This project was part of the Data Science Intensive program module 2 project which emerged the best. Find the certification here. This project's aim was to train a machine vision classifier that identifies five types of material classes for a large mining company in South Africa to allow automatic monitoring of the input bins for their funace. The image sampling system was already implemented and raw image data that is not classified was available. The purpose of this is to help the organization to reduce the material handling errors which leads to inefficiencies in the furnace and drives up production costs.
You can find the project report in this (notebook:)[https://github.com/CateGitau/Rock-Classifier/blob/main/rock_on_report.ipynb]
To get this project up and running in your machine, follow the steps below:
- Clone this repository to your local machine by opening your terminal and typing:
git clone https://github.com/CateGitau/Rock-Classifier
- install the required packages:
pip3 install -r requirements.txt
- Run the app.py file to get the project running in your local machine using Streamlit
streamlit run app.py
We'd like to thank Emmanuel Sekyi who was our Tutor for the duration of this project.