Celovita implementacija globokih nevronskih mrež za klasifikacijo slik na vgrajenih sistemih z uporabo TensorFlow Lite in PyTorch-ONNX

This thesis examines the end-to-end deployment of deep learning models for image classification on the STM32H747I-Discovery board, a resource-constrained embedded system. A detailed comparative analysis of two deployment workflows is provided: one using TensorFlow with its native TFLite format and another using PyTorch with ONNX as an intermediate representation. The study documents the process from model training and quantization to on-device inference and evaluates the trade-offs in memory footprint, latency, and development complexity. The results show that the TensorFlow-to-TFLite workflow is a more efficient and streamlined solution for this application. It yields a model with significantly lower RAM usage and faster inference speed. This demonstrates the benefits of a specialized deployment format over a generalized one for memory-critical applications.