Update README.md

README.md

@@ -22,6 +22,55 @@ This model classifies animals among pandas, cats and dogs. It was trained using
 - **License:** MIT
 - **Contact:** [email protected]
 
+### Data Preprocessing
+The image dataset is preprocessed with the following portion:
+```bash
+transform = transforms.Compose([
+  transforms.Resize((224,224)),
+  transforms.ToTensor(),
+  transforms.Normalize((0.485,0.456,0.406),(0.229,0.224,0.225))
+  ])
+```
+
+transforms.Resize((224,224)) resizes the input image to (224, 224) pixels. 
+transforms.ToTensor() converts the input image into a PyTorch tensor. Neural networks typically operate on tensors, so this transformation converts the image into a format suitable for further processing.
+transforms.Normalize(()) normalizes the tensor image with mean and standard deviation. The values provided are mean and standard deviation values for each channel in the tensor.
+
+### Model Architecture
+
+The model was trained with custom CNN() model. this CNN architecture consists of two convolutional layers followed by two fully connected layers, and it is designed for a classification task with three classes.
+
+```bash
+class CNN(nn.Module):
+    def __init__(self):
+        super(CNN, self).__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.fc1 = nn.Linear(16 * 53 * 53, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 3)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.pool(x)
+        x = self.conv2(x)
+        x = self.pool(x)
+        x = x.view(-1, 16 * 53 * 53)
+        x = self.fc1(x)
+        x = self.fc2(x)
+        x = self.fc3(x)
+        return x
+
+```
+Then used batch_size = 8 and CrossEntropyLoss() for loss function. Then used Adam optimizer with a learning rate 0.001 for optimization process.
+
+
+
+
+
+
+
 ### Project Structure
 ```bash
 |
@@ -29,7 +78,8 @@ This model classifies animals among pandas, cats and dogs. It was trained using
 |       |---images(used for examples)
 |
 |---models
-|       |model_inception.h5
+|       |---cat_dog_cnn.pt
+|
 |---train(image dataset for training)
 |
 |---test(image dataset for testing)