| --- |
| 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 |
|
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| A PatchCore-based industrial anomaly detection model trained for the **Vial** category of the **MVTec AD 2** dataset. |
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| 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. |
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| ## 🚀 Live Demo |
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| Try the deployed model through the interactive Gradio application: |
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| **[Industrial Vial Defect Detection — Hugging Face Space](https://huggingface.co/spaces/pranamjain/industrial-vial-defect-detection)** |
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| Upload a vial image and receive: |
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| - Normal / Defective prediction |
| - Anomaly score |
| - Anomaly heatmap |
| - Visual localization of suspicious regions |
|
|
| --- |
|
|
| ## 🧠 Model Overview |
|
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| ### Architecture |
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| **PatchCore** |
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| 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. |
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| 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. |
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| ### Model Details |
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|
| | 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` | |
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|
| --- |
|
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| ## 📊 Evaluation |
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| The trained Vial model was evaluated on the MVTec AD 2 public test set. |
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|
| | Metric | Score | |
| |---|---:| |
| | Image AUROC | **0.7578** | |
| | Image F1 Score | **0.5315** | |
| | Pixel AUROC | **0.9142** | |
| | Pixel F1 Score | **0.1197** | |
|
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| ### Interpretation |
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| **Image AUROC — 0.7578** |
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| The model provides useful separation between normal and anomalous vial images at the image level. |
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| **Image F1 Score — 0.5315** |
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| The F1 score reflects the balance between precision and recall under the evaluation threshold used during the original evaluation. |
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| **Pixel AUROC — 0.9142** |
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| The high pixel-level AUROC indicates that the model can effectively rank anomalous regions relative to normal regions. |
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| **Pixel F1 Score — 0.1197** |
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| The lower pixel-level F1 indicates that precise defect segmentation remains challenging, even though the anomaly map provides useful localization information. |
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| --- |
|
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| ## 🎯 Deployment Threshold |
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| During development, the default image-level threshold resulted in a relatively high number of false negatives for the Vial model. |
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| An additional threshold analysis was performed using the MVTec AD 2 public test set. |
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| The experimental deployment threshold selected for the interactive demo is: |
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| ```text |
| 0.09 |
