nhellyercreek/moon-cipher-detector

Moon Cipher Detector (Classifier)

CNN classifier for moon cipher glyph recognition: 27 classes (A–Z plus ~). Input: 128×128 grayscale glyph crop. Use with a detector for full image decoding.

Model card (right sidebar): This repo includes Safetensors (model.safetensors), so the Hub shows Model size, Tensor type (F32), and format. Best checkpoint from training (up to 150 epochs).

Model metadata

Model	Format	Size	Params	Tensor type
Classifier (model.safetensors)	Safetensors	51.98 MB	13,616,347 params	F32

Usage

1. Install

pip install torch torchvision huggingface_hub safetensors

2. Download from Hugging Face

from huggingface_hub import hf_hub_download

# Safetensors (preferred; used for Hub widget)
model_path = hf_hub_download(
    repo_id="nhellyercreek/moon-cipher-detector",
    filename="model.safetensors"
)
mappings_path = hf_hub_download(
    repo_id="nhellyercreek/moon-cipher-detector",
    filename="mappings.json"
)

3. Load and run

Use MoonClassifier from the Moon-Cipher-Detector repo (models/moon_classifier.py). Load Safetensors with safetensors.torch.load_file(model_path) and model.load_state_dict(state_dict), or use best_classifier.pth with torch.load(..., weights_only=True). Input: 128×128 grayscale glyph crops.

Config

• Classes: 27 (A–Z + ~)
• Architecture: Custom CNN (MoonClassifier), 128×128 input.
• Format: Safetensors + PyTorch .pth, tensor type: F32.
• Training: Best checkpoint (up to 150 epochs).

See config.json for machine-readable settings (params, size, format, training_epochs).