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166 lines
6.0 KiB
Markdown
166 lines
6.0 KiB
Markdown
---
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title: Modifying Dataviews
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---
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An experiment that has been executed can be [cloned](../../webapp/webapp_exp_reproducing.md), then the cloned experiment's
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execution details can be modified, and the modified experiment can be executed.
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In addition to all the [ClearML tuning capabilities](../../webapp/webapp_exp_tuning.md), the **ClearML Enterprise WebApp** (UI)
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enables modifying [Dataviews](webapp_dataviews.md), including:
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* [Selected Dataview](#selecting-dataviews)
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* [Dataset versions](#selecting-dataset-versions)
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* [Frame filtering](#filtering-frames)
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* [Label mapping](#mapping-labels-label-translation)
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* [Class label enumeration](#label-enumeration)
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* [Data augmentation](#data-augmentation)
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* [Input frame iteration controls](#iteration-controls)
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## Selecting Dataviews
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**To choose a Dataview**, do any of the following:
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* Create a new Dataview
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* Click **+** and then follow the instructions below to select Hyper-Dataset versions, filter frames, map labels (label translation),
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and set label enumeration, data augmentation, and iteration controls.
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* Select a different Dataview already associated with the experiment.
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* In the **SELECTED DATAVIEW** list, choose a Dataview.
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* Import a different Dataview associated with the same or another project.
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* Click <img src="/docs/latest/icons/ico-import.svg" alt="Import" className="icon size-md space-sm" /> (**Import dataview**) and then
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select **Import to current dataview** or **Import as aux dataview**.
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:::note
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After importing a Dataview, it can be renamed and/or removed.
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:::
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### Selecting Dataset Versions
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To input data from a different data source or different version of a data source, select a different Dataset version used
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by the Dataview.
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**To select Dataset versions for input data:**
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1. In the **INPUT** area, click **EDIT**.
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1. Do any of the following:
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* Add a Dataset version - Input frames from another version of another Dataset.
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* Click **+**
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* Select a Dataset and a Dataset version
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* Remove a Dataset version - Do not input frames from a Dataset version.
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Select frames from as many Dataset versions as are needed.
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1. Click **SAVE**.
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## Filtering Frames
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Filtering of SingleFrames iterated by a Dataview for input to the experiment is accomplished by frame filters.
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For more detailed information, see [Filtering](../dataviews.md#filtering).
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**To modify frame filtering:**
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1. In the **FILTERING** area, click **EDIT**.
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1. For each frame filter:
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1. Select the Hyper-Dataset version to which the frame filter applies.
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1. Add, change, or remove any combination of the following rules:
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* ROI rule - Include or exclude frames containing any single ROI with any combination of labels in the Dataset
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version. Specify a range of the number of matching ROI (instances) per frame, and a range of confidence levels.
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* Frame rule - Filter by frame metadata key-value pairs, or ROI labels.
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* Source rule - Filter by frame `source` dictionary key-value pairs.
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1. Optionally, debias input data by setting ratios for frames returned by the Dataview for each frame filter. These
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ratios allow adjusting an imbalance in input data.
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1. Click **SAVE**.
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## Mapping Labels (Label Translation)
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Modify the ROI label mapping rules, which translate one or more input labels to another label for the output model. Labels
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that are not mapped are ignored.
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**To modify label mapping:**
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1. In the **MAPPING** section, click **EDIT**
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* Add (**+**) or edit a mapping:
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1. Select the Hyper-Dataset and version whose labels will be mapped.
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1. Select one or more labels to map.
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1. Select or enter the label to map to in the output model.
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* Remove (<img src="/docs/latest/icons/ico-trash.svg" alt="Trash" className="icon size-md space-sm" />) a mapping.
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1. Click **SAVE**
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## Label Enumeration
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Modify the label enumeration assigned to output models.
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**To modify label enumeration:**
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1. In the **LABELS ENUMERATION** section, click **EDIT**.
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* Add (**+**) or edit an enumeration:
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* Select a label and then enter an integer for it.
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* Remove (<img src="/docs/latest/icons/ico-trash.svg" alt="Trash" className="icon size-md space-sm" />) an enumeration.
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1. Click **SAVE**.
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## Data Augmentation
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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.
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For more detailed information, see [Data Augmentation](../dataviews.md#data-augmentation).
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**To modify data augmentation**
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1. In the **AUGMENTATION** section, click **EDIT**.
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* Add (**+**) or edit an augmentation step - Select a **METHOD**, **OPERATION**, and **STRENGTH**.
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* Remove (<img src="/docs/latest/icons/ico-trash.svg" alt="Trash" className="icon size-md space-sm" />) an augmentation step.
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1. Click **SAVE**.
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## Iteration Controls
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Modify the frame iteration performed by the Dataview to control the order, number, timing, and reproducibility of frames
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for training.
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For more detailed information, see [Iteration Control](../dataviews.md#iteration-control).
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**To modify iteration controls:**
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1. In the **ITERATION** sections, click **EDIT**.
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1. Select the **ORDER** of the SingleFrames returned by the iteration, either:
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* **Sequential** - Iterate SingleFrames in sorted order by context ID and timestamp.
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* **Random** - Iterate SingleFrames randomly using the random seed you can set (see Random Seed below).
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1. Select the frame **REPETITION** option, either:
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* **Use Each Frame Once**
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* **Limit Frames**
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* **Infinite Iterations**
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1. Select the **RANDOM SEED** - If the experiment is rerun and the seed remains unchanged, the frame iteration is the same.
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1. For video, enter a **CLIP LENGTH** - For video data sources, in the number of sequential frames from a clip to iterate.
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1. Click **SAVE**.
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