Jwalit
/

document-moire-detector

@@ -1,199 +1,93 @@
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 ---
+license: apache-2.0
+tags:
+- image-classification
+- moire-detection
+- document-analysis
+- document-quality
+- vision
+datasets:
+- hf-tuner/rvl-cdip-document-classification
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+pipeline_tag: image-classification
 ---
+# Document Moiré Detection Model
+A fine-tuned **DeiT-tiny** (Vision Transformer) model for detecting moiré patterns in document images.
+## Model Description
+This model performs binary classification to detect whether a document image contains moiré patterns —
+visual artifacts that commonly occur when:
+- Photographing a screen displaying a document
+- Scanning documents at certain resolutions
+- Screen-capturing documents with resolution mismatches
+**Labels:**
+- `clean` (0): No moiré patterns detected
+- `moire` (1): Moiré patterns detected
+## Training
+- **Base model:** `facebook/deit-tiny-patch16-224` (5.5M parameters)
+- **Training data:** 6,000 samples (3,000 clean + 3,000 synthetic moiré)
+- **Source images:** [rvl-cdip document classification dataset](https://huggingface.co/datasets/hf-tuner/rvl-cdip-document-classification)
+- **Moiré generation:** 4 synthetic methods:
+  1. Resize aliasing (screen-camera simulation)
+  2. Frequency-domain pattern overlay
+  3. Multi-frequency band interference with color fringing
+  4. Screen pixel grid + capture simulation
+- **Epochs:** 5
+- **Learning rate:** 5e-5 (cosine schedule)
+- **Effective batch size:** 64
+## Performance
+| Metric    | Validation | Test (held-out) |
+|-----------|-----------|-----------------|
+| Accuracy  | 99.8%     | 99.5% |
+| F1 Score  | 0.998     | 0.995 |
+| Precision | 100%      | 99.3% |
+| Recall    | 99.6%     | 99.7% |
+## Usage
+```python
+from transformers import pipeline
+classifier = pipeline("image-classification", model="Jwalit/document-moire-detector")
+result = classifier("path/to/document_image.jpg")
+print(result)
+# [{'label': 'clean', 'score': 0.99}, {'label': 'moire', 'score': 0.01}]
+```
+Or manually:
+```python
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import torch
+processor = AutoImageProcessor.from_pretrained("Jwalit/document-moire-detector")
+model = AutoModelForImageClassification.from_pretrained("Jwalit/document-moire-detector")
+image = Image.open("document.jpg")
+inputs = processor(image, return_tensors="pt")
+with torch.no_grad():
+    logits = model(**inputs).logits
+    predicted_class = logits.argmax(-1).item()
+print(model.config.id2label[predicted_class])  # 'clean' or 'moire'
+```
+## Limitations
+- Trained on synthetic moiré patterns — may not capture all real-world moiré variations
+- Optimized for document images; performance on natural scene images may vary
+- Input images are resized to 224×224; very subtle moiré in high-resolution images may be lost