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Jwalit/document-moire-detector

sourceHugging Faceapache-2.0updated 5mo agoView on Hugging Face

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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

	V1 (DeiT-tiny)	V2 (DeiT-small)
Parameters	5.5M	22M
Training samples	6,000	8,000
Moiré methods	4	6 (+subtle, +localized)
Label smoothing	—	0.05
Accuracy	99.5%	99.1%
F1 Score	0.995	0.991
Precision	99.3%	98.5%
Recall	99.7%	99.8%

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

Parameter	Value
Base model	`facebook/deit-small-patch16-224`
Parameters	22M
Training samples	8,000 (4,000 clean + 4,000 moiré)
Eval samples	800 (400 clean + 400 moiré)
Epochs	5
Learning rate	3e-5 (cosine schedule)
Effective batch size	64
Label smoothing	0.05
Warmup steps	60
Best checkpoint	Epoch 2 (by F1)

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)

Metric	Value
Accuracy	99.12%
F1 Score	0.9913
Precision	98.52%
Recall	99.75%
Eval Loss	0.170

Training Progression

Epoch	Eval Loss	Accuracy	F1	Precision	Recall
1	0.173	99.0%	0.990	99.3%	98.8%
2	0.177	99.1%	0.991	99.0%	99.3%
3	0.154	99.1%	0.991	99.3%	99.0%
4	0.170	99.1%	0.991	98.5%	99.8%
5	0.168	99.0%	0.990	98.3%	99.8%

Usage

python

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:

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 = 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%)