Website | Arxiv

Transformers gain huge attention since they are first introduced and have a wide range of applications. Transformers start to take over all areas of deep learning and the Vision transformers paper also proved that they can be used for computer vision tasks. In this paper, we utilized a vision transformer-based custom-designed model, tensor-to-image, for the image to image translation. With the help of self-attention, our model was able to generalize and apply to different problems without a single modification

Clone the repo

git clone https://github.com/yigitgunduc/tensor-to-image/

Install requirements

pip3 install -r requirements.txt

For GPU support setup TensorFlow >= 2.4.0 with CUDA v11.0 or above

• you can ignore this step if you are going to train on the CPU

Train the model

python3 src/train.py

Weights are saved after every epoch and can be found in ./weights/

After you have trained the model you can test it against 3 different criteria (FID, Structural similarity, Inceptoin score).

python3 src/evaluate.py path/to/weights

Implementation support 8 datasets for various tasks. 6 pix2pix datasets and two additional ones. 6 of the pix2pix dataset can be used by changing the DATASET variable on the src/train.py for the additional datasets please see notebooks/object-segmentation.ipynb and notebooks/depth.ipynb

Dataset available thought the src/train.py

• cityscapes 99 MB
• edges2handbags 8.0 GB
• edges2shoes 2.0 GB
• facades 29 MB
• maps 239 MB
• night2day 1.9 GB

Dataset available though the notebooks

• Oxford-IIIT Pets
• RGB+D DATABASE

If you use this code for your research, please cite our paper Tensor-to-Image: Image-to-Image Translation with Vision Transformers

@article{gunducc2021tensor,
  title={Tensor-to-Image: Image-to-Image Translation with Vision Transformers},
  author={G{\"u}nd{\"u}{\c{c}}, Yi{\u{g}}it},
  journal={arXiv preprint arXiv:2110.08037},
  year={2021}
}