---
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`.