Papers › SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable...
SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection
Blaž Rolih, Matic Fučka, Danijel Skočaj
The aim of surface defect detection is to identify and localise abnormal regions on the surfaces of captured objects, a task that's increasingly demanded across various industries. Current approaches frequently fail to fulfil the extensive demands of these industries, which encompass high performance, consistency, and fast operation, along with the capacity to leverage the entirety of the available training data. Addressing these gaps, we introduce SuperSimpleNet, an innovative discriminative model that evolved from SimpleNet. This advanced model significantly enhances its predecessor's training consistency, inference time, as well as detection performance. SuperSimpleNet operates in an unsupervised manner using only normal training images but also benefits from labelled abnormal training images when they are available. SuperSimpleNet achieves state-of-the-art results in both the supervised and the unsupervised settings, as demonstrated by experiments across four challenging benchmark datasets. Code: https://github.com/blaz-r/SuperSimpleNet .
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Detection | MVTec AD | SuperSimpleNet | Detection AUROC | 98.4 | #59 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | SuperSimpleNet | FPS | 107 (Tesla V100S) | #59 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | SuperSimpleNet | Segmentation AUPRO | 91.1 | #59 of 148 | Archive leaderboard | report |
| Anomaly Detection | VisA | SuperSimpleNet | Detection AUROC | 93.4 | #23 of 50 | Archive leaderboard | report |
| Anomaly Detection | VisA | SuperSimpleNet | Segmentation AUPRO | 87.4 | #23 of 50 | Archive leaderboard | report |
| Anomaly Detection | VisA | SuperSimpleNet | Segmentation AUPRO (until 30% FPR) | 87.4 | #23 of 50 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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