---
tags:
- huggan
- gan
datasets:
- huggan/maps
# See a list of available tags here:
# https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12
# task: unconditional-image-generation or conditional-image-generation or image-to-image
license: mit
---

# Pix2Pix trained on the maps dataset

## Model description

This model is a [Pix2Pix]() model trained on the [huggan/maps]() dataset. The goal for the model is to turn a satellite map into a geographic map à la Google Maps, and the other way around.

The model was trained using the [example script](https://github.com/huggingface/community-events/tree/main/huggan/pytorch/pix2pix) provided by HuggingFace as part of the [HugGAN sprint](https://github.com/huggingface/community-events/tree/main/huggan).

## Intended uses & limitations

#### How to use

```python
from huggan.pytorch.pix2pix import GeneratorUNet

generator = GeneratorUNet.from_pretrained("huggan/pix2pix-maps")
```

#### Limitations and bias

Provide examples of latent issues and potential remediations.

## Training data

The data used was huggan/maps.

## Training procedure

Preprocessing, hardware used, hyperparameters...

## Eval results

## Generated Images

You can embed local or remote images using `![](...)`

### BibTeX entry and citation info

```bibtex
@article{DBLP:journals/corr/IsolaZZE16,
  author    = {Phillip Isola and
               Jun{-}Yan Zhu and
               Tinghui Zhou and
               Alexei A. Efros},
  title     = {Image-to-Image Translation with Conditional Adversarial Networks},
  journal   = {CoRR},
  volume    = {abs/1611.07004},
  year      = {2016},
  url       = {http://arxiv.org/abs/1611.07004},
  eprinttype = {arXiv},
  eprint    = {1611.07004},
  timestamp = {Mon, 13 Aug 2018 16:49:05 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/IsolaZZE16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```