Skip to main content

Modifying Dataviews

An experiment that has been executed can be cloned, then the cloned experiment's execution details can be modified, and the modified experiment can be executed.

In addition to all the ClearML tuning capabilities, the ClearML Enterprise WebApp (UI) enables modifying Dataviews, including:

Selecting Dataviews#

To choose a Dataview, do any of the following:

  • Create a new Dataview
    • Click + and then follow the instructions below to select Hyper-Dataset versions, filter frames, map labels (label translation), and set label enumeration, data augmentation, and iteration controls.
  • Select a different Dataview already associated with the experiment.
    • In the SELECTED DATAVIEW list, choose a Dataview.
  • Import a different Dataview associated with the same or another project.
    • Click Import (Import dataview) and then select Import to current dataview or Import as aux dataview.

After importing a Dataview, it can be renamed and / or removed.

Selecting Dataset Versions#

To input data from a different data source or different version of a data source, select a different Dataset version used by the Dataview.

To select Dataset versions for input data:

  1. In the INPUT area, click EDIT.

  2. Do any of the following:

    • Add a Dataset version - Input frames from another a version of another Dataset.

      • Click +

      • Select a Dataset and a Dataset version

    • Remove a Dataset version - Do not input frames from a Dataset version.

    Select frames from as many Dataset versions as are needed.

  3. Click SAVE.

Filtering Frames#

Filtering of SingleFrames iterated by a Dataview for input to the experiment is accomplished by frame filters. For more detailed information, see Filtering.

To modify frame filtering:

  1. In the FILTERING area, click EDIT.

  2. For each frame filter:

    1. Select the Hyper-Dataset version to which the frame filter applies.

    2. Add, change, or remove any combination of the following rules:

      • ROI rule - Include or exclude frames containing any single ROI with any combination of labels in the Dataset version. Specify a range of the number of matching ROI (instances) per frame, and a range of confidence levels.
      • Frame rule - Filter by frame metadata key-value pairs, or ROI labels.
      • Source rule - Filter by frame source dictionary key-value pairs.
    3. Optionally, debias input data by setting ratios for frames returned by the Dataview for each frame filter. These ratios allow adjusting an imbalance in input data.

  3. Click SAVE.

Mapping Labels (Label Translation)#

Modify the ROI label mapping rules, which translate one or more input labels to another label for the output model. Labels that are not mapped are ignored.

To modify label mapping:

  1. In the MAPPING section, click EDIT

    • Add (+) or edit a mapping:

      1. Select the Hyper-Dataset and version whose labels will be mapped.

      2. Select one or more labels to map.

      3. Select or enter the label to map to in the output model.

    • Remove (Trash) a mapping.

  2. Click SAVE

Label Enumeration#

Modify the label enumeration assigned to output models.

To modify label enumeration:

  1. In the LABELS ENUMERATION section, click EDIT.

    • Add (+) or edit an enumeration:

      • Select a label and then enter an integer for it.
    • Remove (Trash) an enumeration.

  2. Click SAVE.

Data Augmentation#

Modify the on-the-fly data augmentation applied to frame input from the select Hyper-Dataset versions and filtered by the frame filters. Data augmentation is applied in steps, where each step applies a method, operation, and strength.

For more detailed information, see Data Augmentation.

To modify data augmentation

  1. In the AUGMENTATION section, click EDIT.

    • Add (+) or edit an augmentation step - Select a METHOD, OPERATION, and STRENGTH.
    • Remove (Trash) an augmentation step.
  2. Click SAVE.

Iteration Controls#

Modify the frame iteration performed by the Dataview to control the order, number, timing, and reproducibility of frames for training.

For more detailed information, see Iteration Control.

To modify iteration controls:

  1. In the ITERATION sections, click EDIT.

  2. Select the ORDER of the SingleFrames returned by the iteration, either:

    • Sequential - Iterate SingleFrames in sorted order by context ID and timestamp.
    • Random - Iterate SingleFrames randomly using the random seed you can set (see Random Seed below).
  3. Select the frame REPETITION option, either:

    • Use Each Frame Once

    • Limit Frames

    • Infinite Iterations

  4. Select the RANDOM SEED - If the experiment is rerun and the seed remains unchanged, the frame iteration is the same.

  5. For video, enter a CLIP LENGTH - For video data sources, in the number of sequential frames from a clip to iterate.

  6. Click SAVE.