Lava-DL, but with PyTorch-Lightning flavour

Last update: Oct 31, 2022

Related tags

Deep Learning lava-dl-lightning

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

Description

What it does

How to run

Requires Python 3.8+. First, install dependencies

# clone project   
git clone https://github.com/Barchid/lava-dl-lightning
cd lava-dl-lightning

# install lava
wget https://github.com/lava-nc/lava/releases/download/v0.2.0/lava-nc-0.2.0.tar.gz
pip install -U pip
pip install lava-nc-0.2.0.tar.gz

# install lava-dl
wget https://github.com/lava-nc/lava-dl/releases/download/v0.1.1/lava-dl-0.1.1.tar.gz
pip install lava-dl-0.1.1.tar.gz

# install pytorch-lightning
pip install pytorch-lightning torchmetrics

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}
}

Lava-DL, but with PyTorch-Lightning flavour

Related tags

Overview

Deep learning project seed

Goals

DELETE EVERYTHING ABOVE FOR YOUR PROJECT

Your Project Name

Description

How to run

Imports

Citation

Owner

Sami BARCHID

HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset (ICCV 2021)

DetCo: Unsupervised Contrastive Learning for Object Detection

Temporal Dynamic Convolutional Neural Network for Text-Independent Speaker Verification and Phonemetic Analysis

For IBM Quantum Challenge 2021 (May 20 - 26)

Infrastructure as Code (IaC) for a self-hosted version of Gnosis Safe on AWS

From the basics to slightly more interesting applications of Tensorflow

Code for Motion Representations for Articulated Animation paper

Code for Multiple Instance Active Learning for Object Detection, CVPR 2021

Arch-Net: Model Distillation for Architecture Agnostic Model Deployment

Random-Afg - Afghanistan Random Old Idz Cloner Tools

A command line simple note taking app

Efficient Lottery Ticket Finding: Less Data is More

docTR by Mindee (Document Text Recognition) - a seamless, high-performing & accessible library for OCR-related tasks powered by Deep Learning.

The code for Expectation-Maximization Attention Networks for Semantic Segmentation (ICCV'2019 Oral)

A GOOD REPRESENTATION DETECTS NOISY LABELS

This PyTorch package implements MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation (NAACL 2022).

Differentiable architecture search for convolutional and recurrent networks

An implementation on "Curved-Voxel Clustering for Accurate Segmentation of 3D LiDAR Point Clouds with Real-Time Performance"

Easy-to-use micro-wrappers for Gym and PettingZoo based RL Environments

Self-Supervised Pre-Training for Transformer-Based Person Re-Identification