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# Pix2Pix UNet Model |
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- **Image Size:** 1024 |
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- **Model Type:** big_UNet (1024) |
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## Model Architecture |
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UNet( |
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(encoder): Sequential( |
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(0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) |
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(1): ReLU(inplace=True) |
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(2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) |
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(3): ReLU(inplace=True) |
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(4): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) |
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(5): ReLU(inplace=True) |
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(6): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) |
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(7): ReLU(inplace=True) |
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(8): Conv2d(512, 1024, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) |
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(9): ReLU(inplace=True) |
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) |
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(decoder): Sequential( |
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(0): ConvTranspose2d(1024, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) |
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(1): ReLU(inplace=True) |
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(2): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) |
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(3): ReLU(inplace=True) |
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(4): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) |
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(5): ReLU(inplace=True) |
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(6): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) |
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(7): ReLU(inplace=True) |
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(8): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) |
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(9): Tanh() |
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) |
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) |