https://github.com/alkasaliss/whichflower

# whichFlower : a flower species recognition app using tensorflow/keras and React-Native
https://github.com/alkasaliss/whichflower

computer-vision flower-classification keras react-native tensorflow

Last synced: about 2 months ago
JSON representation

# whichFlower : a flower species recognition app using tensorflow/keras and React-Native

• Host: GitHub
• URL: https://github.com/alkasaliss/whichflower
• Owner: AlkaSaliss
• Created: 2019-07-19T09:24:56.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2023-01-04T13:07:45.000Z (about 2 years ago)
• Last Synced: 2023-03-02T15:37:29.034Z (almost 2 years ago)
• Topics: computer-vision, flower-classification, keras, react-native, tensorflow
• Language: Jupyter Notebook
• Homepage: https://alkasaliss.github.io/whichFlower/
• Size: 46.1 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 12
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# whichFlower : a flower species recognition app using tensorflow/keras and React-Native



As a passionate person about computer vision (CV), I came to know that model deployment is also important in model development process because the usefulness of a model is measured by the satisfaction of end users. In a previous project I named DEmoClassi (Demographic (age, gender race) and Emotion (happy, neutral, angry, ...) Classification) I tried to turned my trained models in a standalone python module that can be run on windows/Linux using OpenCV. [You can check it here](https://github.com/AlkaSaliss/DEmoClassi).

In this new project I decided to give mobile technologies a try. Today the models are migrating more and more to the edge devices (mobile, sensors, ... IOT in general). So I started by learning React-Native, a cross-platform mobile development framework developed by Facebook. The course [is available on youtube](https://www.youtube.com/playlist?list=PLhQjrBD2T382gdfveyad09Ierl_3Jh_wR), it is a little bit long, but it worth learning it.

The end goal for me was to combine my 2 passions, CV and programming into another project : this time I opted for CV model training and deployment on mobile device of a flower species recognition app I called, with no suspens,  `WhichFlower`.

Below is a short animated demo showing the prediction process using the app : 

[Here is the link](https://alkasaliss.github.io/whichFlower/) describing my approach from exploratory data analysis to model deployment in the app.