MODEL_CARD.md

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.