Chasston/defect-vision-patchcore-bottle

DefectVision — PatchCore Anomaly Detection (bottle)

PatchCore implemented from scratch with PyTorch on MVTec AD.

Why PatchCore over YOLOv8?

Model	Approach	Key Metric
YOLOv8n	Supervised	mAP50 = 0.09
PatchCore	Unsupervised	AUROC = 0.9976

YOLOv8 struggles with MVTec AD because defect images are scarce. PatchCore trains on normal images only and generalizes to unseen defects.

Architecture

• Backbone: WideResNet50 (ImageNet pretrained)
• Layers: layer2 + layer3 → 1536-dim patch features
• Memory Bank: coreset 10% of train patches
• Scoring: max nearest-neighbor distance (k=9)

Metrics — bottle

• AUROC : 0.9976
• Best F1: 0.9920

Usage

import torch
from huggingface_hub import hf_hub_download

path = hf_hub_download(repo_id='Chasston/defect-vision-patchcore-bottle', filename='memory_bank.pt')
data = torch.load(path)
memory_bank = data['memory_bank']

Author

Chasston