SuperSimpleNet

SuperSimpleNet#

Architecture#

SuperSimpleNet.

ICPR 2024 - SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection.

JIMS 2025 - No Label Left Behind: A Unified Surface Defect Detection Model for all Supervision Regimes

This module implements the SuperSimpleNet model for surface defect / anomaly detection. SuperSimpleNet is a simple yet strong discriminative model consisting of a pretrained feature extractor with upscaling, feature adaptor, train-time feature-level synthetic anomaly generation module, and segmentation-detection module.

Using the adapted features, the model predicts an anomaly map via the segmentation head and an anomaly score using the classification head. It delivers strong performance while maintaining fast inference.

Example

>>> from anomalib.data import MVTecAD
>>> from anomalib.models import Supersimplenet
>>> from anomalib.engine import Engine

>>> datamodule = MVTecAD()
>>> model = Supersimplenet()
>>> engine = Engine()

>>> engine.fit(model, datamodule=datamodule)
>>> predictions = engine.predict(model, datamodule=datamodule)

Paper:

Original: SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection. URL: https://arxiv.org/pdf/2408.03143

Extension: No label left behind: a unified surface defect detection model for all supervision regimes URL: https://link.springer.com/article/10.1007/s10845-025-02680-8

Notes

This implementation supports both unsupervised and supervised setting, but Anomalib currently supports only unsupervised learning.

SuperSimpleNet

Contents

SuperSimpleNet#

Architecture#