---
tags:
- anomaly-detection
- computer-vision
- patchcore
- defect-detection
- industrial
datasets:
- mvtec-ad
---

# 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']
```

## Author
[Chasston](https://huggingface.co/Chasston)