defect-vision-patchcore-bottle – AI Model by Chasston | AlphaNeural AI

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Chasston/
defect-vision-patchcore-bottle

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anomaly-detection

computer-vision

defect-detection

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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

python

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']

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