RitualCNN

RitualCNN recognizes the seven ritual drawing classes used by the Rituals game.

Use the model

The ONNX file is RitualCNN.onnx. It accepts a grayscale image as a float32 tensor named input, with shape [batch, 1, 128, 128].

Before inference, convert the image to grayscale, resize it to 128 by 128 pixels, set pixels above 127 to 1 and all other pixels to 0. The model returns seven raw scores named output. The highest score is the predicted class.

The output order is: Acervus, Avarus, Flosculus, Mutus, Not, Pudor, Regium.

Example predictions

These are model inputs from the held-out validation set after preprocessing. When a class has no correct validation predictions, its row shows the highest-scoring example. The top softmax score is not a calibrated probability.

Input image	True label	Predicted label	Top softmax score	Result
	Acervus	Acervus	19%	Correct
	Avarus	Avarus	61%	Correct
	Flosculus	Flosculus	51%	Correct
	Mutus	Regium	39%	Incorrect
	Not	Not	65%	Correct
	Pudor	Pudor	27%	Correct
	Regium	Regium	52%	Correct

Confusion matrix

Rows show the true label. Columns show the predicted label. This matrix uses all 33 held-out validation images.

Training and evaluation

The model was trained on 163 labeled source image files. A stratified 80/20 split was made by class before augmentation. Augmentation runs on the fly for training images only: rotations up to 15 degrees and translations up to 12 pixels. The split contains 130 training images and 33 validation images. 0 byte-identical duplicate groups and 0 duplicate model-input groups were found.

Validation accuracy is 21/33 (63.6%); macro F1 is 59.8%. The validation set has 3 to 7 images per class. Early stopping selected epoch 47 using validation loss, so these results are for model selection rather than an independent final test.

A separate flat test folder had 6 files. 2 exact duplicates were excluded, leaving 4 images from 3 classes; 3/4 were correct. This set is too small and incomplete to estimate general performance.

The split is by file. The repository has no creator or session IDs for a grouped split, and drawings of the same ritual naturally look similar. Validation results are an estimate from a small dataset, not a guarantee of accuracy on new drawings. See metrics.json for the exact file lists, dataset fingerprint, per-class results, and ONNX compatibility checks.

Files

• RitualCNN.onnx — trained model, ONNX opset 11.
• config.json — input, output, and class information.
• metrics.json — training details and validation results.
• examples/ — validation input images shown above.
• confusion_matrix.png — validation confusion matrix.
• README.md — this model card.

The original drawings and the game source are in the Rituals GitHub repository. The exact training split and configuration for this model are listed in metrics.json.