AnomaVision

EfficientAD

EfficientAD is available as a native AnomaVision algorithm and uses the same training, model loading, detection, evaluation, and export workflow as PaDiM and PatchCore.

The implementation follows the EfficientAD student/teacher design: a frozen EfficientNet teacher provides normal feature targets, a lightweight student learns those features, and a compact autoencoder adds a global reconstruction signal. The original EfficientAD paper combines local teacher/student discrepancy with global reconstruction discrepancy for fast anomaly detection.

Select the algorithm

The existing top-level selector remains supported:

algorithm: efficientad

AnomaVision also accepts the native model selector:

model:
  name: efficientad

For existing projects, changing only algorithm is the safest option because the rest of the current configuration remains unchanged.

Training

anomavision train --config config.yml

Recommended starting values:

algorithm: efficientad
resize: [224, 224]
normalize: true
batch_size: 1
efficientad_model_size: s
efficientad_lr: 0.0001
efficientad_weight_decay: 0.00001
efficientad_epochs: 1
efficientad_pretrained_teacher: true

EfficientAD uses ImageNet preprocessing for the teacher, so normalize: true is required by the AnomaVision integration.

Detection and thresholding

Use the same command as the other algorithms:

anomavision detect --config config.yml --model model.onnx

EfficientAD and PaDiM do not produce scores on the same numerical scale. PaDiM’s threshold (for example 13.0) must not be reused for EfficientAD. EfficientAD normalizes its image score using the normal-training score mean and standard deviation, so its threshold is expressed in standard deviations from the normal score distribution.

The default configuration uses an independent EfficientAD threshold:

thresh_padim: 13.0
thresh_patchcore: 0.25
thresh_efficientad: 3.0

3.0 is a conservative 3-sigma starting point for deployment. It is intentionally separate from PaDiM and should be calibrated on the validation set for the target MVTec class if you need the closest possible classification agreement with an existing PaDiM deployment.

Export

The trained PyTorch model can be exported through the existing exporter:

anomavision export --config config.yml --model model.pt --format onnx

The ONNX graph contains the EfficientAD inference path, including the teacher, student, autoencoder, score calculation, and anomaly map generation.

Evaluation

anomavision eval --config config.yml --model model.pt --class_name bottle

EfficientAD has its own score distribution, so thresholds should be calibrated independently from PaDiM. The inference threshold is controlled by thresh_efficientad.

Model artifacts

Training produces the same primary artifact layout used by the other AnomaVision algorithms:

distributions/
└── efficientad/
    └── bottle/
        └── anomav_exp/
            ├── model.pt
            ├── model.pth
            └── config.yml

model.pt is the complete PyTorch model used by the normal AnomaVision inference backend. model.pth is a self-contained EfficientAD checkpoint artifact containing the model state and metadata.

Difference from PaDiM

EfficientAD is not numerically interchangeable with PaDiM. PaDiM models feature distributions with Gaussian statistics, while EfficientAD learns a student/teacher representation and reconstruction model. Consequently, scores, thresholds, training time, and localization patterns will differ. What remains intentionally identical is the AnomaVision contract: dataset input, CLI commands, artifact layout, fit, predict, model loading, and ONNX export.

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