Object Detection
ultralytics
Czech
cropilot
yolo
document-layout-analysis
document-cropping
digitization
Instructions to use cropilot-community/default_crop_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use cropilot-community/default_crop_v1 with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("cropilot-community/default_crop_v1") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
| license: agpl-3.0 | |
| language: | |
| - cs | |
| library_name: ultralytics | |
| pipeline_tag: object-detection | |
| tags: | |
| - cropilot | |
| - yolo | |
| - document-layout-analysis | |
| - document-cropping | |
| - digitization | |
| # Default Crop v1 | |
| `default_crop_v1.pt` is a Cropilot model for detecting page crop regions in scanned printed documents. It is the general-purpose default crop model, trained on a mixed corpus of books, magazines, and newspapers. | |
| It is published here as a reusable model artifact for use in the [Cropilot](https://github.com/moravianlibrary/cropilot) document-cropping ecosystem. | |
| ## License | |
| Released under the GNU Affero General Public License v3.0 (`AGPL-3.0`). | |
| ## Model File | |
| - File: `default_crop_v1.pt` | |
| - Base model: YOLO11s | |
| - Task: object-detection (page crop region) | |
| ## Intended Use | |
| This model is intended for semi-automated processing in a Cropilot workflow: | |
| 1. Compressed JPEGs are created from original scans (e.g. TIFF). | |
| 2. Cropilot runs AI detection using this model. | |
| 3. A human operator reviews and corrects the result in the Cropilot editor. | |
| 4. Final crops are applied to the original scans by the production tooling. | |
| The model handles both **single-page** and **double-page (spread)** scans, making it a good general default when the document type is mixed or unknown. | |
| ## Recommended Integration | |
| Typical Cropilot settings for this model: | |
| - Crop model: `default_crop` | |
| - Rotation model: `text` | |
| ## Training Data | |
| The model was trained on a mixed corpus of digitized printed documents covering both single-page and double-page layouts. Approximate composition: | |
| - ~80% books | |
| - ~15% magazines / periodicals | |
| - ~5% newspapers | |
| The material spans roughly **1950–2010**. | |
| ## Limitations | |
| - Performance is strongest on recent books; older material, heavily illustrated layouts, or document types outside the training mix may need validation or further fine-tuning. | |
| - Book covers are detected poorly. | |
| ## Example Download | |
| ```python | |
| from huggingface_hub import hf_hub_download | |
| path = hf_hub_download( | |
| repo_id="cropilot-community/default_crop_v1", | |
| filename="default_crop_v1.pt", | |
| ) | |
| print(path) | |
| ``` | |
| ## Versioning | |
| This repository stores **v1** of the default crop model. Newer fine-tuned models should be published as separate `v<n>` repositories so deployments remain reproducible. | |