Bureaucrats, dave_user, Administrators
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→Reference application #1: fruit classifier
The dataset was created collecting 240 images of 6 different fruits. 75% of the images were used for the training (''training dataset'') and the rest was used for test/validation purposes (''test dataset'', ''validation dataset''). Training the model with a greater number of images would have led to better accuracy, but '''it wouldn't have changed the inference time'''.
Several measures were taken to counter the high overfitting tendency due to the small number of images. For instance, new images were synthesized from the existing ones to simulate a larger dataset(''data augmentation''), as shown below:
The following plots show the training history:
[[File:Keras loss history.png|nonethumb|thumbcenter|400x400px600px|Variation of the loss (blue) and the validation loss (orange) through the epochs during training]] [[File:Keras acc history.png|none|thumb|400x400px600px|Variation of the accuracy (blue) and the validation accuracy (orange) through the epochs during training]]
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