Image-to-Image Translation with Conditional Adversarial Nets

An implementation of pix2pix described in the paper using tensorflow.

• Image-to-Image Translation with Conditional Adversarial Nets

Published in CVPR 2017, written by P. Isola, J-Y Zhu, T. Zhou and A. Efros

Requirement

• Python 3.6.4
• Tensorflow 1.8.0
• Pillow 5.0.0
• numpy 1.14.5

How to Run

If you want to see more example commands, please refer to run.sh file in my repository.

Train

python main.py --tr_data_path ./dataSet/facades/train --transfer_type B_to_A --model_name facades

test

python main.py --mode test --val_data_path ./dataSet/facedes/val --transfer_type B_to_A --load_size 256 --pre_trained_model ./model/pix2pix-facades

Datasets

Shell script which downloads datasets will be uploaded soon!

Pre-trained model

• facades
• map2aerial
• aerial2map
• edges2shoes

Experimental Results

Experimental results on map dataset(map to aeriel)

Input	result	Target

Experimental results on map dataset(aeriel to map)

Input	result	Target

Experimental results on facades dataset

Input	result	Target

Experimental results on edges2shoes dataset

Input	result	Target

Comments

If you have any questions or comments on my codes, please email to me. son1113@snu.ac.kr

Reference

[1] https://github.com/phillipi/pix2pix