https://github.com/lubosmj/i2i-gans
GANs used for translating images of faces to preserve the privacy of individuals.
https://github.com/lubosmj/i2i-gans
discogan gcgan image-to-image-translation tensorflow travelgan
Last synced: 2 months ago
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GANs used for translating images of faces to preserve the privacy of individuals.
- Host: GitHub
- URL: https://github.com/lubosmj/i2i-gans
- Owner: lubosmj
- License: apache-2.0
- Created: 2021-02-25T14:03:06.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2021-05-05T10:02:39.000Z (about 5 years ago)
- Last Synced: 2025-04-06T00:44:51.679Z (over 1 year ago)
- Topics: discogan, gcgan, image-to-image-translation, tensorflow, travelgan
- Language: Python
- Homepage:
- Size: 50.8 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# I2I-GANs
Common generative adversarial networks (GANs) implemented in TensorFlow 2.4.1. The GANs are suitable
for image-to-image translation tasks.
The repository was published as a part of the master's thesis (Generative Adversarial Networks Applied
for Privacy Preservation in Biometric-Based Authentication and Identification). Preliminary
results were presented at http://excel.fit.vutbr.cz/submissions/2021/031/31.pdf.
The following architectures are implemented:
- TraVeLGAN (https://github.com/KrishnaswamyLab/travelgan)
- DiscoGAN (https://github.com/SKTBrain/DiscoGAN)
- GcGAN (https://github.com/hufu6371/GcGAN)
### Setup
1. Clone this repository:
```
git clone https://github.com/lubosmj/I2I-GANs && cd I2I-GANs
```
2. Create a new virtual environment:
```
python3 -m venv venv
source source venv/bin/activate
```
3. Install the packages:
```
python3 setup.py install
```
4. Use the installed modules in your application:
```python3
from i2i_gans import TraVeLGAN
travelgan = TraVeLGAN(...)
travelgan.compile()
travelgan.load_weights(...)
fake_images = travelgan.generator(...)
```
### Running the Examples
1. Train a new TraVeLGAN model:
```
python3 -m examples.travelgan_trainer train --domain_A "path/to/dataset/A/*.png" --domain_B "path/to/dataset/B/*.png" --dataset_size 5000 --batch_size=16 --checkpoints_freq 10 --parallel --samples_freq 10 --samples_dir samples --checkpoints_dir checkpoints --augment random_flip_left_right --epochs 250
```
2. Train a new DiscoGAN model:
```
python3 -m examples.discogan_trainer train --domain_A "path/to/dataset/A/*.png" --domain_B "path/to/dataset/B/*.png" --dataset_size 5000 --batch_size=200 --checkpoints_freq 10 --parallel --samples_freq 10 --samples_dir samples --checkpoints_dir checkpoints --augment random_flip_left_right --epochs 200
```
3. Train a new GcGAN model:
```
python3 -m examples.gcgan_trainer train --domain_A "path/to/dataset/A/*.png" --domain_B "path/to/dataset/B/*.png" --dataset_size 5000 --batch_size=12 --checkpoints_freq 10 --parallel --samples_freq 10 --samples_dir samples --checkpoints_dir checkpoints --augment random_flip_left_right --epochs 200
```
### Generated Images
#### TraVeLGAN
The GAN was trained for 250 epochs with Adam optimizer (learning rate: 0.0002, batch size: 16, dataset size: 8,000).
- Datasets:
- Augmented images from [CelebA](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html)
- [flowers102](https://www.robots.ox.ac.uk/~vgg/data/flowers/)
#### DiscoGAN
The GAN was trained for 200 epochs with the same hyper-parameters as recommended in the original paper (dataset size: 20,000). Additionally, one convolution layer with 100 filters was inserted into the generators.
- Datasets:
- [CelebA](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html)
- [UT Zappos50K](http://vision.cs.utexas.edu/projects/finegrained/utzap50k/)
