Ir para o conteúdo
ModelListadosExterno
Baixar

TCGscanner Detector

This repository contains the current ONNX card-boundary detector used by the TCGscanner prototype.

TipoOtherVersãoRevision 524cffaCnota

Fonte do modelo

Tipo
Other
Tamanho do arquivo
11,7 MB
Versão
vRevision 524cffa
Atualizado
11 de out. de 2026
Downloads
0
Fonte
Hugging Face

Descrição da fonte

TCGscanner Card Detector

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.

Current Artifact

  • Size: 11.70 MB
  • SHA256: 8566d3c8556183c780eab0937f65d9862bdfc57697bfd3c33135218f28230f41
  • Class labels: card
  • Default confidence threshold in the app: 0.35

Training Summary

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:

ExperimentTest precisionTest recallTest mAP50Test mAP50-95
localization_only0.99571.00000.99500.9141
hybrid0.99921.00000.99500.9635

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.

Fontes

1 fonte
Hugging FacePrimárioVersão 524cffa578558b186088787252fbd61e1920ccc2
huggingface.co/Adrihp06/TCGscanner-detector/tree/524cffa578558b186088787252fbd61e1920ccc2

Verificado 26 de ago.

Artefatos de modelo

1 artefato

riftbound_regions.onnx

onnx · 11,7 MB · SHA-256 8566d3c85561…0f41 · Hugging Face

Baixar

Trechos de fonte

2 trechos

--- license: other library_name: ultralytics pipeline_tag: object-detection tags: - trading-card-games - object-detection - yolo - onnx - riftbound --- # TCGscanner Card Detector 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. ## Current Artifact - Size: `11.70 MB` - SHA256: `8566d3c8556183c780eab0937f65d9862bdfc57697bfd3c33135218f28230f41` - Class labels: `card` - Default confidence threshold in the app: `0.35` ## Training Summary 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 ```bash uv run python scripts/download_detector.py ``` The scanner loads the downloaded model from: ```text 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.

Source context: 0 downloads · 1 likes · Pipeline object-detection · Library ultralytics · Repo Adrihp06/TCGscanner-detector