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Version: v1.3.0

Import dataset settings: Image object detection

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.

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 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).

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.

Train labels folder​

Specifies a directory containing a train labels folder that needs to contain .xml files with labels in Pascal VOC format with training records that H2O Hydrogen Torch uses to train the model. The records are combined into mini-batches when training the model.

note

This setting is available when Pascal VOC is selected in the Data format setting.

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.

Validation json​

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

note

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

Validation labels folder​

Specifies a directory containing a validation labels folder that needs to contain .xml files with labels in Pascal VOC format with validation records that H2O Hydrogen Torch uses to validate the model during training.

note

This setting is available when Pascal VOC 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 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).

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.

Test labels folder​

Specifies a directory containing one or more validation labels folder that needs to contain .xml files with labels in Pascal VOC format with test records that H2O Hydrogen Torch uses to test the model.

note

This setting is available when Pascal VOC is selected in the Data format setting.

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.

Unlabeled dataframe​

Defines a separate .csv or .pq file (depending on the problem type) containing a dataframe with unlabeled records that H2O Hydrogen Torch utilizes to generate pseudo labels. H2O Hydrogen Torch first trains the model with the provided labeled data (Train dataframe). Right after, the model predicts pseudo labels for the provided unlabeled dataframe before doing another training run that combines the original labels and pseudo labels.

note
  • Image regression | Image classification | Image object detection
    • The unlabeled dataframe just needs to contain a single image column
  • Text regression | Text classification
    • The unlabeled dataframe just needs to contain a single text column
  • Audio regression | Audio classification | Speech recognition
    • The unlabeled dataframe just needs to contain a single audio column
  • Image regression | Image classification | Image object detection | Audio regression | Audio classification | Speech recognition
    • Assets (images or audios) need to be located in the Data folder (setting)
  • All supported problem types
    • The training time can significantly increase depending on the size of the unlabeled data
tip

As labeling can be expensive, having additional unlabeled data is quite common. Providing this unlabeled data in H2O Hydrogen Torch trains the model semi-supervised, potentially improving the model quality in contrast to only training on labeled data.

Class name column​

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

X min column​

Defines the dataset column containing a list of minimum X positions H2O Hydrogen Torch uses for each bounding box.

Y min column​

Defines the dataset column containing a list of minimum Y positions H2O Hydrogen Torch uses for each bounding box.

X max column​

Defines the dataset column containing a list of maximum X positions H2O Hydrogen Torch uses for each bounding box.

Y max column​

Defines the dataset column containing a list of maximum Y positions H2O Hydrogen Torch uses for each bounding box.

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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