Efficientnet-B0-based image classification: architecture, training, evaluation and visualization

This EfficientNet-B0 powered image classification model strives to achieve optimal accuracy while maintaining speed by utilizing an accuracy/cost-balanced convolution deep neural network (CNN). Implemented in PyTorch, the model automates classifying images into ‘Normal’ and ‘Text’ images through two-step transfer learning: pre-trained model fine-tuning and custom model fine-tuning. The dual refinement strategy allows the model to capture both domain space and general features to ensure perfect classification performance. Its performance evaluation attracted various metrics including accuracy, precision, recall, F1 score, and confusion matrixes, these various metrics offer an in depth awareness of the models predictive power an d the outcomes consistently show exceptional dependability across various data subset. The classifier achieved 99.98% accuracy on the validation subset, confirming the model’s practical applicability, overall this work confirms that the EfficientNet-B0 when its optimized through transfer learning can achieve a powerful binary classification tasks, not only that the resource usage is low with this build making it feasible for embedded systems and IoT devices.