7rplus/pagescan-weights

pagescan weights

Pre-trained model weights used by pagescan, a privacy-first document scanner that turns phone photos into clean, deskewed, print-ready PDFs.

These weights power the default detection cascade:

1. YOLO11 detects a coarse axis-aligned bounding box around the document.
2. HQ-SAM ViT-B, prompted by that bbox, returns a precise binary mask.
3. The mask is fitted to a 4-corner quadrilateral for perspective correction.

Files

File	Size	Description
yolo_doc_v1.onnx	11 MB	Exported YOLO11 detector — used at inference time by pagescan.detector (no torch required).
yolo_doc_v1.pt	5.3 MB	Original PyTorch checkpoint — kept for re-export / fine-tuning.
sam_hq_vit_b.pth	362 MB	HQ-SAM ViT-B checkpoint — re-hosted copy (see attribution below).

Usage

These weights are downloaded automatically by pagescan on first use:

pip install pagescan
python -c "from pagescan import scan; scan('photo.jpg', 'out.pdf')"

To pin a specific revision or use a local cache:

from huggingface_hub import hf_hub_download

onnx_path = hf_hub_download(
    repo_id="7rplus/pagescan-weights",
    filename="yolo_doc_v1.onnx",
)

Training

yolo_doc_v1 was trained from yolo11n-obb.pt on a 1000-photo private corpus of phone-captured documents (Dec 2025) with oriented bounding box (OBB) labels. The training script lives in training/yolo/. A v2 trained on an extended, distribution-balanced corpus is in progress.

Attribution — HQ-SAM

sam_hq_vit_b.pth is not original work from this repository. It is a re-hosted copy of the ViT-B HQ-SAM checkpoint from the HQ-SAM authors, included here so pagescan installs ship with a single, stable weight source.

HQ-SAM — Segment Anything in High Quality Lei Ke, Mingqiao Ye, Martin Danelljan, Yifan Liu, Yu-Wing Tai, Chi-Keung Tang, Fisher Yu. NeurIPS 2023. Paper: https://arxiv.org/abs/2306.01567 Code & original weights: https://github.com/SysCV/sam-hq Original Hugging Face mirror: https://huggingface.co/lkeab/hq-sam

HQ-SAM is released under the Apache 2.0 license. We re-host the ViT-B checkpoint unchanged; all credit for the model itself belongs to the original authors. If you use pagescan in research that depends on the cascade's segmentation quality, please cite the HQ-SAM paper:

@inproceedings{ke2023segment,
  title     = {Segment Anything in High Quality},
  author    = {Ke, Lei and Ye, Mingqiao and Danelljan, Martin and Liu, Yifan and Tai, Yu-Wing and Tang, Chi-Keung and Yu, Fisher},
  booktitle = {Advances in Neural Information Processing Systems},
  year      = {2023}
}

License

• yolo_doc_v1.{pt,onnx} — released under Apache 2.0 by 7R+ GmbH.
• sam_hq_vit_b.pth — Apache 2.0, original authors (see above).

The pagescan package itself is MIT-licensed; see the main repository.