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All Projects → togheppi → pix2pix

togheppi / pix2pix

Licence: other
PyTorch implementation of Image-to-Image Translation with Conditional Adversarial Nets (pix2pix)

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python
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Labels

mapspytorchganfacadespix2piximage-translationcityscapesimage-to-image-translationedges2shoesedges2handbags

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pix2pix

PyTorch implementation of Image-to-Image Translation with Conditional Adversarial Nets (pix2pix)

Generating Facades dataset

  • Image size: 256x256
  • Number of training images: 400
  • Number of test images: 106

Results

  • Adam optimizer is used. Learning rate = 0.0002, batch size = 1, # of epochs = 200:
GAN losses
( AE0000 : Generator / FF8900 : Discriminator)		 Generated images
(Input / Generated / Target)

  • Generated images using test data

    1st column: Input / 2nd column: Generated / 3rd column: Target

Generating Cityscapes dataset

  • Image size: 256x256
  • Number of training images: 2,975
  • Number of test images: 500

Results

  • Adam optimizer is used. Learning rate = 0.0002, batch size = 1, # of epochs = 200:
GAN losses
( AE0000 : Generator / FF8900 : Discriminator)		 Generated images
(Input / Generated / Target)

  • Generated images using test data

    1st column: Input / 2nd column: Generated / 3rd column: Target

Generating Maps dataset

  • Image is resized to 256x256 image (Original size: 600x600)
  • Number of training images: 1,096
  • Number of test images: 1,098

Results

  • Adam optimizer is used. Learning rate = 0.0002, batch size = 1, # of epochs = 200:
GAN losses
( AE0000 : Generator / FF8900 : Discriminator)		 Generated images
(Input / Generated / Target)

  • Generated images using test data

    1st column: Input / 2nd column: Generated / 3rd column: Target

Generating Edges2Shoes dataset

  • Image size: 256x256
  • Number of training images: 49,825
  • Number of test images: 200

Results

  • Adam optimizer is used. Learning rate = 0.0002, batch size = 4, # of epochs = 15:
GAN losses
( AE0000 : Generator / FF8900 : Discriminator)		 Generated images
(Input / Generated / Target)

  • Generated images using test data

    1st column: Input / 2nd column: Generated / 3rd column: Target

Generating Edges2Handbags dataset

  • Image size: 256x256
  • Number of training images: 138,567
  • Number of test images: 200

Results

  • Adam optimizer is used. Learning rate = 0.0002, batch size = 4, # of epochs = 15:
GAN losses
( AE0000 : Generator / FF8900 : Discriminator)		 Generated images
(Input / Generated / Target)

  • Generated images using test data

    1st column: Input / 2nd column: Generated / 3rd column: Target

References

  1. https://github.com/mrzhu-cool/pix2pix-pytorch
  2. https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
  3. https://github.com/znxlwm/pytorch-pix2pix
  4. https://affinelayer.com/pix2pix/
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