So, you’ve learned Python and many of the libraries like NumPy, Pandas, Matplotlib, Seaborn, scikit-learn and you’re ready to start building ML projects but you don’t have an idea about where to start and how to turn data into models. In that case, this article has you covered. Here, we will be going over some of the basic steps we should cover to build a model that generalizes well to unseen data.
Here, we will explore the following topics:
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.
Please note that I am no longer maintaining this project because of my busy schedule. Breaking changes introduced in the recent version of Flutter and dependencies may break the app. But you’re free to fork the repo and change things your way…
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.