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Classification

Classification#

Classification modules.

class anomalib.models.components.classification.FeatureScalingMethod(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)#

Bases: str, Enum

Determines how the feature embeddings are scaled.

class anomalib.models.components.classification.KDEClassifier(n_pca_components=16, feature_scaling_method=FeatureScalingMethod.SCALE, max_training_points=40000)#

Bases: Module

Classification module for KDE-based anomaly detection.

Parameters:

  • n_pca_components (int, optional) – Number of PCA components. Defaults to 16.

  • feature_scaling_method (FeatureScalingMethod, optional) – Scaling method applied to features before passing to KDE. Options are norm (normalize to unit vector length) and scale (scale to max length observed in training).

  • max_training_points (int, optional) – Maximum number of training points to fit the KDE model. Defaults to 40000.

compute_kde_scores(features, as_log_likelihood=False)#

Compute the KDE scores.

The scores calculated from the KDE model are converted to densities. If as_log_likelihood is set to true then

the log of the scores are calculated.

Parameters:

  • features (torch.Tensor) – Features to which the PCA model is fit.

  • as_log_likelihood (bool | None, optional) – If true, gets log likelihood scores. Defaults to False.

Returns:

Score

Return type:

(torch.Tensor)

static compute_probabilities(scores)#

Convert density scores to anomaly probabilities (see https://www.desmos.com/calculator/ifju7eesg7).

Parameters:

scores (torch.Tensor) – density of an image.

Return type:

Tensor

Returns:

probability that image with {density} is anomalous

fit(embeddings)#

Fit a kde model to embeddings.

Parameters:

embeddings (torch.Tensor) – Input embeddings to fit the model.

Return type:

bool

Returns:

Boolean confirming whether the training is successful.

forward(features)#

Make predictions on extracted features.

Return type:

Tensor

pre_process(feature_stack, max_length=None)#

Pre-process the CNN features.

Parameters:

  • feature_stack (torch.Tensor) – Features extracted from CNN

  • max_length (Tensor | None) – Used to unit normalize the feature_stack vector. If max_len is not provided, the length is calculated from the feature_stack. Defaults to None.

Returns:

Stacked features and length

Return type:

(Tuple)

predict(features)#

Predicts the probability that the features belong to the anomalous class.

Parameters:

features (torch.Tensor) – Feature from which the output probabilities are detected.

Return type:

Tensor

Returns:

Detection probabilities

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