Overview / Usage

About the project,

I Trained a Computer🖥️ to recognize a [HUMAN], [MOBILE PHONE], [BALL], [BOTTLE], controlled by webcam image using pre-trained neural network models. .I train an image classifier and import the machine learning model into a p5.js sketch with the ml5.js library. I pre-trained the model with 500 photos and store the model in the cloud.

Problems Solved

The project is a web application and I will convert it into an android app(i am on it) !! so that when the camera(in the android phone) points to an object it displays the name or tells about the object. it can be very helpful to blind people and also in various other situations !!

how this work is used in production?

There are so many amazing ways This project is used behind the scenes to impact our everyday lives:-

• Self-Driving Cars and Automated Transportation( It’s based on the same fundamentals of the above model to recognise the obstacles on the road. You have input features (i.e. the real-time visual and sensor data) and an output (i.e. a decision among the universe of possible next “actions” for a car).
• **Enhanced Health Care **The Model can be programmed to read human emotions, develop its own, and help its human friends stay happy. As funny as it sounds, the day that one can literally “buy a friend” is not too far away.

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Methodology / Approach

Tested-Marjoram is an interactive web Application powered Teachable Machine 2 and p5.js

The idea is that the machine can recognize real-world objects. By training an image classifier and import the machine learning model into a p5.js sketch with the ml5.js library.

Teachable Machine is a web-based tool that makes creating machine learning models fast, easy, and accessible to everyone. Teachable Machine uses Tensorflow.js, a library for machine learning in Javascript, to train and run the models you make in your web browser.

Technologies Used

• ml5.js
• p5.js
• Tensorflow.js
• Visual studio code
• JavaScript

Repository

https://github.com/ayushman17/Tested-Marjoram

Other links

• my project video
• my project (in action)