accidentals/CLAUDE.md

CLAUDE.md

Project

Accidental classifier — a small CNN that identifies sharp, flat, and natural symbols in cropped images of engraved music scores. Part of a larger OMR (optical music recognition) pipeline.

Key facts

• Model: 3-block CNN (16→32→64 channels), ~115k params, 1×40×40 grayscale input, 3-class output
• Dataset: Crops extracted from PrIMuS via Verovio rendering. ~5k per class after subsampling.
• Performance: 100% on held-out test set (750 samples). The three classes are visually distinct enough that this is a solved problem.
• Seed: 42. All splits use this seed for reproducibility.
• Split ratios: 5% test, 10% val, rest train (stratified)
• Labels: ["flat", "natural", "sharp"] — alphabetical order, indices 0/1/2

Layout

• train.py — training pipeline, defines AccidentalCNN and all data loading
• eval.py — evaluates saved checkpoint on the test split (imports from train.py)
• extract_accidentals.py — multi-font crop extraction from PrIMuS MEI files
• extract_fast.py — faster single-font variant
• explore_dataset.py — dataset statistics
• segment_test.py — connected-component segmentation experiment
• model/accidental_classifier.pt — saved weights
• crops/{flat,natural,sharp}/ — extracted crops (gitignored)
• dataset/ — raw PrIMuS data (gitignored)

Conventions

• All Python scripts use sys.stdout.reconfigure(encoding="utf-8") for Windows compatibility
• Hardcoded paths under C:\src\accidentals\ — this is a local workstation project
• No requirements.txt; deps are PyTorch, torchvision, NumPy, Pillow, OpenCV, and optionally Verovio + cairosvg for extraction

Remote

• Gitea at git.xml-pipeline.org/dullfig/accidentals, branch main