GitPlanet

Cheap and reliable Node.js hosting starts at $3/month, and $1/month static HTML hosting

Created with love in Canada, visit hostnodejs.com today

Feel like to post an Ad? Learn Details
All Projects → solesensei → day2night

solesensei / day2night

Licence: other
Image2Image Translation Research

Programming Languages

python
139335 projects - #7 most used programming language
TeX
3793 projects
shell
77523 projects

Labels

researchunitunsupervised-learningimage-translationcycleganimage2imagebdd100knexetday2night

Projects that are alternatives of or similar to day2night

multitask-CycleGAN
Pytorch implementation of multitask CycleGAN with auxiliary classification loss
Stars: ✭ 88 (+91.3%)
Mutual labels:  image-translation, cyclegan
Pytorch-Image-Translation-GANs
Pytorch implementations of most popular image-translation GANs, including Pixel2Pixel, CycleGAN and StarGAN.
Stars: ✭ 106 (+130.43%)
Mutual labels:  image-translation, cyclegan
pytorch-CycleGAN
Pytorch implementation of CycleGAN.
Stars: ✭ 39 (-15.22%)
Mutual labels:  image-translation, cyclegan
CycleGAN
A simple code of CycleGAN which is easy to read is implemented by TensorFlow
Stars: ✭ 21 (-54.35%)
Mutual labels:  cyclegan, image2image
Cyclegan
PyTorch implementation of CycleGAN
Stars: ✭ 38 (-17.39%)
Mutual labels:  image-translation, cyclegan
ganslate
Simple and extensible GAN image-to-image translation framework. Supports natural and medical images.
Stars: ✭ 17 (-63.04%)
Mutual labels:  image-translation, cyclegan
BicycleGAN-pytorch
Pytorch implementation of BicycleGAN with implementation details
Stars: ✭ 99 (+115.22%)
Mutual labels:  image-translation, cyclegan
Cyclegan Tensorflow 2
CycleGAN Tensorflow 2
Stars: ✭ 330 (+617.39%)
Mutual labels:  image-translation, cyclegan
Cyclegan Tensorflow
Tensorflow implementation for learning an image-to-image translation without input-output pairs. https://arxiv.org/pdf/1703.10593.pdf
Stars: ✭ 676 (+1369.57%)
Mutual labels:  image-translation, cyclegan
Attentiongan
AttentionGAN for Unpaired Image-to-Image Translation & Multi-Domain Image-to-Image Translation
Stars: ✭ 341 (+641.3%)
Mutual labels:  image-translation, cyclegan
CS-DisMo
[ICCVW 2021] Rethinking Content and Style: Exploring Bias for Unsupervised Disentanglement
Stars: ✭ 20 (-56.52%)
Mutual labels:  unsupervised-learning, image-translation
Free Ai Resources
🚀 FREE AI Resources - 🎓 Courses, 👷 Jobs, 📝 Blogs, 🔬 AI Research, and many more - for everyone!
Stars: ✭ 192 (+317.39%)
Mutual labels:  research, unsupervised-learning
cycleGAN-PyTorch
A clean and lucid implementation of cycleGAN using PyTorch
Stars: ✭ 107 (+132.61%)
Mutual labels:  image-translation, cyclegan
Manifest
Manifest is an investigative toolkit intended for researchers, journalists, students, and scholars interested in visualizing, analyzing, and documenting supply chains, production lines, and trade networks.
Stars: ✭ 12 (-73.91%)
Mutual labels:  research
M-NMF
An implementation of "Community Preserving Network Embedding" (AAAI 2017)
Stars: ✭ 119 (+158.7%)
Mutual labels:  unsupervised-learning
covid19 scenarios data
Data preprocessing scripts and preprocessed data storage for COVID-19 Scenarios project
Stars: ✭ 43 (-6.52%)
Mutual labels:  research
lxa5
Linguistica 5: Unsupervised Learning of Linguistic Structure
Stars: ✭ 27 (-41.3%)
Mutual labels:  unsupervised-learning
Dual-CNN-Models-for-Unsupervised-Monocular-Depth-Estimation
Dual CNN Models for Unsupervised Monocular Depth Estimation
Stars: ✭ 36 (-21.74%)
Mutual labels:  unsupervised-learning
showstopper
ShowStopper is a tool for helping malware researchers explore and test anti-debug techniques or verify debugger plugins or other solutions that clash with standard anti-debug methods.
Stars: ✭ 132 (+186.96%)
Mutual labels:  research
Awesome-Cross-Domain-Person-Re-identification
Awesome-Cross-Domain-Person-Re-identification
Stars: ✭ 17 (-63.04%)
Mutual labels:  unsupervised-learning
View All Similar Projects ➔

Day2Night | Image2Image Translation Research

Diploma | Graduation Work | Bachelor's Degree

Lomonosov Moscow State University, Faculty of Computer Science, Graphics Lab

Usage

This repo is huge

System

  • Python 3.6
  • Ubuntu 18.04 LTS

Requirements

pip3 install -r requirements.txt
pip3 install -r scripts/requirements.txt # for scripts

or use docker

Usage

Get project

Clone repository

# Full repo
git clone --depth 1 https://github.com/solesensei/day2night.git
# Mini repo (just code)
git clone --branch code --depth 1 https://github.com/solesensei/day2night.git

or get the code from drive (if not working)

wget --no-check-certificate -r "https://docs.google.com/uc?export=download&id=1mrj0vDzuFufpmxSW5SMIAn9XekegX4Hh" -O code.zip
unzip -o code.zip

Testing

Modify parametrs in test.sh

And run test

cd ~/prj/UNIT
bash ./scripts/test.sh
# or
python test_batch.py --device $gpu --config $config --input_folder $indir --output_folder $outdir --number $number --checkpoint $checkpoint --a2b $d2n --trainer UNIT --recon

Training

Modify parametrs in train.sh and configs.

Get data

# BDD100k
kaggle datasets download -d solesensei/solesensei_bdd100k
# NEXET
kaggle datasets download -d solesensei/nexet-original

Prepare data

See DomainShifter and DataClassificator.

Start training

bash ./scripts/train.sh
# or
python train.py --device $gpu --config $config --trainer UNIT

Docker

Automated

Download bash script

wget https://raw.githubusercontent.com/solesensei/day2night/master/day2night/UNIT/scripts/day2night.sh -O ~/prj/day2night.sh

Then just run it (in repository you want to start)

cd ~/prj
bash day2night.sh

Manually

Get project

Get image

Pull Docker image

docker pull solesensei/day2night:pytorch_0.4.1 # CUDA 10 : Ubuntu 18.04 LTS
# or
docker pull solesensei/day2night:pytorch_0.4.1_cuda9 # CUDA 9 : Ubuntu 16.04 LTS

All tags:

  • pytorch_0.4.1_cuda9 : CUDA 9, Ubuntu 16.04 LTS, Pytorch==0.4.1
  • cyclegan - CUDA 9, Base, Pytorch>=1.0.1
  • pytorch_0.4.1 - CUDA 10, Ubuntu 18.04 LTS, Pytorch==0.4.1
  • pytorch_latest - CUDA 10, Ubuntu 18.04 LTS, Pytorch>=1.0.1

Run container

cd ~/prj
docker run -it -p 1111:1111 --name day2night --mount type=bind,source=$PWD,target=/mnt/w/prj -w /mnt/w/prj/UNIT --runtime nvidia -i -t solesensei/day2night:pytorch_0.4.1 # your tag here

Run Test

PT-models

UNIT vs. CycleGAN

RetinaNet Day Night All
Real Data Images (NEXET) 0.8664 0.8406 0.8535
CycleGAN 0.8701 (+0.42%) 0.8571 (+1.96%) 0.8636 (+1.18%)
UNIT 0.8749 (+0.98%) 0.8512 (+1.26%) 0.8631 (+1.12%)
Faster R-CNN Day Night All
Real Data Images (NEXET) 0.9015 0.8822 0.8919
CycleGAN 0.9087 (+0.79%) 0.8881 (+0.66%) 0.8984 (+0.72%)
UNIT 0.9066 (+0.56%) 0.8929 (+1.21%) 0.8998 (+0.88%)

VGG16 and Normalization

Results

UNIT

(top to bottom: input - reconstructed - translated)

Input - no VGG - VGG

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].