metadata
language:
- en
- multilingual
license: mit
tags:
- CNN
- ONNX
- onnxruntime
- Balatro
datasets:
- proj-airi/games-balatro-2024-entities-detection
Balatro (2024, game) card-corner classifier
This model classifies cropped Balatro card corners into rank and suit labels. It is a small shared-backbone CNN, not an Ultralytics YOLO classifier.
ONNX Interface
The exported ONNX model is available at:
onnx/model.onnx
Input:
images: float32[batch, 3, 64, 64]
Outputs:
rank_logits: float32[batch, 13]
suit_logits: float32[batch, 4]
Rank label order:
A, 2, 3, 4, 5, 6, 7, 8, 9, 10, J, Q, K
Suit label order:
spades, hearts, clubs, diamonds
Training Run
The recovered local training run is stored at:
runs/classify/card-corner-cnn/mps-aug-weighted-seed2-latest
The run includes args.json, metrics.json, PyTorch checkpoints, and a copy
of the exported ONNX model under onnx/model.onnx.
Best validation metrics from the recovered run:
rank_accuracy: 0.8387096774193549
suit_accuracy: 1.0
exact_accuracy: 0.8387096774193549
Export
Use the export utility to regenerate the ONNX artifact from a checkpoint:
pixi run python cli/export-card-corner-classifier-onnx.py \
--checkpoint runs/classify/card-corner-cnn/mps-aug-weighted-seed2-latest/card-corner-classifier-best.pt \
--output models/games-balatro-2024-card-corner-classifier/onnx/model.onnx
Publish
After authenticating with Hugging Face, upload this model directory as the model repository root:
pixi run python - <<'PY'
from huggingface_hub import HfApi
repo_id = 'proj-airi/games-balatro-2024-card-corner-classifier'
folder = 'models/games-balatro-2024-card-corner-classifier'
api = HfApi()
api.create_repo(repo_id=repo_id, repo_type='model', exist_ok=True)
api.upload_folder(
repo_id=repo_id,
repo_type='model',
folder_path=folder,
commit_message='Add Balatro card-corner CNN ONNX model',
)
PY