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language:
- en
tags:
- anomaly-detection
- industrial-inspection
- computer-vision
- patchcore
- anomalib
- mvtec-ad-2
- defect-detection
- manufacturing
- pytorch
library_name: anomalib
pipeline_tag: image-classification
---
# Vial PatchCore — Industrial Anomaly Detection
A PatchCore-based industrial anomaly detection model trained for the **Vial** category of the **MVTec AD 2** dataset.
The model is designed to distinguish anomalous vial images from normal vial images and provides an anomaly map for visualizing regions that contribute to the detected anomaly.
## 🚀 Live Demo
Try the deployed model through the interactive Gradio application:
**[Industrial Vial Defect Detection — Hugging Face Space](https://huggingface.co/spaces/pranamjain/industrial-vial-defect-detection)**
Upload a vial image and receive:
- Normal / Defective prediction
- Anomaly score
- Anomaly heatmap
- Visual localization of suspicious regions
---
## 🧠 Model Overview
### Architecture
**PatchCore**
PatchCore is an industrial anomaly detection approach that represents image patches using deep visual features and compares them against a memory bank representing normal samples.
The model is trained to learn the visual characteristics of normal vial images. During inference, deviations from the learned normal representation produce higher anomaly scores.
### Model Details
| Property | Value |
|---|---|
| Architecture | PatchCore |
| Framework | PyTorch |
| Library | Anomalib |
| Dataset | MVTec AD 2 |
| Category | Vial |
| Input Resolution | 256 × 256 |
| Task | Industrial Anomaly Detection |
| Model File | `model.ckpt` |
---
## 📊 Evaluation
The trained Vial model was evaluated on the MVTec AD 2 public test set.
| Metric | Score |
|---|---:|
| Image AUROC | **0.7578** |
| Image F1 Score | **0.5315** |
| Pixel AUROC | **0.9142** |
| Pixel F1 Score | **0.1197** |
### Interpretation
**Image AUROC — 0.7578**
The model provides useful separation between normal and anomalous vial images at the image level.
**Image F1 Score — 0.5315**
The F1 score reflects the balance between precision and recall under the evaluation threshold used during the original evaluation.
**Pixel AUROC — 0.9142**
The high pixel-level AUROC indicates that the model can effectively rank anomalous regions relative to normal regions.
**Pixel F1 Score — 0.1197**
The lower pixel-level F1 indicates that precise defect segmentation remains challenging, even though the anomaly map provides useful localization information.
---
## 🎯 Deployment Threshold
During development, the default image-level threshold resulted in a relatively high number of false negatives for the Vial model.
An additional threshold analysis was performed using the MVTec AD 2 public test set.
The experimental deployment threshold selected for the interactive demo is:
```text
0.09 |