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

A system for quickly generating training data with weak supervision

A fast, dataset-agnostic, deep visual search engine for digital art history

Pacman-AI - AI project designed by UC Berkeley. Designed reflex and minimax agents for the game Pacman.

A library for low-memory inferencing in PyTorch.

Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs

The code for the NeurIPS 2021 paper "A Unified View of cGANs with and without Classifiers".

The official implementation of CVPR 2021 Paper: Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation.

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Official pytorch implementation of Active Learning for deep object detection via probabilistic modeling (ICCV 2021)

PyTorch reimplementation of the Smooth ReLU activation function proposed in the paper "Real World Large Scale Recommendation Systems Reproducibility and Smooth Activations" [arXiv 2022].

TraSw for FairMOT - A Single-Target Attack example (Attack ID: 19; Screener ID: 24):

Hidden-Fold Networks (HFN): Random Recurrent Residuals Using Sparse Supermasks

Using Machine Learning to Create High-Res Fine Art

Towards Implicit Text-Guided 3D Shape Generation (CVPR2022)

[CVPR 2022 Oral] Balanced MSE for Imbalanced Visual Regression https://arxiv.org/abs/2203.16427

Kaggle Ultrasound Nerve Segmentation competition [Keras]

This project implements "virtual speed" from heart rate monito

Offical implementation of Shunted Self-Attention via Multi-Scale Token Aggregation

Implementation of "StrengthNet: Deep Learning-based Emotion Strength Assessment for Emotional Speech Synthesis"

This code is part of the reproducibility package for the SANER 2022 paper "Generating Clarifying Questions for Query Refinement in Source Code Search".