fruit_image_classification

The repository contains 4 Jupyter notebooks for the 4 experiments performed. All experimentsare performed using the Fruits 360 dataset which is available at https://github.com/Horea94/Fruit-Images-Dataset.

The following are the 4 Jupyter notebooks and a brief description of their contents:

1. fruit_images_classification_using data augmentation5.ipynb:

This notebook contains the first experiment where a self-designed 14-layer convolutional neural net is used for fruit image classification.

2. fruit_images_classification_using_inception v3.ipynb:

This notebook contains the second experiment where a CNN based on transfer learning using Inception-v3 model is exploited for fruit image classification.

3. fruit_image_classification_using vgg16_transfer learning_data augmentation4.ipynb

This notebook contains the third experiment where a CNN based on transfer learning using VGG16 model is exploited for fruit image classification.

4. fruit_image_classification_using vgg16_fine tuning_data augmentation_RMSprop3.ipynb

This notebook contains the fourth experiment where a CNN based on fine tuning using VGG16 model is exploited for fruit image classification.