license: unlicense
library_name: onnx
pipeline_tag: image-classification
tags:
- onnx
- image-classification
- ritual-recognition
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.