DeepForest modal app

Modal app for serverless DeepForest [1] inference, training/fine tuning of tree crown detection and species classification models.

Features

Execute all your pipeline (preprocessing, training/fine tuning, inference, postprocessing...) within the same local script/notebook:

• When running DeepForest inference and training/fine tuning of tree detection models, this library will handle setting up a Modal ephemeral apps in a GPU-enabled environment, execute the deep learning parts there and you will then retrieve the results (e.g., a geopandas data frame) as a local variable within your notebook
• Optimized defaults for the serverless infrastructure (i.e., different training and inference GPUs) and matching settings (batch sizes, number of workers, image pre-loading...) to improve performance. TODO: support for multi-GPU training coming shortly.
• The required data (e.g., aerial imagery) and model checkpoints are uploaded to persistent Modal storage volumes
• Model checkpoints from HuggingFace Hub and PyTorch Hub are cached locally in a storage volume so uptime for ephemeral apps is minimal

Example annotations from the TreeAI Database (left), predictions with the DeepForest pre-trained tree crown model (center) and with the fine-tuned model (right).

Examples

The following example notebooks use the TreeAI Database [2] to illustrate the features of this setup:

• getting-started.ipynb: example notebook showcasing inference and training/fine-tuning (with the default settings).
• advanced-customizations.ipynb: shows how to use data augmentations, logging, callbacks and sharing checkpoints in HuggingFace Hub.
• crop-model.ipynb: draft on multi-species classification using the DeepForest crop model.

Installation

This app requires geopandas in the local environment, which cannot be installed with pip. Until we have a working conda-forge recipe, the easiest solution is to first install geopandas using conda/mamba, e.g.:

conda install geopandas

and then install "deepforest-modal-app" using pip:

pip install deepforest-modal-app

Acknowledgements

• A big thank you to Charles Frye and Thomas Capelle for helping me to get started with Modal.
• This package was created with the martibosch/cookiecutter-geopy-package project template.

References

1. Weinstein, B. G., Marconi, S., Aubry‐Kientz, M., Vincent, G., Senyondo, H., & White, E. P. (2020). DeepForest: A Python package for RGB deep learning tree crown delineation. Methods in Ecology and Evolution, 11(12), 1743-1751.
2. Beloiu Schwenke, M., Xia, Z., Novoselova, I., Gessler, A., Kattenborn, T., Mosig, C., Puliti, S., Waser, L., Rehush, N., Cheng, Y., Xinliang, L., Griess, V. C., & Mokroš, M. (2025). TreeAI Global Initiative - Advancing tree species identification from aerial images with deep learning (TreeAI.V1.2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15351054