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Design of Accuracy Predictors for Convolutional Neural Networks
Šmída, Šimon ; Mrázek, Vojtěch (oponent) ; Sekanina, Lukáš (vedoucí práce)
The aim of this thesis is to present a method of constructing accuracy predictors for convolutional neural networks (CNNs) by leveraging databases of trained CNNs (NAS-Bench-101) and employing machine learning (ML) techniques as performance estimation strategies. The study begins with a description of various ML methods used in building CNN accuracy predictors, followed by an in-depth examination of CNNs and databases of pre-trained CNNs. The proposed method involves selecting a suitable task for the CNNs (image classification), assembling a dataset, defining relevant features for the predictor input, and choosing five ML methods for training the predictors. Using existing libraries, the accuracy predictors are implemented, trained, and experimentally validated to assess their functionality and performance. The results are thoroughly evaluated, providing insights into the effectiveness of the proposed method and the potential for further refinement in the field of CNN accuracy prediction.
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