---
license: mit
library_name: pytorch
pipeline_tag: image-classification
tags:
  - blur-detection
  - image-quality
  - mobilenet
  - magika
  - laplacian
metrics:
  - f1
  - accuracy
  - precision
  - recall
  - roc_auc
model-index:
  - name: MagikaDocumentFromPixel — Lightweight Blur Detector
    results:
      - task:
          type: image-classification
          name: Blur Detection (sharp / blurred)
        dataset:
          type: gopro-large
          name: GoPro Large (test split)
        metrics:
          - type: f1
            value: 0.9803
          - type: accuracy
            value: 0.9806
          - type: precision
            value: 0.9981
          - type: recall
            value: 0.9631
          - type: roc_auc
            value: 0.9989
---

# MagikaDocumentFromPixel — Lightweight Blur Detector

A **Magika-inspired image quality gate** that classifies images as `sharp`, `blurred`, or `uncertain` in a few milliseconds on CPU. Built to sit at the front of vision pipelines so expensive downstream models (OCR, detection, classification, VLMs) never waste compute on unusable input.

GitHub repo (training code, Dockerfile, full README): **[bradduy/MagikaDocumentFromPixel](https://github.com/bradduy/MagikaDocumentFromPixel)**

## Result on GoPro Large test split

| Metric | Value |
|---|---|
| F1 | **0.9803** |
| Accuracy | 0.9806 |
| Precision | 0.9981 |
| Recall | 0.9631 |
| AUC | 0.9989 |
| Model size | 17 MB |
| Inference latency | ~17 ms / image (CPU, single-scale) |

## Recipe

- **Backbone**: MobileNetV3-Large, ImageNet-pretrained, 2-class softmax head (~3.3M parameters).
- **Frequency-domain auxiliary channel (Freq-Aux)**: a per-image-standardized Laplacian magnitude map is concatenated to the RGB tensor as a **4th input channel**. The first conv is expanded from 3→4 channels (pretrained RGB weights preserved; the new slice is initialized from the mean of the RGB kernels). The Laplacian gives the network an explicit, scale-invariant edge-energy cue.
- **Training**: 384×384 input, AdamW lr=1e-4, CosineAnnealing, CrossEntropy, 25 epochs, medium augmentation, mixed-precision, GoPro Large with `blur_gamma` extra positives.
- **Inference**: 5-scale multi-scale TTA at 256, 320, 384, 448, 512.
- **Routing**: return `sharp` or `blurred` when max softmax ≥ 0.60, otherwise return `uncertain`.

## Files

- `best.pt` — PyTorch state dict for the `FreqAuxModel(MobileNetV3-Large)` 4-channel-input model.

## Usage

Clone the GitHub repo for the inference scripts, then load this checkpoint.

```bash
git clone https://github.com/bradduy/MagikaDocumentFromPixel.git
cd MagikaDocumentFromPixel
pip install -r blur_detector/requirements.txt

# Download this checkpoint
pip install huggingface_hub
python -c "from huggingface_hub import hf_hub_download; \
  hf_hub_download('bradduy/MagikaDocumentFromPixel', 'best.pt', \
                  local_dir='blur_detector/outputs/checkpoints/champion')"

# Run inference
python blur_detector/scripts/predict.py \
  --checkpoint blur_detector/outputs/checkpoints/champion/best.pt --freq_aux \
  path/to/image.jpg
```

Or in Python:

```python
from blur_detector.src.models.blur_detector import build_model
from blur_detector.src.datasets.freq_aux import FreqAuxModel
from blur_detector.src.inference.predictor import BlurPredictor
import torch

backbone = build_model("mobilenet_v3_large", pretrained=False, in_channels=4)
model = FreqAuxModel(backbone)
model.load_state_dict(torch.load("best.pt"))

predictor = BlurPredictor(model, image_size=[256, 320, 384, 448, 512])
pred = predictor.predict("receipt.jpg")
print(pred.label, pred.confidence)
```

## Intended use

- Pre-check before OCR / VLM / paid vision API calls.
- Upload-time quality filter ("please retake the photo").
- Dataset curation for ML programs.
- Edge / on-device inference (single-scale 384px → ONNX → mobile/browser).

## Limitations

- Trained on GoPro motion blur. Domain-shift retraining is recommended for defocus blur, low-light, scanner skew, or compression artifacts.
- Threshold (0.60) is a product-level knob — sweep on a small hand-labeled slice of your traffic to set the precision/recall trade-off.

## Citation

If you use this work in research or production, please cite:

> Duy, Tran Thanh (2026). *Edges Before Embeddings: A Confidence-Aware Blur Gate for Vision-Language Pipelines.* Zenodo. https://doi.org/10.5281/zenodo.19765336

BibTeX:

```bibtex
@misc{duy2026edges,
  author       = {Duy, Tran Thanh},
  title        = {Edges Before Embeddings: A Confidence-Aware Blur Gate for Vision-Language Pipelines},
  year         = {2026},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.19765336},
  url          = {https://doi.org/10.5281/zenodo.19765336}
}
```

## License

MIT — see [LICENSE](https://github.com/bradduy/MagikaDocumentFromPixel/blob/main/LICENSE). Copyright © 2026 Duy Tran Thanh (Brad Duy).

## Author

**Duy Tran Thanh (Brad Duy)** — Sr. Applied AI Engineer

- GitHub: [@bradduy](https://github.com/bradduy)
- Hugging Face: [@bradduy](https://huggingface.co/bradduy)