# pytorch-CycleGAN
**Repository Path**: windclub/pytorch-CycleGAN
## Basic Information
- **Project Name**: pytorch-CycleGAN
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-10-29
- **Last Updated**: 2020-12-19
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# pytorch-CycleGAN
Pytorch implementation of CycleGAN [1].
* you can download datasets: https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/
* you can see more information for network architecture and training details in https://arxiv.org/pdf/1703.10593.pdf
## dataset
* apple2orange
* apple training images: 995, orange training images: 1,019, apple test images: 266, orange test images: 248
* horse2zebra
* horse training images: 1,067, zebra training images: 1,334, horse test images: 120, zebra test images: 140
## Resutls
### apple2orange (after 200 epochs)
* apple2orange
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* orange2apple
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* Learning Time
* apple2orange - Avg. per epoch: 299.38 sec; Total 200 epochs: 62,225.33 sec
### horse2zebra (after 200 epochs)
* horse2zebra
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* zebra2horse
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* Learning Time
* horse2zebra - Avg. per epoch: 299.25 sec; Total 200 epochs: 61,221.27 sec
## Development Environment
* Ubuntu 14.04 LTS
* NVIDIA GTX 1080 ti
* cuda 8.0
* Python 2.7.6
* pytorch 0.1.12
* matplotlib 1.3.1
* scipy 0.19.1
## Reference
[1] Zhu, Jun-Yan, et al. "Unpaired image-to-image translation using cycle-consistent adversarial networks." arXiv preprint arXiv:1703.10593 (2017).
(Full paper: https://arxiv.org/pdf/1703.10593.pdf)