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model_card.md

@@ -5,21 +5,21 @@ This model is a Convolutional Neural Network (CNN) trained to classify images of
 
 ## Model Architecture
 
-- 3 Convolutional layers with ReLU activation and Batch Normalization
+- 4 Convolutional layers with ReLU activation and Batch Normalization
 - MaxPooling layers after each convolutional layer
 - Dropout layer after the second convolutional layer
-- Global Average Pooling layer
-- Fully connected layer with 256 units and ReLU activation
+- Flatten layer
+- Fully connected layer with 512 units and ReLU activation
 - Output layer with softmax activation for 2 classes (cats and dogs)
 
 ## Training
 
-The model was trained using the Adam optimizer with a learning rate of 0.001 and Sparse Categorical Crossentropy loss. It was trained for 5 epochs with a batch size of 32.
+The model was trained using the Adam optimizer with a learning rate of 0.001 and Sparse Categorical Crossentropy loss. It was trained for 20 epochs with a batch size of 64. Data augmentation was also used.
 
 ## Performance
 
-The model achieved an accuracy of X% on the validation set.
+(Add your model's performance metrics here after training)
 
 ## Usage
 
-To use this model, you can load it using TensorFlow and make predictions on new images of cats and dogs.
+To use this model, you can load it using TensorFlow/Keras and make predictions on new images of cats and dogs.