In this tutorial, we build a vehicle classification model using VGG16 for feature extraction and XGBoost for classification! ️ It will based on Tensorflow and Keras What You’ll Learn : Part 1: We kick off by preparing our dataset, which consists of thousands of vehicle images across five categories. We demonstrate how to load and organize the training and validation data efficiently. Part 2: With our data in order, we delve into the feature extraction process using VGG16, a pre-trained convolutional neural network. We explain how to load the model, freeze its layers, and extract essential features from our images. These features will serve as the foundation for our classification model. Part 3: The heart of our classification system lies in XGBoost, a powerful gradient boosting algorithm. We walk you through the training process, from loading the extracted features to fitting our model to the data. By the end of this part, you’ll have a finely-tuned XGBoost classifier ready for predictions. Part 4: The moment of truth arrives as we put our classifier to the test. We load a test image, pass it through the VGG16 model to extract features, and then use our trained XGBoost model to predict the vehicle’s category. You’ll witness the prediction live on screen as we map the result back to a human-readable label. You can find link for the code in the blog : https://ko-fi.com/s/9bc3ded198 Full code description for Medium users : https://medium.com/@feitgemel/objec...assify-vehicles-using-tensorflow-76f866f50c84 You can find more tutorials, and join my newsletter here : https://eranfeit.net/ Check out our tutorial here : https://youtu.be/taJOpKa63RU&list=UULFTiWJJhaH6BviSWKLJUM9sg Enjoy Eran #Python #CNN #ImageClassification #VGG16FeatureExtraction #XGBoostClassifier #DeepLearningForImages #ImageClassificationPython #TransferLearningVGG16 #FeatureExtractionWithCNN #XGBoostImageRecognition #ComputerVisionPython
