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Deep Learning for Food Image Classification by dtfuvihyv6b is a document available to read on EtoBox.

What is Deep Learning for Food Image Classification about?

This paper discusses automatic food image classification using deep learning techniques, specifically SqueezeNet and VGG-16 models. SqueezeNet achieved an accuracy of 77.20% with a smaller model size, while VGG-16, being deeper with more parameters, outperformed it with an accuracy of 85.07%. Data augmentation and hyperparameter tuning were utilized to enhance the performance of these networks.

Author

dtfuvihyv6b

Language

EN