Flowers classification using Transfer Learning

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Flowers classification using Transfer Learning

Classification of images plays an important role in sorting the images into classes based on their similarities. 

Recently, the demand for classifying images according to their features have shown great interest in many areas such as digital library, search engine, or any content-based image retrieval system with the advantage of advanced computer technologies.

However, some of the data used for image classification includes unnecessary information such as noise and the influence of the sun or light.

Hence, this study was performed to reduce the unnecessary data obtained during the feature extraction phase prior to classifying the images using a neural network.

In this study, flower image classification is based on low-level features such as color and texture to define and describe the image content.

This study analyzes the classification’s performance of the dataset using Convolutional neural network. Flowers classification using Transfer Learning

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