| --- |
| license: apache-2.0 |
| tags: |
| - document-scanner |
| - document-detection |
| - perspective-correction |
| - object-detection |
| - segmentation |
| - yolo |
| - sam |
| library_name: pagescan |
| pipeline_tag: object-detection |
| --- |
| |
| # pagescan weights |
|
|
| Pre-trained model weights used by [pagescan](https://github.com/7RPlus-GmbH/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: |
|
|
| ```bash |
| pip install pagescan |
| python -c "from pagescan import scan; scan('photo.jpg', 'out.pdf')" |
| ``` |
|
|
| To pin a specific revision or use a local cache: |
|
|
| ```python |
| 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/`](https://github.com/7RPlus-GmbH/pagescan/tree/main/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: |
|
|
| ```bibtex |
| @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](https://github.com/7RPlus-GmbH/pagescan). |
|
|
