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Version: v1.5.0

Prediction settings: 3D image classification

Overview​

To score (predict) new data through the H2O Hydrogen Torch UI (with a built model), you need to specify certain settings refer as prediction settings (which are comprised of certain dataset, prediction, and environment settings similar to those utilized when creating an experiment). Below observe the prediction settings for a 3D image classification model.

General settings​

Experiment​

This setting defines the model (experiment) H2O Hydrogen Torch utilizes to score new data.

Prediction name​

This setting defines the name of the prediction.

Dataset settings​

Dataset​

This setting specifies the dataset to score.

Test dataframe​

This setting defines the file containing the test dataset that H2O Hydrogen Torch scores.

note
  • Image regression | 3D image regression | Image classification | 3D 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 | Graph node classification | Graph node regression
    • Defines a CSV or Parquet file containing the test dataset that H2O Hydrogen Torch utilizes for scoring.
    note

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

  • Image object detection | Image semantic segmentation | 3D image semantic segmentation | Image instance segmentation
    • Defines a Parquet file containing the test dataset that H2O Hydrogen Torch utilizes for scoring.
      :::

Data folder test​

Defines the folder location of the assets (for example, 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 loads from the Data folder test during scoring.

Prediction settings​

Metric​

This setting defines the evaluation metric in which H2O Hydrogen Torch evaluates the model's accuracy on generated predictions.

Batch Size Inference​

This setting defines the batch size of examples to utilize for inference.

note

Selecting 0 will set the Batch size inference to the same value used for the Batch size setting (utilized during training).

Probability threshold​

This setting determines the cutoff point for classifying instances into one of the classes.

In the context of machine learning, the probability threshold is a hyperparameter that is used when generating predictions from a model. It is specifically relevant in binary classification tasks where the goal is to classify instances into one of two classes.

When a machine learning model makes predictions, it assigns a probability or confidence score to each instance, indicating the likelihood of it belonging to a particular class. The probability threshold is a value that is set to determine the cutoff point for classifying instances into one of the classes.

By default, a probability threshold of 0.5 is often used, meaning that if the predicted probability of an instance belonging to a certain class is greater than or equal to 0.5, it is classified as belonging to that class. Conversely, if the predicted probability is less than 0.5, it is classified as belonging to the other class.

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.

Details
Options

Details
Image regression | 3D image regression | Image classification | 3D image classification | Image semantic segmentation | 3D image semantic segmentation | Image instance segmentation | Image metric learning

  • Horizontal flip
    • H2O Hydrogen Torch applies a horizontal flip as the test time augmentation(s).
  • Vertical flip
    • H2O Hydrogen Torch applies a vertical flip as the test time augmentation(s).

tip

This technique can improve the model accuracy.

Environment settings​

GPUs​

This setting 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). If no GPUs are selected, H2O Hydrogen Torch utilizes CPUs for model scoring.

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