Classifying fruits using a tensorflow convolutional neural network
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fruits-360
.gitignore
README.md
changelog.txt
classifier.py
dataset.py

README.md

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