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Version: v1.3.0

Import dataset settings: 3D image semantic segmentation

Dataset name​

Name of the dataset.

Problem category​

This setting defines a particular general problem type category, for example, image.

note
  • The selected problem category (for example, image) determines the options in the Problem type setting.
  • The following option is available when defining the settings of an experiment: From experiment.
    • The From experiment option enables you to utilize the settings of an experiment (another experiment).

Problem type​

Defines the problem type of the experiment, which also defines the settings H2O Hydrogen Torch displays for the experiment.

Note
  • The selected problem category (in the Problem category setting) determines the available problem types.
  • The selected problem type and experience level determine the settings H2O Hydrogen Torch displays for the experiment.

Train dataframe​

Defines a .csv or .pq file containing a dataframe with training records that H2O Hydrogen Torch uses to train the model.

note
  • The records are combined into mini-batches when training the model.
  • If a validation dataframe is provided, a fold column is not needed in the train dataframe.
  • You can now import datasets for inference only. To do so, when defining the setting for an experiment, set the Train dataframe setting to None while setting the Test dataframe setting to the relevant dataframe (as a result, H2O Hydrogen Torch utilizes the relevant dataset for predictions and not for training).

Data folder​

Defines the location of the folder containing assets (for example, images or audio clips) the model utilizes for training. H2O Hydrogen Torch loads assets from this folder during training.

Validation dataframe​

Defines a .csv or .pq file containing a dataframe with validation records that H2O Hydrogen Torch uses to evaluate the model during training.

Note
  • To set a Validation dataframe requires the Validation strategy to be set to Custom holdout validation. In this case, H2O Hydrogen Torch fully respects the choice of a separate validation dataframe and does not perform any internal cross-validation. In other words, the model is trained on the full provided train dataframe, and model performance is evaluated on the provided validation dataframe.
  • The validation dataframe should have the same format as the train dataframe but does not require a fold column.

Test dataframe​

Defines a .csv or .pq file containing a dataframe with test records that H2O Hydrogen Torch uses to test the model.

note
  • The test dataframe should have the same format as the train dataframe but does not require a label column.
  • You can now import datasets for inference only. To do so, when defining the setting for an experiment, set the Train dataframe setting to None while setting the Test dataframe setting to the relevant dataframe (as a result, H2O Hydrogen Torch utilizes the relevant dataset for predictions and not for training).

Data folder test​

Defines the location of the folder containing assets (for example, images, texts, or audio clips) H2O Hydrogen Torch utilizes to test the model. H2O Hydrogen Torch loads the assets from this folder when testing the model. This setting is only available if a test dataframe is selected.

Note

The Data folder test setting appears when you specify a test dataframe in the Test dataframe setting.

Class name column​

Defines the dataset column containing a list of class names that H2O Hydrogen Torch uses for each instance mask.

RLE mask column​

Defines the dataset column containing a list of run-length encoded (RLE) masks that H2O Hydrogen Torch uses for instance class.

Image column​

Defines the dataframe column storing the names of images that H2O Hydrogen Torch loads from the data folder and data folder test when training and testing the model.

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