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Version: v1.2.0

Import dataset settings: Image instance segmentation

Dataset name​

Name of the dataset.

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 type and experience level determine the settings H2O Hydrogen Torch displays for the experiment
  • The From experiment option allows you to use the settings from a previously run experiment

Data format​

Specifies the data format of the training, validation, and test dataframes.

note
  • Image object detection
    • Supported formats for an image object detection experiment are as follows:
      • Hydrogen Torch format
      • Individual boxes format
      • COCO format
      • Pascal VOC format
  • Image semantic segmentation | Image instance segmentation
    • Supported formats for an image semantic segmentation and image instance segmentation experiment are as follows:
      • Hydrogen Torch format
      • COCO format

Train dataframe​

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

note
  • The records will be combined into mini-batches when training the model.
  • If a validation dataframe is provided, a fold column is not needed in the train dataframe.

Train json​

A .json file in COCO format with test records that H2O Hydrogen Torch uses to test the model.

note

This setting is available when COCO is selected in the Data format setting.

Data folder​

Defines the folder location of the assets (e.g., 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 will use 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 will fully respect the choice of a separate validation dataframe and will 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.

Validation json​

A .json file in COCO format with validation records that H2O Hydrogen Torch will use to validate the model during training.

note

This setting is available when COCO is selected in the Data format setting.

Test dataframe​

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

note

The test dataframe should have the same format as the train dataframe but does not require a label column.

Test json​

A .json file in COCO format with test records that H2O Hydrogen Torch uses to test the model.

note

This setting is available when COCO is selected in the Data format setting.

Data folder test​

Defines the folder location of the assets (e.g., images or texts) H2O Hydrogen Torch will use to test the model. H2O Hydrogen Torch will load 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 will use for each instance mask.

RLE mask column​

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

Image column​

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

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