> labels (htype = class_label)
-> boxes (htype = bbox)

However, if datasets are highly complex, assuming that all tensor are related may lead to visualization errors, because every tensor may not be related to every other tensor:

ds
-> images (htype = image)
-> vehicle_labels (htype = class_label)
-> vehicle_boxes (htype = bbox)
-> people_labels (htype = class_label)
-> people_masks (htype = binary_mask)

In the example above, only some of the annotation tensors are related to each other:

• vehicle_labels -> vehicle_boxes: Boxes and labels describing cars, trucks, etc.

• people_labels -> people_masks: Binary masks and labels describing adults, toddlers, etc.

The best method for disambiguating the relationships between tensors is to place them in groups, because the visualizer assumes that annotation tensors in different groups are not related.

In the example above, the following groups could be used to disambiguate the annotations:

ds
-> images (htype = image)
-> vehicles (group)
   -> vehicle_labels (htype = class_label)
   -> vehicle_boxes (htype = bbox)
-> people (group)
   -> people_labels (htype = class_label)
   -> people_masks (htype = binary_mask)