Welcome to our tutorial on object classification using VGG16 and XGBoost! 🚗📸

Part 1: Data Preparation

Load a dataset of vehicle images divided into five categories. Read and resize the images. Prepare training and validation data. Save the data to numpy arrays.

Part 2: Data Loading and Preprocessing

Load the previously saved training and validation data. Encode text labels to integers using LabelEncoder. Normalize pixel values. Load the VGG16 model without the fully connected layer.

Part 3: Feature Extraction with VGG16

Extract features from the VGG16 model for the training data. Reshape the features. Save the features and the VGG16 model.

Part 4 : Train an XGBoost classifier using the extracted features.

Part 5: Testing the Model

Load test data. Load the trained XGBoost model. Load the VGG16 model. Choose a random test image. Extract VGG16 features for the test image. Use XGBoost to predict the class. Display the predicted class on the image.

Code for this video: https://github.com/feitgemel/TensorFlowProjects/tree/master/Vehichles%20classification%20-%20XGBoost%20and%20VGG16

00:00 Intro !!!!!!!
00:20 Download and prepare the data
08:23 Feature Extraction with VGG16
20:30 Train an XGBoost classifier using the extracted features.
23:50 Test the model 

 ~~~~~~~~~~~~~~~ recommended courses and books ~~~~~~~~~~~~~~~
A perfect course for learning modern Computer Vision with deep dive in TensorFlow , Keras and Pytorch . You can find it here : http://bit.ly/3HeDy1V 
I also recommend this book, https://amzn.to/44GnlLW : "Make Your Own Neural Network - An In-depth Visual Introduction For Beginners ".
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