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.
You can check the documentation here (
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.ymlFor more detailed installation guide, check out here.
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
Distributed under the terms of the BSD-3 license, "featureforest" is free and open source software
If you encounter any problems, please [file an issue] along with a detailed description.