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README.md

@@ -6,28 +6,18 @@ tags:
   - cipher
   - moon-cipher
   - pytorch
-  - crnn
   - cnn
+  - image-classification
 ---
 
-# Moon Cipher Detector
+# Moon Cipher Detector (Classifier)
 
-Deep learning models for detecting and decoding **moon cipher** glyphs from images (A–Z plus `~`).
-
-## Model variants
-
-| File | Description |
-|------|-------------|
-| `best_end_to_end.pth` | End-to-end CRNN: full image → decoded text (recommended for full-page decoding). |
-| `best_classifier.pth` | CNN classifier for single glyphs (used with a separate detector in two-stage decoding). |
-
-Use **end-to-end** for full image decoding; use **classifier** if you already have cropped glyphs or use the two-stage pipeline (detect then classify).
+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 metadata
 
 | Model | Format | Size | Params | Tensor type |
 |-------|--------|------|--------|--------------|
-| End-to-end (`best_end_to_end.pth`) | PyTorch .pth (state_dict) | 30.0 MB | 7,856,348 params | F32 |
 | Classifier (`best_classifier.pth`) | PyTorch .pth (state_dict) | 51.98 MB | 13,616,347 params | F32 |
 
 Weights are saved as **PyTorch state dict** (`.pth`), not Safetensors. Load with `torch.load(..., weights_only=True)`.
@@ -45,10 +35,9 @@ pip install torch torchvision huggingface_hub
 ```python
 from huggingface_hub import hf_hub_download
 
-# End-to-end model (full image → text)
 model_path = hf_hub_download(
     repo_id="nhellyercreek/moon-cipher-detector",
-    filename="best_end_to_end.pth"
+    filename="best_classifier.pth"
 )
 mappings_path = hf_hub_download(
     repo_id="nhellyercreek/moon-cipher-detector",
@@ -56,35 +45,14 @@ mappings_path = hf_hub_download(
 )
 ```
 
-### 3. Load and decode (end-to-end)
-
-Use the same architecture as in this repo’s `models/end_to_end_decoder.py`:
-
-```python
-import torch
-import json
-from pathlib import Path
-
-# Add the Moon-Cipher-Detector repo to path, then:
-# from models.end_to_end_decoder import EndToEndMoonCipherDecoder
-
-# decoder = EndToEndMoonCipherDecoder(
-#     model_path=model_path,
-#     model_type="crnn",
-#     mappings_path=mappings_path,
-#     device="cuda" if torch.cuda.is_available() else "cpu"
-# )
-# result = decoder.decode_with_confidence(your_grayscale_image)
-# print(result["text"])
-```
+### 3. Load and run
 
-For the **classifier**, use `models/moon_classifier.MoonClassifier` and load `best_classifier.pth`; see the project’s `models/sequence_decoder.py` and `backend/main.py` for the full pipeline.
+Use `MoonClassifier` from the Moon-Cipher-Detector repo (`models/moon_classifier.py`). Load with `torch.load(model_path, weights_only=True)` and pass 128×128 grayscale glyph crops.
 
 ## Config
 
 - **Classes**: 27 (A–Z + `~`)
-- **End-to-end**: Deep CRNN (CNN + bidirectional LSTM, CTC).
-- **Classifier**: Custom CNN, 128×128 input, 27 classes.
+- **Architecture**: Custom CNN (MoonClassifier), 128×128 input.
 - **Weight format**: PyTorch `.pth` (state_dict), **tensor type**: F32.
 
-See `config.json` in this repo for machine-readable settings (params, size, format).
+See `config.json` for machine-readable settings (params, size, format).