Skip to main content
Version: v1.2.0

Prediction settings: Image classification

To score (predict) new data using a built model through the H2O Hydrogen Torch UI, H2O Hydrogen Torch requires the specification of certain settings refer as prediction settings (which are comprised of a certain dataset, prediction and environment settings similar to those when creating an experiment). Below observe the prediction settings for an image classification model.

General settings​

Experiment​

Defines the model (experiment) H2O Hydrogen Torch uses to score new data.

Prediction name​

It defines the name of the prediction.

Dataset settings​

Dataset​

Specifies the dataset to use for scoring.

Test dataframe​

Defines the file(s) containing the test dataframe that H2O Hydrogen Torch will use for scoring.

note
  • Image regression | Image classification | Image metric learning | Text regression | Text classification | Text sequence to sequence | Text span prediction | Text token classification | Text metric learning | Audio regression | Audio classification
    • Defines a .csv or .pq file containing the test dataframe that H2O Hydrogen Torch will use for scoring.
      note

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

  • Image object detection | Image semantic segmentation | Image instance segmentation
    • Defines a .pq file containing the test dataframe that H2O Hydrogen Torch will use for scoring.

Data folder test​

Defines the folder location of the assets (e.g., images or audios) H2O Hydrogen Torch utilizes for scoring. H2O Hydrogen Torch loads assets from this folder during scoring.

Image column​

Specifies the dataframe column storing the names of images that H2O Hydrogen Torch will load from the Data folder test during scoring.

Prediction settings​

Metric​

Specifies the evaluation metric to use to evaluate the model's accuracy.

note

Usually, the evaluation metric should reflect the quantitative way of assessing the model's value for the corresponding use case.

Probability threshold​

Specifies the evaluation metric to use to evaluate the model's accuracy.

note

Usually, the evaluation metric should reflect the quantitative way of assessing the model's value for the corresponding use case.

Test time augmentations​

Specifies the test time augmentation(s) to apply during inference. Test time augmentations are applied when the model makes predictions on new data. The final prediction is an average of the predictions for all the augmented versions of an image.

Options

  • Horizontal Flip

    H2O Hydrogen Torch applies a horizontal flip as a test time augmentation.

  • Vertical Flip

    H2O Hydrogen Torch applies a vertical flip as a test time augmentation.

tip

This technique can improve the model accuracy.

Environment settings​

GPUs​

Specifies the list of GPUs H2O Hydrogen Torch can use for scoring. GPUs are listed by name, referring to their system ID (starting from 1).

Feedback