Student-Teacher Feature Pyramid Matching for Anomaly Detection

Code

• github.com/openvinotoolkit/anomalib
  pytorch★ 5,507
• github.com/open-edge-platform/geti
  pytorch★ 467
• github.com/hcw-00/STPM_anomaly_detection
  pytorch★ 85
• github.com/xiahaifeng1995/STPM-Anomaly-Detection-Localization-master
  pytorch★ 46
• github.com/gdwang08/STFPM
  pytorch★ 31
• github.com/SimonThomine/RememberingNormality
  pytorch★ 23
• github.com/SimonThomine/DistillationAD
  pytorch★ 14
• github.com/CuberrChen/STFPM-Paddle
  paddle★ 3
• github.com/Rthete/STPM-mindspore
  mindspore★ 0
• github.com/kingcong/models/tree/main/stpm
  mindspore★ 0

Abstract

Anomaly detection is a challenging task and usually formulated as an one-class learning problem for the unexpectedness of anomalies. This paper proposes a simple yet powerful approach to this issue, which is implemented in the student-teacher framework for its advantages but substantially extends it in terms of both accuracy and efficiency. Given a strong model pre-trained on image classification as the teacher, we distill the knowledge into a single student network with the identical architecture to learn the distribution of anomaly-free images and this one-step transfer preserves the crucial clues as much as possible. Moreover, we integrate the multi-scale feature matching strategy into the framework, and this hierarchical feature matching enables the student network to receive a mixture of multi-level knowledge from the feature pyramid under better supervision, thus allowing to detect anomalies of various sizes. The difference between feature pyramids generated by the two networks serves as a scoring function indicating the probability of anomaly occurring. Due to such operations, our approach achieves accurate and fast pixel-level anomaly detection. Very competitive results are delivered on the MVTec anomaly detection dataset, superior to the state of the art ones.

Tasks

Benchmark Results

Reproductions