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https://github.com/ImagingLab/Colorizing-with-GANs
Grayscale Image Colorization with Generative Adversarial Networks. https://arxiv.org/abs/1803.05400
https://github.com/ImagingLab/Colorizing-with-GANs
colorization convolutional-networks deep-learning gans generative-adversarial-network
Last synced: 3 months ago
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Grayscale Image Colorization with Generative Adversarial Networks. https://arxiv.org/abs/1803.05400
- Host: GitHub
- URL: https://github.com/ImagingLab/Colorizing-with-GANs
- Owner: ImagingLab
- License: apache-2.0
- Created: 2018-01-25T03:45:42.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2022-09-05T00:16:08.000Z (about 2 years ago)
- Last Synced: 2024-05-09T11:31:56.372Z (6 months ago)
- Topics: colorization, convolutional-networks, deep-learning, gans, generative-adversarial-network
- Language: Python
- Homepage:
- Size: 23.5 MB
- Stars: 260
- Watchers: 13
- Forks: 92
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Image Colorization with Generative Adversarial Networks
In this work, we generalize the colorization procedure using a conditional Deep Convolutional Generative Adversarial Network (DCGAN) as as suggested by [Pix2Pix](https://arxiv.org/abs/1611.07004). The network is trained on the datasets [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) and [Places365](http://places2.csail.mit.edu). Some of the results from Places365 dataset are [shown here.](#places365-results)
## Prerequisites
- Linux
- Tensorflow 1.7
- NVIDIA GPU (12G or 24G memory) + CUDA cuDNN
## Getting Started
### Installation
- Clone this repo:
```bash
git clone https://github.com/ImagingLab/Colorizing-with-GANs.git
cd Colorizing-with-GANs
```
- Install Tensorflow and dependencies from https://www.tensorflow.org/install/
- Install python requirements:
```bash
pip install -r requirements.txt
```
### Dataset
- We use [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) and [Places365](http://places2.csail.mit.edu) datasets. To train a model on the full dataset, download datasets from official websites.
After downloading, put then under the `datasets` folder.
### Training
- To train the model, run `main.py` script
```bash
python main.py
```
- To train the model on places365 dataset with tuned hyperparameters:
```
python train.py \
--seed 100 \
--dataset places365 \
--dataset-path ./dataset/places365 \
--checkpoints-path ./checkpoints \
--batch-size 16 \
--epochs 10 \
--lr 3e-4 \
--label-smoothing 1
```
- To train the model of cifar10 dataset with tuned hyperparameters:
```
python train.py \
--seed 100 \
--dataset cifar10 \
--dataset-path ./dataset/cifar10 \
--checkpoints-path ./checkpoints \
--batch-size 128 \
--epochs 200 \
--lr 3e-4 \
--lr-decay-steps 1e4 \
--augment True
```
### Test
- Download the pre-trained weights [from here.](https://drive.google.com/open?id=1jTsAUAKrMiHO2gn7s-fFZ_zUSzgKoPyp) and copy them in the `checkpoints` folder.
- To test the model on a custom image(s), run `test.py` script:
```bash
python test.py \
--checkpoints-path ./checkpoints \ # checkpoints path
--test-input ./checkpoints/test \ # test image(s) path
--test-output ./checkpoints/output \ # output image(s) path
```
### Visual Turing Test
- Download the pre-trained weights [from here.](https://drive.google.com/open?id=1jTsAUAKrMiHO2gn7s-fFZ_zUSzgKoPyp) and copy them in the `checkpoints` folder.
- To evaluate the model qualitatively using visual Turing test, run `test-turing.py`:
```bash
python test-turing.py
```
- To apply time-based visual Turing test run (2 seconds decision time):
```bash
python test-turing.py --test-delay 2
```
## Networks Architecture
The architecture of generator is inspired by [U-Net](https://arxiv.org/abs/1505.04597): The architecture of the model is symmetric, with `n` encoding units and `n` decoding units. The contracting path consists of 4x4 convolution layers with stride 2 for downsampling, each followed by batch normalization and Leaky-ReLU activation function with the slope of 0.2. The number of channels are doubled after each step. Each unit in the expansive path consists of a 4x4 transposed convolutional layer with stride 2 for upsampling, concatenation with the activation map of the mirroring layer in the contracting path, followed by batch normalization and ReLU activation function. The last layer of the network is a 1x1 convolution which is equivalent to cross-channel parametric pooling layer. We use `tanh` function for the last layer.
For discriminator, we use patch-gan architecture with contractive path similar to the baselines: a series of 4x4 convolutional layers with stride 2 with the number of channels being doubled after each downsampling. All convolution layers are followed by batch normalization, leaky ReLU activation with slope 0.2. After the last layer, a sigmoid function is applied to return probability values of `70x70` patches of the input being real or fake. We take the average of the probabilities as the network output!
## Places365 Results
Colorization results with Places365. (a) Grayscale. (b) Original Image. (c) Colorized with GAN.
## Citation
If you use this code for your research, please cite our paper Image Colorization with Generative Adversarial Networks:
```
@inproceedings{nazeri2018image,
title={Image Colorization Using Generative Adversarial Networks},
author={Nazeri, Kamyar and Ng, Eric and Ebrahimi, Mehran},
booktitle={International Conference on Articulated Motion and Deformable Objects},
pages={85--94},
year={2018},
organization={Springer}
}
```