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Object Classification is one of the most significant tasks whose development is constantly growing in the field of deep learning research. The objective of this study is the development of neural architectures for the classification of images (of fruits and vegetables) contained within the Fruits-360 dataset. The methodological approach adopted …

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Advance Machine Learning Project: Image Classification for Fruits-360 dataset

Authors: Dario Della Mura - David Doci

Grazie al progetto di Advance Machine Learning si è potuto apprendere:

  1. come sviluppare una rete neurale convoluzionale da zero, gestendo la costruzione totale dell'architettura (il numero e le tipologie di layer, il numero di neuroni), tematiche come la standardizzazione, la normalizzazione e l'overfitting;
  2. applicazione di tecniche di Fature Extraction per l'addestramento di reti neurali pre-addestrate;
  3. appplicazione di tecniche di Transfer Learning e Data Augmentation tramite l'utilizzo di reti come VGG16

Thanks to the Advance Machine Learning project it was possible to learn:

  1. how to develop a convolutional neural network from scratch, managing the total construction of the architecture (the number and types of layers, the number of neurons), issues such as standardization, normalization and overfitting;
  2. application of Fature Extraction techniques for the training of pre-trained neural networks;
  3. Application of Transfer Learning and Data Augmentation techniques through the use of networks such as VGG16

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Object Classification is one of the most significant tasks whose development is constantly growing in the field of deep learning research. The objective of this study is the development of neural architectures for the classification of images (of fruits and vegetables) contained within the Fruits-360 dataset. The methodological approach adopted …

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