diff --git a/examples/alexnet/alexnet.cpp b/examples/alexnet/alexnet.cpp
index e28787e..65effff 100644
--- a/examples/alexnet/alexnet.cpp
+++ b/examples/alexnet/alexnet.cpp
@@ -4,15 +4,30 @@
 #include <vector>
 
 std::vector<float>
-readAndNormalizeImage(const std::string &imagePath, int width, int height) {
+readAndNormalizeImage(const std::string &imagePath, int resizeSize, int cropSize) {
     // Read the image using OpenCV
     cv::Mat image = cv::imread(imagePath, cv::IMREAD_COLOR);
+    // Convert the image from BGR to RGB
+    cv::cvtColor(image, image, cv::COLOR_BGR2RGB);
 
-    // Resize and normalize the image
-    cv::resize(image, image, cv::Size(width, height));
+    // Calculate the scaling factor
+    double scale = std::max(static_cast<double>(resizeSize) / image.cols, static_cast<double>(resizeSize) / image.rows);
+
+    // Resize the image
+    cv::Mat resized;
+    cv::resize(image, resized, cv::Size(), scale, scale, cv::INTER_AREA);
+
+    // Calculate the cropping coordinates
+    int x = (resized.cols - cropSize) / 2;
+    int y = (resized.rows - cropSize) / 2;
+
+    // Perform center cropping
+    cv::Rect roi(x, y, cropSize, cropSize);
+    image = resized(roi);
+
+    // Normalize the image
     image.convertTo(image, CV_32FC3, 1.0 / 255.0);
 
-    // Normalize the image https://pytorch.org/hub/pytorch_vision_alexnet/
     cv::Mat mean(image.size(), CV_32FC3, cv::Scalar(0.485, 0.456, 0.406));
     cv::Mat std(image.size(), CV_32FC3, cv::Scalar(0.229, 0.224, 0.225));
     cv::subtract(image, mean, image);
@@ -124,7 +139,7 @@ int main(int argc, const char *const argv[]) {
 
     // Read and normalize the image
     std::vector<float> imageData =
-        readAndNormalizeImage(imagePath, inputSize.first, inputSize.second);
+        readAndNormalizeImage(imagePath, inputSize.first, inputSize.first);
 
     // Print the size of the image data
     const float *output = model->predict(imageData.data());
diff --git a/examples/alexnet/alexnet.py b/examples/alexnet/alexnet.py
index bcd0a36..73c2a68 100644
--- a/examples/alexnet/alexnet.py
+++ b/examples/alexnet/alexnet.py
@@ -3,11 +3,17 @@ import sys
 import torchvision
 
 sys.path.append('../../tools')  # Ugly hack 
-from utils import export_model_weights, print_model_parameters
+from utils import export_model_weights, print_model_parameters, predict
 
 if __name__ == "__main__":
-    alexnet = torchvision.models.alexnet(weights=torchvision.models.AlexNet_Weights.DEFAULT)
-    print_model_parameters(alexnet)  # print layer names and number of parameters
-    export_model_weights(alexnet, 'alexnet_weights.bin')
-    # predict(alexnet, 'cat.jpg')
+
+    weights = torchvision.models.AlexNet_Weights.DEFAULT
+    alexnet = torchvision.models.alexnet(weights=weights)
+
+    # print_model_parameters(alexnet)  # print layer names and number of parameters
+    export_model_weights(alexnet, 'alexnet_weights.bin')
+    
+    # class_labels = weights.meta["categories"]
+    # prediction = predict(alexnet, "margot.jpg")
+    # print(prediction, class_labels[prediction])