Build a No-Code AI Solution with Google Teachable Machine

Table of Contents:

1. Introduction
2. Background
3. Project Inspiration
4. Project Overview
5. Machine Learning Model Without Programming
6. Teachable Machine by Google
7. Training Dataset
8. Building the Model
9. testing the Model
10. Fine-Tuning the Model
11. Exporting the Model
12. Conclusion

Introduction

In this article, we will explore how to create a machine learning model using computer vision to detect whether someone is touching their face or not. This project has become particularly Relevant during the time of the coronavirus pandemic, as avoiding face touching is a crucial precaution. We will be using the Teachable Machine web interface by Google to build our model, without the need for any programming. Let's dive into the details and see how we can accomplish this without writing a single line of code.

Detecting Face Touch using Machine Learning and Computer Vision

Introduction

In the midst of the global pandemic caused by the coronavirus, it has become essential to adopt precautionary measures to prevent the spread of the virus. One such measure is avoiding touching one's face, as the hands can come into contact with contaminated surfaces. To address this issue, we will learn how to create a machine learning model using computer vision techniques to detect whether someone is touching their face or not. This project has gained significant attention and was inspired by the founder of Kaos on Twitter. We will be using a combination of PI touch and Yolo v3, and the training images have been graciously collected by Krazee Kumar. The best part is that we will be able to build this machine learning model without programming, thanks to the Teachable Machine project developed by Google. So, let's get started and see how we can create an effective model that can detect face touching through images.

Background

The outbreak of the coronavirus has prompted a worldwide health crisis, and people have been advised to follow various preventive measures to contain its spread. Among these measures, avoiding touching the face has garnered significant attention. Since the hands are known to come into contact with potentially contaminated surfaces, touching the face can facilitate the transmission of the virus into the body. To address this issue, we can leverage the power of machine learning and computer vision to develop a model that can detect instances of face touching. By employing Image Recognition algorithms, we can analyze images and determine whether a person is touching their face or not. In this article, we will explore the process of building such a model using the Teachable Machine project by Google.

Project Inspiration

The idea behind this project originated from a tweet by the founder of Kaos on Twitter. This tweet sparked inspiration among many developers and led to the creation of various projects aimed at detecting face touching. One such project that caught my attention was the work of Krazee Kumar, who based his project on PI touch and Yolo v3. His efforts in collecting the training images have been commendable, and they serve as a valuable resource for our project. It is through the collaboration and sharing of ideas that we can make significant progress in developing innovative solutions during times of crisis.

Project Overview

In this project, we will utilize the Teachable Machine web interface provided by Google to create a machine learning model capable of detecting whether an individual is touching their face or not. The web interface allows us to train our model using a dataset of images, eliminating the need for writing complex code. We will be using the training images collected by Krazee Kumar, along with additional images sourced from Google. While the current setup focuses on analyzing images, it is important to note that the same concept can be applied to videos by utilizing the corresponding training datasets. So, let's dive into the details and start building our machine learning model step by step.

Machine Learning Model Without Programming

One of the remarkable aspects of this project is that we can build a machine learning model without having to write a single line of code. Thanks to the Teachable Machine project by Google, we have access to a user-friendly web interface that simplifies the entire process. While traditional machine learning models often require expertise in programming languages such as Python, this approach allows individuals with no coding experience to embark on their machine learning journey. By leveraging the capabilities of the Teachable Machine web interface, we will create a robust model capable of detecting face touching accurately.

Teachable Machine by Google

Teachable Machine is an innovative project developed by Google that allows users to train a machine learning model for various applications. The project aims to bridge the gap between the complexity of machine learning and the accessibility to the general public. With Teachable Machine, users can train models without the need for extensive coding knowledge, making it an ideal platform for beginners. It provides different output model options, including TensorFlow Lite for edge devices, TensorFlow.js for websites, and regular TensorFlow files. This versatility ensures that the trained model can be deployed in various environments, depending on the specific requirements.

Training Dataset

Creating a reliable machine learning model requires a well-curated training dataset. In this project, we will utilize two sets of images: one set containing images of people touching their faces and another set with images of people not touching their faces. While the dataset used in this demonstration is relatively small, it is important to note that for production-level models, a more extensive and diverse dataset is necessary. When deploying the model on a larger Scale, ensuring an adequate training dataset will contribute to improved accuracy and performance. So, for demonstration purposes, let's proceed with the available dataset and begin the model-building process.

Building the Model

To build our machine learning model, we will start by organizing the images into two classes: "Face Touch" and "No touch." These classes represent instances where individuals are touching their faces and instances where they are not, respectively. We will then proceed to upload our training images to the Teachable Machine web interface. The web interface provides options for uploading images from various sources, including local storage and Google Drive. Once the images are uploaded, we can begin the model training process. The training phase should take a relatively short amount of time, typically lasting a few minutes. During this process, the model will learn to distinguish between images of face touching and images of no touch.

Testing the Model

After successfully training our machine learning model, we can proceed to test its accuracy. The Teachable Machine web interface offers a testing feature that allows us to upload images not used in the training process and obtain predictions. It is important to note that the testing images should be separate from the training images to ensure unbiased results. By uploading images of individuals touching their faces and images of individuals not touching their faces, we can evaluate how accurately our model can classify them. The testing process provides insights into the model's performance and allows us to make further adjustments if necessary.

Fine-Tuning the Model

During the testing phase, it is possible to identify scenarios where the model may misclassify images or exhibit subpar performance. In such cases, fine-tuning the model becomes crucial. Teachable Machine provides options for hyperparameter tuning, such as adjusting the learning rate, batch size, and other parameters, which can help improve the model's accuracy and reliability. Fine-tuning the model involves iteratively adjusting these parameters to achieve desirable results. By carefully calibrating the model and continuously testing it with a diverse range of images, we can enhance its performance and ensure its effectiveness in real-world scenarios.

Exporting the Model

Once we are satisfied with the performance of our machine learning model, we can proceed to export it for further use. Teachable Machine offers multiple export options to cater to different deployment scenarios. One option is to download the model locally, which provides a wide range of file formats, such as TensorFlow, JSON, and more. These formats enable integration with other applications or frameworks, allowing developers to leverage the trained model in their projects. Additionally, Teachable Machine offers the ability to host the model on its website, which is useful for cloud-based deployments or sharing the model with others.

Conclusion

In conclusion, this article has demonstrated how to create a machine learning model using computer vision techniques to detect instances of face touching. By leveraging the Teachable Machine web interface by Google, we were able to build a robust model without the need for programming. We explored the importance of training datasets, the model-building process, testing the accuracy, and fine-tuning the model for better performance. Additionally, we discussed the various export options available to users, enabling the integration of the trained model into different applications. Moving forward, it is essential to continuously improve the model by gathering more training data and fine-tuning the hyperparameters. By staying vigilant and adopting innovative solutions like this, we can effectively combat the challenges posed by the coronavirus pandemic.

Highlights

• Create a machine learning model to detect face touching without programming.
• Utilize the Teachable Machine web interface by Google for model development.
• Train the model using a dataset of images collected for face touching detection.
• Test the accuracy of the model using separate testing images.
• Fine-tune the model to improve its performance and reliability.
• Export the model for integration into different applications or hosting on the web.

FAQ

Q: Can this model be used to detect face touching in real-time videos? A: Yes, while the demonstration focuses on images, the same principles can be applied to videos by utilizing the corresponding training datasets.

Q: Is a large training dataset necessary for accurate results? A: While a larger and more diverse training dataset generally improves accuracy, the current dataset can be used for demonstration purposes. However, in a real-world scenario or production-level model, a more extensive training dataset is recommended.

Q: Can the model be deployed on edge devices or websites? A: Yes, the Teachable Machine project offers export options for deploying the model on edge devices using TensorFlow Lite, for website integration using TensorFlow.js, and for general use with regular TensorFlow files.

Q: How can I fine-tune the model for better performance? A: Teachable Machine provides options for adjusting hyperparameters such as the learning rate and batch size, which can help improve the model's accuracy. By iteratively fine-tuning these parameters and testing the model with a diverse range of images, its performance can be enhanced.

Q: Can the model be integrated with other frameworks or applications? A: Yes, the model can be exported in various formats, such as TensorFlow and JSON, allowing integration with other frameworks and applications. This flexibility enables developers to utilize the trained model based on their specific requirements.

Resources

• Teachable Machine by Google: https://teachablemachine.withgoogle.com/