Devolearn is a Python package that aims to automate the process of collecting metadata from videos/images of the C. elegans embryo with the help of deep learning models. It is specialized for the analysis of 2-D slices of C. elegans embryogenesis.
This project will focus on improving the performance of existing models and training/benchmarking new models on a broad range of data sets from different species relevant to developmental biology. Your goals will be to improve overall performance of the models in terms of accuracy and generalizability, training/adding new models and improve the library’s usability.
There would be 4 key elements in this project:
- Improving the current models
- Training and adding more useful models
- Improving usability
- (optional) Interactive online demos.
Improving/adding new models
In order to get good performance from a deep-learning model, we should use techniques like:
- k-fold cross validation
- Ensembling/stacking
- Hyperparameter optimization
- Label smoothing
- Mixed precision inference
- Using more training data for re-training the pre-existing models for better accuracy
Improving usability
As the library grows, we should have a better place to host proper documentation on a static website. We also have to train devolearn’s deep-learning models on commonly available data, the more common the use case, the more helpful it is to the community.
Online demos
Jupyter notebooks, however simple they may seem, are still a bit intimidating to people from non CS backgrounds, so there should be a focus on making interactive and easy to run online demos which showcase both our research and tutorials.
Pre-requisites
Proficiency in python with a good hold of tools like numpy, pandas and PyTorch will be required.
Resources
More information can be found at: Proposals-Public-Lectures/upgrading-devolearn.md at master · devoworm/Proposals-Public-Lectures · GitHub
Mentors: Bradly Alicea, Mayukh Deb
Tags: DevoWorm, Python, microscopy, deep learning, data science
