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  # Pix2Pix UNet Model
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- - **Image Size:** 256
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- - **Model Type:** small_UNet (256)
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  ## Model Architecture
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  UNet(
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  (encoder): Sequential(
@@ -11,13 +11,21 @@ UNet(
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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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  )
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  (decoder): Sequential(
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- (0): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
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  (1): ReLU(inplace=True)
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- (2): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
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  (3): ReLU(inplace=True)
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- (4): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
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- (5): Tanh()
 
 
 
 
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  )
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  )
 
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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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  (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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  )