Update README.md

README.md

@@ -1,9 +1,143 @@
 ---
 tags:
-- model_hub_mixin
+- autoencoder
+- image-colorization
+- pytorch
 - pytorch_model_hub_mixin
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: [More Information Needed]
-- Docs: [More Information Needed]
+# 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
+
+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:
+
+Epoch 1: Training Loss: 0.0063, Validation Loss: 0.0042
+Epoch 2: Training Loss: 0.0036, Validation Loss: 0.0035
+Epoch 3: Training Loss: 0.0032, Validation Loss: 0.0032
+Epoch 4: Training Loss: 0.0030, Validation Loss: 0.0030
+Epoch 5: Training Loss: 0.0029, Validation Loss: 0.0030
+The model demonstrated continuous improvement in reducing both training and validation loss over the epochs.
+
+### Usage
+You can load the model from the Hugging Face Hub using the following code:
+
+```
+# Ensure you have the necessary dependencies installed:
+pip install torch torchvision transformers
+
+from transformers import AutoModel
+
+model = AutoModel.from_pretrained("sebastiansarasti/AutoEncoderImageColorization")
+```