Instructions to use MykolaL/DelineateAnything with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use MykolaL/DelineateAnything with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("MykolaL/DelineateAnything") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Model Card for Delineate Anything: Resolution-Agnostic Field Boundary Delineation on Satellite Imagery
by Mykola Lavreniuk, Nataliia Kussul, Andrii Shelestov, Yevhenii Salii, Volodymyr Kuzin, Sergii Skakun, Zoltan Szantoi
Delineate Anything Flow (DelAnyFlow) is a resolution-agnostic methodology for fast, country-level agricultural field boundary detection from satellite imagery. It utilizes the DelAny instance segmentation model, which is based on a YOLOv11 backbone and trained on the large-scale Field Boundary Instance Segmentation-22M (FBIS 22M) dataset.
DelAny delivers state-of-the-art accuracy, showing significantly higher mAP and faster inference than alternatives like SAM2. It supports national-scale applications, having been used to generate a complete field boundary layer for Ukraine (603,000 km²) in under six hours.
Paper
Delineate Anything Flow: Fast, Country-Level Field Boundary Detection from Any Source
Citation
@misc{lavreniuk2025delineate,
title={Delineate Anything Flow: Fast, Country-Level Field Boundary Detection from Any Source},
author={Mykola Lavreniuk and Nataliia Kussul and Andrii Shelestov and Yevhenii Salii and Volodymyr Kuzin and Sergii Skakun and Zoltan Szantoi},
year={2025},
eprint={2511.13417},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2511.13417},
}
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