This repository contains source code and documentation for EksFlorasi's machine learning development.
- Machine Learning:
- M038DSX0335 - Jason Andrew Gunawan
- M038DSX0340 - James Rafferty Lee
- M172DSX1831 - Mohammad Azri Harahap
- Cloud Computing:
- C303DSY0829 - Calista Chandra
- C016DSX2599 - Sholeh Rodhi Putra Siswantoro
- Mobile Development
- A038DSX1146 - Frans Wijaya
EksFlorasi is using two machine learning models, to classify images for Flora and Fauna. This section will explain how our team develop EksFlorasi's machine learning models.
We are using Python version 3.91 for this project.
Clone or download this repository.
git clone https://github.com/EksFlorasi/machine-learning.git
Go to the project directory.
cd machine-learning
Install all required packages.
pip install -r requirements.txt
Go to notebooks directory.
cd notebooks
There are four notebooks2 of interest:
modeling/dataset-preparation.ipynb
to prepare our datasetsmodeling/fauna-transfer.ipynb
to develop a fauna modelmodeling/flowers16-transfer.ipynb
to develop a flora modelinference-test.ipynb
to test our models
EksFlorasi deploys its models alongside its backend server using JavaScript. This section will explain how we use TensorFlow in JavaScript on our project.
We are using Node.js version 16.203 for this project.
Go to the deployment directory from the root directory.
cd deployment/image-recognition-js
Install all required packages.
npm install
Run the script after providing your models4.
node test-import.js
Footnotes
-
Python 3.9: https://www.python.org/downloads/release/python-390/ ↩
-
Each notebook has guides explaining the code and what they do. Should there be an error, it's most likely due to file paths. ↩
-
Node.js 16.20: https://nodejs.org/download/release/v16.20.0/ ↩
-
Due to size limitations, we have uploaded our models on Google Drive. After downloading the models, update the modelPath variable in
image-classification.js
based off of your model location. ↩