README.md

metadata

title: Industrial Anomaly Detection — PatchCore Demo
emoji: 🔍
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 6.17.3
app_file: app.py
pinned: false

Industrial Anomaly Detection with PatchCore

Detects surface defects in industrial images using PatchCore (Roth et al., CVPR 2022) — a training-free method that builds a memory bank of normal patch features and flags anomalies as regions far from that bank.

Supported categories (15): bottle, cable, capsule, carpet, grid, hazelnut, leather, metal_nut, pill, screw, tile, toothbrush, transistor, wood, zipper

How it works: Upload any product image → nearest-neighbour distance from patch features to the memory bank → pixel-level anomaly heatmap + image-level score and pass/fail verdict.

Backbone: WideResNet-101-2 (ImageNet pretrained, layers 2 + 3 concatenated → 1536-dim descriptors)

Note: Thresholds are not stored in metrics.json (only AUROC/PRO metrics are saved). The verdict currently uses a default threshold of 0.5 — calibrate per category from the score distributions in results/{category}/score_distribution.png.