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---
name: "K-POP"
license: "mit"
metrics:
- MAE
- PLCC
- SRCC
- R2
tags:
- focus-prediction
- microscopy
- pytorch
---
# K-POP: Predicting Distance to Focal Plane for Kato-Katz Prepared Microscopy Slides Using Deep Learning
<a href="https://pytorch.org/get-started/locally/"><img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white"></a><a href="https://pytorchlightning.ai/">
<img alt="Lightning" src="https://img.shields.io/badge/-Lightning-792ee5?logo=pytorchlightning&logoColor=white"></a>
<a href="https://hydra.cc/"><img alt="Config: Hydra" src="https://img.shields.io/badge/Config-Hydra-89b8cd"></a>
## Description
TODO
## How to run
TODO
Install dependencies
```bash
# clone project
git clone https://github.com/YourGithubName/your-repo-name
cd your-repo-name
# [OPTIONAL] create conda environment
conda create -n myenv python=3.8
conda activate myenv
# install pytorch according to instructions
# https://pytorch.org/get-started/
# install requirements
pip install -r requirements.txt
```
Train model with default configuration
```bash
# train on CPU
python train.py trainer.gpus=0
# train on GPU
python train.py trainer.gpus=1
```
Train model with chosen experiment configuration from [configs/experiment/](configs/experiment/)
```bash
python train.py experiment=experiment_name.yaml
```
You can override any parameter from command line like this
```bash
python train.py trainer.max_epochs=20 datamodule.batch_size=64
```
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