Update README.md

README.md

@@ -57,68 +57,7 @@ class ModelColorization(nn.Module, PyTorchModelHubMixin):
         x = self.decoder(x)
         return x
 
-Here's your model card in Markdown format:
-
-md
-Copy code
----
-tags:
-- autoencoder
-- image-colorization
-- pytorch
-- pytorch_model_hub_mixin
----
-
-# Model Colorization Autoencoder
-
-## Model Description
-
-This autoencoder model is designed for image colorization. It takes grayscale images as input and outputs colorized versions of those images. The model architecture consists of an encoder-decoder structure, where the encoder compresses the input image into a latent representation, and the decoder reconstructs the image in color.
-
-### Architecture
-
-- **Encoder**: The encoder comprises three convolutional layers followed by max pooling and ReLU activations, each paired with batch normalization. It ends with a flattening layer and a fully connected layer to produce a latent vector.
-- **Decoder**: The decoder mirrors the encoder, using linear and transposed convolutional layers with ReLU activations and batch normalization. The final layer outputs a color image using a sigmoid activation function.
-
-The architecture details are as follows:
-```python
-class ModelColorization(nn.Module, PyTorchModelHubMixin):
-    def __init__(self):
-        super(ModelColorization, self).__init__()
-        self.encoder = nn.Sequential(
-            nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1),
-            nn.MaxPool2d(kernel_size=2, stride=2),
-            nn.ReLU(),
-            nn.BatchNorm2d(64),
-            nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1),
-            nn.MaxPool2d(kernel_size=2, stride=2),
-            nn.ReLU(),
-            nn.BatchNorm2d(32),
-            nn.Conv2d(32, 16, kernel_size=3, stride=1, padding=1),
-            nn.MaxPool2d(kernel_size=2, stride=2),
-            nn.ReLU(),
-            nn.BatchNorm2d(16),
-            nn.Flatten(),
-            nn.Linear(16*45*45, 4000),
-        )
-        self.decoder = nn.Sequential(
-            nn.Linear(4000, 16 * 45 * 45),
-            nn.ReLU(),
-            nn.Unflatten(1, (16, 45, 45)),
-            nn.ConvTranspose2d(16, 32, kernel_size=3, stride=2, padding=1, output_padding=1),
-            nn.ReLU(),
-            nn.BatchNorm2d(32),
-            nn.ConvTranspose2d(32, 64, kernel_size=3, stride=2, padding=1, output_padding=1),
-            nn.ReLU(),
-            nn.BatchNorm2d(64),
-            nn.ConvTranspose2d(64, 3, kernel_size=3, stride=2, padding=1, output_padding=1),
-            nn.Sigmoid()
-        )
-
-    def forward(self, x):
-        x = self.encoder(x)
-        x = self.decoder(x)
-        return x
+```
 
 ### Training Details
 The model was trained using PyTorch for 5 epochs. Here are the training and validation losses observed during the training:
@@ -140,4 +79,4 @@ pip install torch torchvision transformers
 from transformers import AutoModel
 
 model = AutoModel.from_pretrained("sebastiansarasti/AutoEncoderImageColorization")
-```
+```python