# ParcelGuard Model Card

## Model purpose
Experimental reference-guided anomaly scoring for product images.

## Inputs
- One query product image.
- One or more reference images for the same item.

## Outputs
- P95 anomaly score.
- Binary label from a provisional fixed threshold.
- Spatial anomaly map and overlay.

## Architecture
ResNet18 pretrained on ImageNet, using layer2 spatial features.
Cosine distances are computed at corresponding feature-map locations;
the minimum distance across references is used at each location.

## Evaluation
Current sample: 100 queries, 37 defective and 63 normal.
Exploratory classifier cross-validation: balanced accuracy 0.594, ROC-AUC 0.607.
Exploratory P95-only ROC-AUC: approximately 0.684.
These are exploratory results, not a held-out benchmark.

## Failure example
One defective sample had a P95 score of approximately 0.654 and was classified
as NORMAL using the provisional threshold of 0.70.

## Limitations and risks
The method is sensitive to view changes, alignment, packaging, texture and lighting.
The heatmap is not verified pixel-level defect localization.
The threshold was selected on the exploratory sample and requires independent validation.

## Intended use
Research and experimentation only. Do not use as an autonomous quality-control
decision system without rigorous validation on representative held-out data.

## Data and licensing
The project uses the Amazon Kaputt dataset. Review its CC BY-NC-ND 4.0 terms.
No original dataset images are intentionally included in this repository.