Pytorch 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
- 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
- cityscapes after 200 epochs
- First column: input, second column: output, third column: ground truth
- Generate animation for fixed inputs
- Learning Time
- cityscapes pix2pix - Avg. per epoch: 332.08 sec; Total 200 epochs: 66,846.58 sec
- facades after 200 epochs
- First column: input, second column: output, third column: ground truth
- Generate animation for fixed inputs
- Learning Time
- facades pix2pix - Avg. per epoch: 44.94 sec; Total 200 epochs: 9,282.64 sec
- edges2handbags after 15 epochs
- First column: input, second column: output, third column: ground truth
- Generate animation for fixed inputs
- Learning Time
- edges2handbags pix2pix - Avg. per epoch: 10,228.08 sec; Total 15 epochs: 153,443.62 sec
- Ubuntu 14.04 LTS
- NVIDIA GTX 1080 ti
- cuda 8.0
- Python 2.7.6
- pytorch 0.1.12
- matplotlib 1.3.1
- imageio 2.2.0
- scipy 0.19.1
[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)