DFM

DFM#

Deep Feature Modeling (DFM) for anomaly detection.

This module provides a PyTorch Lightning implementation of the DFM model for anomaly detection. The model extracts deep features from images using a pre-trained CNN backbone and fits a Gaussian model on these features to detect anomalies.

Paper: https://arxiv.org/abs/1909.11786

Example

>>> from anomalib.models.image import Dfm
>>> model = Dfm(
...     backbone="resnet50",
...     layer="layer3",
...     pre_trained=True
... )

Notes

The model uses a pre-trained backbone to extract features and fits a PCA transformation followed by a Gaussian model during training. No gradient updates are performed on the backbone.

See also

anomalib.models.image.dfm.torch_model.DFMModel:

PyTorch implementation of the DFM model.

class anomalib.models.image.dfm.lightning_model.Dfm(backbone='resnet50', layer='layer3', pre_trained=True, pooling_kernel_size=4, pca_level=0.97, score_type='fre', pre_processor=True, post_processor=True, evaluator=True, visualizer=True)#

Bases: MemoryBankMixin, AnomalibModule

DFM Lightning Module.

Parameters:

  • backbone (str) – Name of the backbone CNN network. Defaults to "resnet50".

  • layer (str) – Name of the layer to extract features from the backbone. Defaults to "layer3".

  • pre_trained (bool) – Whether to use a pre-trained backbone. Defaults to True.

  • pooling_kernel_size (int) – Kernel size for pooling features. Defaults to 4.

  • pca_level (float) – Ratio of variance to preserve in PCA. Must be between 0 and 1. Defaults to 0.97.

  • score_type (str) – Type of anomaly score to compute. Options are "fre" (feature reconstruction error) or "nll" (negative log-likelihood). Defaults to "fre".

  • pre_processor (PreProcessor | bool) – Pre-processor to use. If True, uses the default pre-processor. If False, no pre-processing is performed. Defaults to True.

  • post_processor (PostProcessor | bool) – Post-processor to use. If True, uses the default post-processor. If False, no post-processing is performed. Defaults to True.

  • evaluator (Evaluator | bool) – Evaluator to use. If True, uses the default evaluator. If False, no evaluation is performed. Defaults to True.

  • visualizer (Visualizer | bool) – Visualizer to use. If True, uses the default visualizer. If False, no visualization is performed. Defaults to True.

static configure_optimizers()#

Configure optimizers for training.

Returns:

DFM doesn’t require optimization.

Return type:

None

fit()#

Fit the PCA transformation and Gaussian model to the embeddings.

The method aggregates embeddings collected during training and fits both the PCA transformation and Gaussian model used for scoring.

Return type:

None

property learning_type: LearningType#

Get the learning type of the model.

Returns:

The model uses one-class learning.

Return type:

LearningType

property trainer_arguments: dict[str, Any]#

Get DFM-specific trainer arguments.

Returns:

Trainer arguments

  • gradient_clip_val: 0 (no gradient clipping needed)

  • max_epochs: 1 (single pass through training data)

  • num_sanity_val_steps: 0 (skip validation sanity checks)

  • devices: 1 (only single gpu supported)

Return type:

dict[str, Any]

training_step(batch, *args, **kwargs)#

Extract features from the input batch during training.

Parameters:

  • batch (Batch) – Input batch containing images.

  • *args – Additional positional arguments (unused).

  • **kwargs – Additional keyword arguments (unused).

Returns:

Dummy loss tensor for compatibility.

Return type:

None

validation_step(batch, *args, **kwargs)#

Compute predictions for the input batch during validation.

Parameters:

  • batch (Batch) – Input batch containing images.

  • *args – Additional positional arguments (unused).

  • **kwargs – Additional keyword arguments (unused).

Returns:

Dictionary containing anomaly scores and maps.

Return type:

Union[Tensor, Mapping[str, Any], None]

PyTorch model for Deep Feature Modeling (DFM).

This module provides a PyTorch implementation of the DFM model for anomaly detection. The model extracts deep features from images using a pre-trained CNN backbone and fits a Gaussian model on these features to detect anomalies.

Example

>>> import torch
>>> from anomalib.models.image.dfm.torch_model import DFMModel
>>> model = DFMModel(
...     backbone="resnet18",
...     layer="layer4",
...     pre_trained=True
... )
>>> batch = torch.randn(32, 3, 224, 224)
>>> features = model(batch)  # Returns features during training
>>> predictions = model(batch)  # Returns scores during inference

Notes

The model uses a pre-trained backbone to extract features and fits a PCA transformation followed by a Gaussian model during training. No gradient updates are performed on the backbone.

class anomalib.models.image.dfm.torch_model.DFMModel(backbone, layer, pre_trained=True, pooling_kernel_size=4, n_comps=0.97, score_type='fre')#

Bases: Module

Deep Feature Modeling (DFM) model for anomaly detection.

The model extracts deep features from images using a pre-trained CNN backbone and fits a Gaussian model on these features to detect anomalies.

Parameters:

  • backbone (str) – Pre-trained model backbone from timm.

  • layer (str) – Layer from which to extract features.

  • pre_trained (bool) – Whether to use pre-trained backbone. Defaults to True.

  • pooling_kernel_size (int) – Kernel size to pool features. Defaults to 4.

  • n_comps (float) – Ratio for PCA components calculation. Defaults to 0.97.

  • score_type (str) – Scoring type - fre or nll. Defaults to fre. Segmentation supported with fre only. For nll, set task to classification.

Example

>>> model = DFMModel(
...     backbone="resnet18",
...     layer="layer4",
...     pre_trained=True
... )
>>> input_tensor = torch.randn(32, 3, 224, 224)
>>> output = model(input_tensor)
fit()#

Fit PCA and Gaussian model to dataset.

Return type:

None

forward(batch)#

Compute anomaly predictions from input images.

Parameters:

batch (Tensor) – Input images with shape (batch_size, channels, height, width).

Returns:

Model predictions. During

training returns features tensor. During inference returns InferenceBatch with prediction scores and anomaly maps.

Return type:

Tensor | InferenceBatch

get_features(batch)#

Extract features from the pretrained network.

Parameters:

batch (Tensor) – Input images with shape (batch_size, channels, height, width).

Return type:

tuple[Tensor, Size]

Returns:

tuple of (features, feature_shapes).

score(features, feature_shapes)#

Compute anomaly scores.

Scores are either PCA-based feature reconstruction error (FRE) scores or Gaussian density-based NLL scores.

Parameters:

  • features (Tensor) – Features for scoring with shape (n_samples, n_features).

  • feature_shapes (tuple) – Shape of features tensor for anomaly map.

Returns:

Tuple containing

(scores, anomaly_maps). Anomaly maps are None for NLL scoring.

Return type:

Tensor

class anomalib.models.image.dfm.torch_model.SingleClassGaussian#

Bases: DynamicBufferMixin

Model Gaussian distribution over a set of points.

This class fits a single Gaussian distribution to a set of feature vectors and computes likelihood scores for new samples.

Example

>>> gaussian = SingleClassGaussian()
>>> features = torch.randn(128, 100)  # 100 samples of 128 dimensions
>>> gaussian.fit(features)
>>> scores = gaussian.score_samples(features)
fit(dataset)#

Fit a Gaussian model to dataset X.

Covariance matrix is not calculated directly using C = X.X^T. Instead, it is represented using SVD of X: X = U.S.V^T. Hence, C = U.S^2.U^T. This simplifies the calculation of the log-likelihood without requiring full matrix inversion.

Parameters:

dataset (Tensor) – Input dataset to fit the model with shape (n_features, n_samples).

Return type:

None

forward(dataset)#

Fit the model to the input dataset.

Transforms the input dataset based on singular values calculated earlier.

Parameters:

dataset (Tensor) – Input dataset with shape (n_features, n_samples).

Return type:

None

score_samples(features)#

Compute the negative log likelihood (NLL) scores.

Parameters:

features (Tensor) – Semantic features on which density modeling is performed with shape (n_samples, n_features).

Returns:

NLL scores for each sample.

Return type:

Tensor