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

I used two open datasets.

Nexet 2017 provided by Nexar. See more.
bdd100k 2018 under bdd100 licence from bdd100k.com. See more.

# 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

Google.Drive
Minimal Source Code

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%)

Day2Night Image2Image Translation Research

Image2Image Translation Research

Day2Night | Image2Image Translation Research

Usage

System

Requirements

Usage

Get project

Testing

Training

Get data

Prepare data

Start training

Docker

Automated

Manually

Get image

Run container

PT-models

UNIT vs. CycleGAN

VGG16 and Normalization

Results

UNIT