featureforest 0.0.9

A napari plugin for segmentation using vision transformer features

Feature Forest

A napari plugin for making image annotation using feature space of vision transformers and random forest classifier. We developed a napari plugin to train a Random Forest model using extracted features of vision foundation models and just a few scribble labels provided by the user as input. This approach can do the segmentation of desired objects almost as well as manual segmentations but in a much shorter time with less manual effort.

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Documentation

You can check the documentation here (⚠️ work in progress!).

Installation

To install this plugin you need to use conda or mamba to create an environment and install the requirements. Use commands below to create the environment and install the plugin:

git clone https://github.com/juglab/featureforest
cd ./featureforest

# for GPU
conda env create -f ./env_gpu.yml

# if you don't have a GPU
conda env create -f ./env_cpu.yml

For developers that want to contribute to FeatureForest, you need to use this command to install the dev dependencies:

pip install -U "featureforest[dev]"

And make sure you have pre-commit installed in your environment, before committing changes:

pre-commit install

For more detailed installation guide, check out here.

Cite us

Seifi, Mehdi, Damian Dalle Nogare, Juan Battagliotti, Vera Galinova, Ananya Kediga Rao, AI4Life Horizon Europe Programme Consortium, Johan Decelle, Florian Jug, and Joran Deschamps. "FeatureForest: the power of foundation models, the usability of random forests." bioRxiv (2024): 2024-12. DOI: 10.1101/2024.12.12.628025

License

Distributed under the terms of the BSD-3 license, "featureforest" is free and open source software

Issues

If you encounter any problems, please file an issue along with a detailed description.