---
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
```