https://github.com/znxlwm/tensorflow-pix2pix

Tensorflow implementation of pix2pix for various datasets.
https://github.com/znxlwm/tensorflow-pix2pix

cgan conditional-gan gan generative-adversarial-network generative-model image-translation pix2pix tensorflow tensorflow-pix2pix unet

Last synced: 29 days ago
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Tensorflow implementation of pix2pix for various datasets.

• Host: GitHub
• URL: https://github.com/znxlwm/tensorflow-pix2pix
• Owner: znxlwm
• Created: 2017-08-16T00:30:49.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2017-08-22T09:19:20.000Z (over 7 years ago)
• Last Synced: 2024-09-05T15:48:31.409Z (4 months ago)
• Topics: cgan, conditional-gan, gan, generative-adversarial-network, generative-model, image-translation, pix2pix, tensorflow, tensorflow-pix2pix, unet
• Language: Python
• Size: 30.7 MB
• Stars: 6
• Watchers: 4
• Forks: 3
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# tensorflow-pix2pix

Tensorflow implementation of pix2pix [1] for various datasets.

* you can download datasets: https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets/

* you can see more information for network architecture and training details in https://arxiv.org/pdf/1611.07004.pdf

## dataset

* cityscapes

  * 2,975 training images, 200 train epochs, 1 batch size, inverse order: True

* facades

  * 400 training images, 200 train epochs, 1 batch size, inverse order: True

* maps

  * 1,096 training images, 200 train epochs, 1 batch size, inverse order: True

* edges2shoes

  * 50k training images, 15 train epochs, 4 batch size, inverse order: False

* edges2handbags

  * 137k training images, 15 train epochs, 4 batch size, inverse order: False

## Resutls

### maps

* facades after 200 epochs

 Input 

 Output 

 Ground truth 



















* Generate animation for fixed inputs

  * First column: input, second column: output, third column: ground truth

![maps_gif](facades_results/facades_generate_animation.gif)

* Learning time

  * Avg. per epoch: 54.19 sec; Total 200 epochs: 11,339.61 sec

  

### maps

* maps after 200 epochs

 Input 

 Output 

 Ground truth 



















* Generate animation for fixed inputs

  * First column: input, second column: output, third column: ground truth

![maps_gif](maps_results/maps_generate_animation.gif)

* Learning time

  * Avg. per epoch: 205.08 sec; Total 200 epochs: 41,622.29 sec

  

## Development Environment

* Windows 7

* GTX1080 ti

* cuda 8.0

* Python 3.5.3

* tensorflow-gpu 1.2.1

* numpy 1.13.1

* matplotlib 2.0.2

* imageio 2.2.0

## Reference

[1] Isola, Phillip, et al. "Image-to-image translation with conditional adversarial networks." arXiv preprint arXiv:1611.07004 (2016).

(Full paper: https://arxiv.org/pdf/1611.07004.pdf)