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
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
- Resize aliasing — downscale+upscale with NEAREST interpolation + pattern overlay
- Pattern overlay — sinusoidal interference with per-channel color variation
- Multi-frequency — 2-4 patterns at different frequencies + color displacement
- Screen simulation — pixel grid + rotation + moiré overlay
- Subtle moiré — very low strength single-frequency (hard examples)
- 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
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%)
