In this guide, we will be developing an application in Flutter using the tflite package and a pre-trained SSD-MobileNet model, capable of detecting objects in images and real-time camera stream. This application is capable of detecting objects offline. We will also be able to take pictures from within the app and feed it to the model for detection.
Setup Flutter on your machine if you haven’t already. This guide in the Flutter website is a great place to start. After setting up Flutter, create a new project by typing the following in your terminal.
$ flutter create object_detection
In this article, I am going to share my experience learning and building a simple application in Flutter. What I made is a dashboard application for the Covid-19 statistics of all the countries. I started learning Flutter in a Sunday morning, and by Saturday evening, I was building the release apk on my machine.
You can look at the project on my GitHub. Any changes and suggestions are welcome and appreciated. You can also contribute to make it better.
You can download the apk from here.
Built using Streamlit and Python.
After spending some time looking at Deep Learning with TensorFlow and training some models on my own, I decided to make the model meaningful. Making a model and training it is one thing, but deploying it and creating something that even people with no experience can work with is another.
You can learn more about Streamlit on their website. …