Training and Analysis of Hyperparameters in Neural Networks for Computer Vision Applications: A Didactic Approach
Saulo Cardoso Barreto, Péter Tamás Szemes · 2022
Machine Learning models are known for having millions of parameters considering different applications, such as object recognition and self-driving cars. To avoid spending more time than necessary in the training stage and more importantly, to achieve a satisfactory model, the hyperparameters should be correctly defined. The main goal of the proposed project was to identify and evaluate the influence of hyperparameters in the training of neural networks for computer vision applications. Besides that, we aim to provide a didactic approach to analyze the acquired generalization capability of a model and easy visualization of the impact of different parameters during the training phase.