Codesota · Industrial Inspection · Anomaly Detection · NEU-DETTasks/Industrial Inspection/Anomaly Detection
Anomaly Detection · benchmark dataset · 2013 · EN

NEU Surface Defect Database.

1,800 grayscale images of hot-rolled steel strip with 6 defect types: rolled-in scale, patches, crazing, pitted surface, inclusion, scratches.

Paper Download datasetSubmit a result
§ 01 · Leaderboard

Best published scores.

1 result indexed across 1 metric. Shaded row marks current SOTA; ties broken by submission date.


Primary
map · higher is better
map· primary
1 row
#ModelOrgSubmittedPaper / codemap
01DefectDet (ResNet)OSSResearchDec 2025research78.40
Fig 2 · Rows sorted by score within each metric. Shaded row marks SOTA. Dates reflect model or paper release where available, otherwise the date Codesota accessed the source.
§ 03 · Progress

1 steps
of state of the art.

Each row below marks a model that broke the previous record on map. Intermediate submissions are kept in the leaderboard above; only SOTA-setting entries are re-listed here.

Higher scores win. Each subsequent entry improved upon the previous best.

SOTA line · map
  1. Dec 19, 2025DefectDet (ResNet)Research78.40
Fig 3 · SOTA-setting models only. 1 entries span Dec 2025 Dec 2025.
§ 06 · Contribute

Have a score that beats
this table?

Submit a checkpoint and a reproduction script. We will run it, publish the score, and — if it takes the top — annotate the step on the progress chart with your name.

Submit a result Read submission guide
What a submission needs
  • 01A public checkpoint or API endpoint
  • 02A reproduction script with frozen commit + seed
  • 03Declared evaluation environment (Python, deps)
  • 04One row per metric declared by this dataset
  • 05A contact so we can follow up on discrepancies