This is an unofficial PyTorch implementation of the paper Image-to-Image Translation with Conditional Adversarial Nets.
If you find this code useful, please star the repository.
- Clone this repository
git clone "https://github.com/FarnoushRJ/MLProject_Pix2Pix.git"
-
Install the requirements
Other Requirements
- Pillow 7.0.0
- numpy 1.18.4
- matplotlib 3.2.1
- barbar 0.2.1
- torch 1.5.0
- torchvision 0.6.0
- Facades and Maps datasets can be downloaded from this link.
Data Directory Structure
|__ DATASET_ROOT
|__ train
|__ test
|__ val
cd train/
python train.py --argsThe models is trained for 200 epochs on both Facades and Maps datasets.
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| Facades Training Loss | Maps(AtoB) Training Loss | Maps(BtoA) Training Loss |
Input, Fake Target, Real Target
Input, Fake Target, Real Target (AtoB)
Input, Fake Target, Real Target (BtoA)
- Models
- Modified Model for deblurring, denoising and Inpainting
