
# Pix2Pix trained on the maps dataset

## Model description

This model is a [Pix2Pix](https://arxiv.org/abs/1611.07004) model trained on the [huggan/maps](https://huggingface.co/datasets/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.modeling_pix2pix import GeneratorUNet
from PIL import Image
from torchvision.utils import save_image

image = Image.open("...")

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

pixel_values = transform(image).unsqueeze(0)
output = generator(pixel_values)
save_image(output, 'output.png', normalize=True)
```

#### Limitations and bias

Provide examples of latent issues and potential remediations.

## Training data

The data used was huggan/maps.

## Training procedure

The following command was used:

```bash
accelerate launch train.py --dataset huggan/maps --push_to_hub --model_name pix2pix-maps --checkpoint_interval 1
```

## 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}
}
```
{"license": "mit", "tags": ["huggan", "gan"], "datasets": ["huggan/maps"]}
pytorch huggan gan dataset:huggan/maps arxiv:1611.07004 license:mit has_space region:us
TAGS
#pytorch #huggan #gan #dataset-huggan/maps #arxiv-1611.07004 #license-mit #has_space #region-us 


# 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 provided by HuggingFace as part of the HugGAN sprint.

## Intended uses & limitations

#### How to use



#### Limitations and bias

Provide examples of latent issues and potential remediations.

## Training data

The data used was huggan/maps.

## Training procedure

The following command was used:



## Eval results

## Generated Images

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

### BibTeX entry and citation info


# 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 provided by HuggingFace as part of the HugGAN sprint. ## Intended uses & limitations #### How to use #### Limitations and bias

Provide examples of latent issues and potential remediations. ## Training data

The data used was huggan/maps. ## Training procedure

The following command was used: ## Eval results ## Generated Images

You can embed local or remote images using '![](...)' ### BibTeX entry and citation info
TAGS
#pytorch #huggan #gan #dataset-huggan/maps #arxiv-1611.07004 #license-mit #has_space #region-us 
 # 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 provided by HuggingFace as part of the HugGAN sprint. ## Intended uses & limitations #### How to use #### Limitations and bias

Provide examples of latent issues and potential remediations. ## Training data

The data used was huggan/maps. ## Training procedure

The following command was used: ## Eval results ## Generated Images

You can embed local or remote images using '![](...)' ### BibTeX entry and citation info
passage: TAGS
#pytorch #huggan #gan #dataset-huggan/maps #arxiv-1611.07004 #license-mit #has_space #region-us 
# 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 provided by HuggingFace as part of the HugGAN sprint.## Intended uses & limitations#### How to use#### Limitations and bias

Provide examples of latent issues and potential remediations.## Training data

The data used was huggan/maps.## Training procedure

The following command was used:## Eval results## Generated Images

You can embed local or remote images using '![](...)'### BibTeX entry and citation info