document-moire-detector-ONNX

document-moire-detector (ONNX)

This is an ONNX version of Jwalit/document-moire-detector. It was automatically converted and uploaded using this Hugging Face Space.

Usage with Transformers.js

See the pipeline documentation for image-classification: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.ImageClassificationPipeline

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Document Moiré Detection Model (V2)

A fine-tuned DeiT-small Vision Transformer for detecting moiré patterns in document images.

Model Description

Binary classifier: detects whether a document image contains moiré artifacts (common from screen photography, scanning, or screen captures).

Labels:

• clean (0): No moiré patterns
• moire (1): Moiré patterns detected

V1 → V2 Comparison

Note: V2 was evaluated on harder examples including subtle single-frequency moiré and localized moiré patterns that V1 never trained on. V2 achieves near-perfect recall (99.75%) — it catches virtually all moiré patterns including very subtle ones, at the cost of slightly lower precision.

Training Details

Moiré Generation Methods

1. Resize aliasing — downscale+upscale with NEAREST interpolation + pattern overlay
2. Pattern overlay — sinusoidal interference with per-channel color variation
3. Multi-frequency — 2-4 patterns at different frequencies + color displacement
4. Screen simulation — pixel grid + rotation + moiré overlay
5. Subtle moiré — very low strength single-frequency (hard examples)
6. Localized moiré — moiré in elliptical region with gaussian mask

Performance (Best Checkpoint)

Training Progression

Usage

from transformers import pipeline

classifier = pipeline("image-classification", model="Jwalit/document-moire-detector")
result = classifier("path/to/document.jpg")
print(result)
# [{'label': 'clean', 'score': 0.99}, {'label': 'moire', 'score': 0.01}]

Or manually:

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 = logits.argmax(-1).item()

print(model.config.id2label[predicted])  # '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 resized to 224×224; very subtle moiré in high-resolution images may be lost
• Higher recall than precision — may occasionally flag clean images as moiré (false positive rate ~1.5%)