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--- |
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tags: |
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- huggan |
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- gan |
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datasets: |
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- arakesh/uavid-15-hq-mixedres |
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license: mit |
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--- |
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# MyModelName |
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## Model description |
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[Pix2pix Model](https://arxiv.org/abs/1611.07004) is a conditional adversarial networks, a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. |
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## Intended uses & limitations: |
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Used for reconstruction of images from edges |
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#### How to use |
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```python |
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from torchvision.transforms import Compose, Resize, ToTensor, Normalize |
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from PIL import Image |
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from torchvision.utils import save_image |
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import cv2 |
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from huggan.pytorch.pix2pix.modeling_pix2pix import GeneratorUNet |
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transform = Compose( |
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[ |
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Resize((256, 256), Image.BICUBIC), |
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ToTensor(), |
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Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), |
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] |
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) |
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model = GeneratorUNet.from_pretrained('huggan/pix2pix-uavid-15) |
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def predict_fn(img): |
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inp = transform(img).unsqueeze(0) |
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out = model(inp) |
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save_image(out, 'out.png', normalize=True) |
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return 'out.png' |
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predict_fn(img) |
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``` |
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#### Limitations and bias |
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* Gives unrealistic colors in the image |
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## Training data |
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* [edges2shoes](https://huggingface.co/datasets/huggan/edges2shoes) |
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## Training procedure |
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``` |
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# clone the repository |
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git clone https://github.com/huggingface/community-events.git |
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pip install . |
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# change directory |
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cd community-events/huggan/pytorch/pix2pix/ |
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# define config |
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accelerate config |
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# launch training with required parameters |
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accelerate launch train.py --checkpoint_interval 1 --dataset arakesh/uavid-15-hq-mixedres --push_to_hub --model_name pix2pix-uavid-15 --batch_size 2 --n_epochs 50 --image_size 1024 --sample_interval 500 |
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``` |
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## Generated Images |
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Here, |
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* First Image Row: Input Image |
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* Second Image Row: Generated Image |
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* Third Image Row: Target Image |
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![image1](34000.png) |
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![image2](35000.png) |
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### BibTeX entry and citation info |
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```bibtex |
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@article{pix2pix2017, |
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title={Image-to-Image Translation with Conditional Adversarial Networks}, |
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author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A}, |
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journal={CVPR}, |
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year={2017} |
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} |
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``` |