Image classification in Galaxy with fruit 360 dataset

<p>Credit: &#39;Fruit recognition from images using deep learning&#39;&nbsp;by H. Muresan and M. Oltean (<a href="https://arxiv.org/abs/1712.00580">https://arxiv.org/abs/1712.00580</a>)<br> <br> Fruit 360 is a dataset with 90380 images of 131 fruits and vegetables (<a href="https://www.kaggle.com/moltean/fruits">https://www.kaggle.com/moltean/fruits</a>). Images are 100 pixel by 100 pixel and are RGB (color) images (3 values for each pixel). This dataset is a subset of Fruit 360 dataset, containing only 10 fruits/vegetables (Strawberry, Apple_Red_Delicious, Pepper_Green, Corn, Banana, Tomato_1, Potato_White, Pineapple, Orange, and Peach). We selected a subset of fruits/vegetables, so the dataset size is smaller and the neural network can be trained faster.</p> <p>&nbsp;</p> <p>The utilities used to create the dataset, along with step by step instructions, can be found here:&nbsp;https://github.com/kxk302/fruit_dataset_utilities<br> <br> First, we created feature vectors for each image. Each image is 100 pixel by pixel&nbsp;and are RGB (color) images (3 values for each pixel). Hence, each image can be represented by 30,000 values (100 X 100 X 3). Second, we selected a subset of 10 fruits/vegetables images (training and test dataset sizes go from 7&nbsp;GB and 2.5 GB for 131 fruits/vegetables to 500 MB and 177 MB for 10 fruits/vegetables, respectively). Third, we created separate files for feature vectors and labels. Finally, we mapped the labels for the 10 selected fruits/vegetables to a range of 0 to 9.</p> <p>&nbsp; &nbsp;</p>