How to Train an EfficientNet Model with a Custom Dataset

Jacob Solawetz provides a comprehensive tutorial on how to train the EfficientNet convolutional neural network using a custom dataset for image classification. EfficientNet, developed by Google Brain, is renowned for its ability to efficiently scale convolutional neural networks, making it highly effective for classification tasks. The tutorial, hosted on Google Colab, focuses on classifying rock, paper, scissors hand gestures but is adaptable to any classification type with proper dataset supervision. Using the Keras framework, the tutorial guides users through setting up their training environment, selecting the appropriate EfficientNet model variant, and preparing their dataset using tools like Roboflow for preprocessing and augmentation. It emphasizes the importance of customizing the model architecture to fit the number of classes in the dataset and provides insights on monitoring training progress through metrics like accuracy and loss. The tutorial also covers fine-tuning, inference testing, and saving model weights for future use, ultimately equipping readers with the skills to leverage EfficientNet for various image classification applications.