Fruits Freshness Classification using Deep Learning Python Project

Fruits Freshness Classification using Deep Learning Python Project is a web application, implemented with Python (Flask framework), which uses a convolutional neural network on the back-end to perform fruit classification. The system is able to distinguish 6 classes of fruits: fresh/rotten apples, fresh/rotten oranges and fresh/rotten bananas. The user is able to interact with the app by uploading images or by showing the fruits to the web-camera. The app uses Web Speech API to make the experience more interactive and fun.
Warning!
As there's no fallback class like "a non-fruit object", please don't take it personally when the model classifies the photo of you as a rotten banana 😅 (this also applies to any object, that doesn't belong to the mentioned classes).
Dependencies
For this project, the following tools were used:
Tensorflow 2
for building and training the model;
Numpy
for working with arrays;
Matplotlib
for visualizing the data;
Flask
for implementing the server side;
HTML5, CSS3, JavaScript (with Web Speech API and
particles.js
) on the front-end.
Dataset for training
The dataset used for training and evaluating the model:
Fruits fresh and rotten for classification
by Sriram Reddy Kalluri. The obtained model has achieved 99% accuracy on the test set.
Network implementation
The network itself was implemented using
transfer learning
. The MobileNet V2 model developed at Google was used as a base model for feature extraction from our data. A custom classification layer was added on top and trained separately.
Installation
To install and run locally in a production mode:
cmd-1
- pip install -r requirements.txt --user
cmd-2
- python app.py
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