This repository contains the implementation of Fast detection of slender bodies in high density microscopy data paper.
To run the code one must first install the dependencies. You can do this in a virtual environment:
python3 -m venv venv
source venv/bin/activate
Start by installing jax following instructions at their repository.
Install the remaining dependencies afterwards:
pip install -r requirements.txt
Note the code relies on a custom fork of dm-haiku.
If you need to use the model and the auxiliary functions outside this repository, you can install it from the root folder by
pip install -e .
To train the model, there is a train script used for the model presented in the paper. The possible arguments can be seen by using the help flag.
python3 train.py --help
An example of a training run would be
python3 train.py --batch_size=32 --eval_interval=10 --nworms=100,200 --save
Example scripts such as detection and tracking can be found in the examples folder
We include a Dockerfile (cpu only). For linux we provide a script to run the relevant commands:
(sudo) sh docker_run.sh
The weights used in the paper can be downloaded from here or by using the following commmand
wget https://sid.erda.dk/share_redirect/cEjIpG1yQl -O weights.zip