diff --git a/examples/image_classification/mnist/src/main.rs b/examples/image_classification/mnist/src/main.rs
index 1111673..ba7e310 100644
--- a/examples/image_classification/mnist/src/main.rs
+++ b/examples/image_classification/mnist/src/main.rs
@@ -6,15 +6,18 @@ use deltaml::data::MnistDataset;
 use deltaml::losses::SparseCategoricalCrossEntropyLoss;
 use deltaml::neuralnet::Sequential;
 use deltaml::neuralnet::{Dense, Flatten};
+use deltaml::neuralnet::layers::{Conv2D, MaxPooling2D};
 use deltaml::optimizers::Adam;
 
 #[tokio::main]
 async fn main() {
     // Create a neural network
     let mut model = Sequential::new()
-        .add(Flatten::new(Shape::new(vec![28, 28]))) // Input: 28x28, Output: 784
-        .add(Dense::new(128, Some(ReluActivation::new()), true)) // Input: 784, Output: 128
-        .add(Dense::new(10, None::<SoftmaxActivation>, false)); // Output: 10 classes
+        .add(Conv2D::new(32, 3, 1, 1, Some(Box::new(ReluActivation::new())), true)) // Conv2D layer with 32 filters, kernel size 3x3
+        .add(MaxPooling2D::new(2, 2)) // MaxPooling2D layer with pool size 2x2
+        .add(Flatten::new(Shape::new(vec![28, 28, 32]))) // Flatten layer
+        .add(Dense::new(128, Some(ReluActivation::new()), true)) // Dense layer with 128 units
+        .add(Dense::new(10, None::<SoftmaxActivation>, false)); // Output layer with 10 classes
 
     // Display the model summary
     model.summary();