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jannikend/dinovtree
dinovtree is a machine learning model from jannikend. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as other.
[](https://RolnickLab.github.io/DINOvTree) [](https://arxiv.org/pdf/2603.23669) [](https://arxiv.org/abs/2603.23669) [](https://huggingface.co/datasets/jannikend/birch-trees)
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Updated Sep 4, 2026
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From the Hugging Face model README
This repository contains the model weights for the DINOvTree model introduced in our paper accepted at ECCV 2026:
Estimating Individual Tree Height and Species from UAV Imagery
Authors: Jannik Endres, Etienne Laliberté, David Rolnick, Arthur Ouaknine
Our model, DINOvTree, leverages a Vision Foundation Model (VFM) to extract features and predicts the height and species of the center tree in the input image with two separate heads.
Note: For full installation, training, and evaluation instructions, please refer to our GitHub repository.
We publish the model weights of DINOvTree in Base size for three distinct datasets.
| Dataset | Weights File | Description |
|---|---|---|
| Quebec Trees | dinovtreeb_quebectrees.pth | Temperate Forest |
| BCI | dinovtreeb_bci.pth | Tropical Forest |
| Quebec Plantations | dinovtreeb_quebecplantations.pth | Boreal Plantation |
The VFM of our checkpoint was initialized with DINOv3 weights, which are licensed under the DINOv3 License. The heads and our changes to the VFM weights are licensed under the Apache License 2.0.
If you find our work useful, please consider citing our paper:
@inproceedings{endres2026treeheightspecies,
title = {Estimating Individual Tree Height and Species from UAV Imagery},
author = {Endres, Jannik and Lalibert{\'e}, Etienne and Rolnick, David and Ouaknine, Arthur},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2026}
}