Pytorch Lightning code guideline for conferences

Overview

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 Conference Conference Conference

CI testing

Description

What it does

How to run

First, install dependencies

# 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.

# 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:

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}
}
Owner
Pytorch Lightning
Pytorch Lightning
Ensemble Learning Priors Driven Deep Unfolding for Scalable Snapshot Compressive Imaging [PyTorch]

Ensemble Learning Priors Driven Deep Unfolding for Scalable Snapshot Compressive Imaging [PyTorch] Abstract Snapshot compressive imaging (SCI) can rec

integirty 6 Nov 01, 2022
BLEND: A Fast, Memory-Efficient, and Accurate Mechanism to Find Fuzzy Seed Matches

BLEND is a mechanism that can efficiently find fuzzy seed matches between sequences to significantly improve the performance and accuracy while reducing the memory space usage of two important applic

SAFARI Research Group at ETH Zurich and Carnegie Mellon University 19 Dec 26, 2022
Collision risk estimation using stochastic motion models

collision_risk_estimation Collision risk estimation using stochastic motion models. This is a new approach, based on stochastic models, to predict the

Unmesh 7 Jun 26, 2022
Reference implementation for Deep Unsupervised Learning using Nonequilibrium Thermodynamics

Diffusion Probabilistic Models This repository provides a reference implementation of the method described in the paper: Deep Unsupervised Learning us

Jascha Sohl-Dickstein 238 Jan 02, 2023
Cognition-aware Cognate Detection

Cognition-aware Cognate Detection The repository which contains our code for our EACL 2021 paper titled, "Cognition-aware Cognate Detection". This wor

Prashant K. Sharma 1 Feb 01, 2022
Can we learn gradients by Hamiltonian Neural Networks?

Can we learn gradients by Hamiltonian Neural Networks? This project was carried out as part of the Optimization for Machine Learning course (CS-439) a

2 Aug 22, 2022
PaddleBoBo是基于PaddlePaddle和PaddleSpeech、PaddleGAN等开发套件的虚拟主播快速生成项目

PaddleBoBo - 元宇宙时代,你也可以动手做一个虚拟主播。 PaddleBoBo是基于飞桨PaddlePaddle深度学习框架和PaddleSpeech、PaddleGAN等开发套件的虚拟主播快速生成项目。PaddleBoBo致力于简单高效、可复用性强,只需要一张带人像的图片和一段文字,就能

502 Jan 08, 2023
tinykernel - A minimal Python kernel so you can run Python in your Python

tinykernel - A minimal Python kernel so you can run Python in your Python

fast.ai 37 Dec 02, 2022
PyTorch Implementation of [1611.06440] Pruning Convolutional Neural Networks for Resource Efficient Inference

PyTorch implementation of [1611.06440 Pruning Convolutional Neural Networks for Resource Efficient Inference] This demonstrates pruning a VGG16 based

Jacob Gildenblat 836 Dec 26, 2022
PyTorch implementation of some learning rate schedulers for deep learning researcher.

pytorch-lr-scheduler PyTorch implementation of some learning rate schedulers for deep learning researcher. Usage WarmupReduceLROnPlateauScheduler Visu

Soohwan Kim 59 Dec 08, 2022
Autonomous Movement from Simultaneous Localization and Mapping

Autonomous Movement from Simultaneous Localization and Mapping About us Built by a group of Clarkson University students with the help from Professor

14 Nov 07, 2022
A Pytree Module system for Deep Learning in JAX

Treex A Pytree-based Module system for Deep Learning in JAX Intuitive: Modules are simple Python objects that respect Object-Oriented semantics and sh

Cristian Garcia 216 Dec 20, 2022
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System

Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System Authors: Yixuan Su, Lei Shu, Elman Mansimov, Arshit Gupta, Deng Cai, Yi-An Lai

Amazon Web Services - Labs 123 Dec 23, 2022
Camera calibration & 3D pose estimation tools for AcinoSet

AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild Daniel Joska, Liam Clark, Naoya Muramatsu, Ricardo Jericevich, Fre

African Robotics Unit 42 Nov 16, 2022
Provide partial dates and retain the date precision through processing

Prefix date parser This is a helper class to parse dates with varied degrees of precision. For example, a data source might state a date as 2001, 2001

Friedrich Lindenberg 13 Dec 14, 2022
Research shows Google collects 20x more data from Android than Apple collects from iOS. Block this non-consensual telemetry using pihole blocklists.

pihole-antitelemetry Research shows Google collects 20x more data from Android than Apple collects from iOS. Block both using these pihole lists. Proj

Adrian Edwards 290 Jan 09, 2023
Convert openmmlab (not only mmdetection) series model to tensorrt

MMDet to TensorRT This project aims to convert the mmdetection model to TensorRT model end2end. Focus on object detection for now. Mask support is exp

JinTian 4 Dec 17, 2021
Imaginaire - NVIDIA's Deep Imagination Team's PyTorch Library

Imaginaire Docs | License | Installation | Model Zoo Imaginaire is a pytorch library that contains optimized implementation of several image and video

NVIDIA Research Projects 3.6k Dec 29, 2022
Cockpit is a visual and statistical debugger specifically designed for deep learning.

Cockpit: A Practical Debugging Tool for Training Deep Neural Networks

Felix Dangel 421 Dec 29, 2022
A tool to estimate time varying instantaneous reproduction number during epidemics

EpiEstim A tool to estimate time varying instantaneous reproduction number during epidemics. It is described in the following paper: @article{Cori2013

MRC Centre for Global Infectious Disease Analysis 78 Dec 19, 2022