Fruit Classification with Tensorflow

Goal:

• To implement a Convolutional Neural Network in Tensorflow which can accurately disntinguish fruits from each other
• To undergo an incremental devleopment cycle in building the model, starting with a few classes and building upwards

Results:

• Final model trained to classify 40 fruits. Successful with 92.47% Accuracy
• Structure of model and statistics about it's success during each step of the design process are in changelog.txt

Data set used: fruits-360 dataset from Horea Muresan, Mihai Oltean, Fruit recognition from images using deep learning, Technical Report, Babes-Bolyai University, 2017