https://github.com/jinzhuoran/paper-template

https://github.com/jinzhuoran/paper-template

Last synced: 4 months ago
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• Host: GitHub
• URL: https://github.com/jinzhuoran/paper-template
• Owner: jinzhuoran
• License: apache-2.0
• Created: 2021-05-10T11:56:20.000Z (about 4 years ago)
• Default Branch: master
• Last Pushed: 2021-05-10T11:56:21.000Z (about 4 years ago)
• Last Synced: 2025-01-04T22:44:50.687Z (5 months ago)
• Language: Python
• Size: 6.84 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
### Deep learning project seed

Use this seed to start new deep learning / ML projects.

- Built in setup.py

- Built in requirements

- Examples with MNIST

- Badges

- Bibtex

#### Goals  

The goal of this seed is to structure ML paper-code the same so that work can easily be extended and replicated.   

### DELETE EVERYTHING ABOVE FOR YOUR PROJECT  

 

---

    

 

# Your Project Name     


[![Paper](http://img.shields.io/badge/paper-arxiv.1001.2234-B31B1B.svg)](https://www.nature.com/articles/nature14539)

[![Conference](http://img.shields.io/badge/NeurIPS-2019-4b44ce.svg)](https://papers.nips.cc/book/advances-in-neural-information-processing-systems-31-2018)

[![Conference](http://img.shields.io/badge/ICLR-2019-4b44ce.svg)](https://papers.nips.cc/book/advances-in-neural-information-processing-systems-31-2018)

[![Conference](http://img.shields.io/badge/AnyConference-year-4b44ce.svg)](https://papers.nips.cc/book/advances-in-neural-information-processing-systems-31-2018)  

![CI testing](https://github.com/PyTorchLightning/deep-learning-project-template/workflows/CI%20testing/badge.svg?branch=master&event=push)

   



 

## Description   

What it does   


## How to run   

First, install dependencies   

```bash

# clone project   

git clone https://github.com/YourGithubName/deep-learning-project-template

# install project   

cd deep-learning-project-template 

pip install -e .   

pip install -r requirements.txt

 ```   

 Next, navigate to any file and run it.   

 ```bash

# module folder

cd project

# run module (example: mnist as your main contribution)   

python lit_classifier_main.py    

```

## Imports

This project is setup as a package which means you can now easily import any file into any other file like so:

```python

from project.datasets.mnist import mnist

from project.lit_classifier_main import LitClassifier

from pytorch_lightning import Trainer

# model

model = LitClassifier()

# data

train, val, test = mnist()

# train

trainer = Trainer()

trainer.fit(model, train, val)

# test using the best model!

trainer.test(test_dataloaders=test)

```

### Citation   

```

@article{YourName,

  title={Your Title},

  author={Your team},

  journal={Location},

  year={Year}

}

```