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A version of a CNN used to classify fruits on the fruits-360 dataset (Tensorflow 1.15.0)

Reference for the CNN: https://arxiv.org/abs/1703.08472

cnn_saver.py:

The file that creates the neural network, and trains it. Once the relevant paths are specified, will also save the variables, model, and other info.

tester.py:

The file that tests the neural network. Once the relevant paths are specified, will retrieve the trained network variables and other testing info.

path_changes.txt:

Consists of all the line numbers in different files where you need to specify a path of your own choice. (What the path is used for has also been described)

tfdl_env.yml

Consists of all the dependencies that you need to install in order to run the network. This .yml file can be used to create a separate conda environment different from the 'base' conda environment. Use the folowing code: conda env create -f tfdl_env.yml

fruits-360 folder

Consists of a sample Training class of images. To download the entire dataset, go to the following link: https://www.kaggle.com/moltean/fruits (Note:- I ignored the following classes in training and testing: Mangostan, Pear Kaiser, Tomato Maroon You can involve them too, but you will have to do some dataset-related modifications)

dataset_rel_changes.txt

Contains required Dataset Related Modifications: (After removing the 3 classes mentioned above, there were 52,200 images in training set total. For each epoch, 200 of these images were used, picked at different indices 2 images each 522 spaces apart) Any dataset modification will require a number of changes in the code of cnn_saver.py.

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A version of a CNN used to classify fruits on the fruits-360 dataset (Tensorflow 1.15.0)

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