readme: ---
  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](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}
  }
  ```