Previous mice experiments found spaceflight may harm ovarian health. More analysis is needed to support prolonged spaceflight. We aim to develop a deep learning algorithm and software to efficiently analyze reproductive-cycle data, and release this tool to the scientific community. This is a work in progress.
We use Anaconda to manage our Python environment. The Python dependencies are listed in environment.yml
.
To create the conda environment, run:
conda env create -f environment.yml
The code for a ResNet transfer-learning model currently exists in the repository. You can train it with the following:
python train.py -e {experiment_name} -d data/[dataset_name] -m {model} {model_params} -o {optimizer} {optimizer_params} -n {num_epochs} -b {batch_size}
Use the -h
flag for help.
By default, training outputs to experiments/[experiment_name]/[dataset_name]/[model]/
.
To contribute, either talk to the team in person or shoot us an email (Andrew's is [email protected]). To propose new changes, create a new branch and submit a PR.