This repository contains the current ONNX card-boundary detector used by the TCGscanner prototype.
The model is a single-class YOLO detector. Its task is to localize the physical trading card in a camera frame or photograph. Card identification is handled separately by SigLIP 2 visual embeddings and LanceDB vector search in the application repository.
Files
riftbound_regions.onnx: exported ONNX detector expected at models/riftbound_regions.onnx by the scanner.
The detector was trained on a universal TCG detection dataset that combines localized card examples from multiple trading card domains. The objective is to learn generic card geometry rather than the visual identity of a specific game.
The selected hybrid experiment used corners, polygons, and isolated full-card samples. The June 27, 2026 audit run reported:
Experiment
Test precision
Test recall
Test mAP50
Test mAP50-95
localization_only
0.9957
1.0000
0.9950
0.9141
hybrid
0.9992
1.0000
0.9950
0.9635
The selected hybrid run was stopped manually during epoch 42 after the validation curve had stabilized for the scanner use case. Its best validation checkpoint was epoch 40 with mAP50=0.9942 and mAP50-95=0.9628.
Usage
uv run python scripts/download_detector.py
The scanner loads the downloaded model from:
models/riftbound_regions.onnx
Limitations
This detector only localizes the card boundary.
It does not identify the card.
The current dataset still needs more real-world Riftbound photographs.
Pricing and collection features are outside this model repository.
