Create a Machine Learning Model without Coding with Google Teachable Machine

Table of Contents:

1. Introduction
2. Project Background
3. Building a Machine Learning Model without Coding
4. Collecting Training Images
5. Using Teachable Machine with Google
6. Setting up the Project
7. Uploading and Classifying the Images
8. Training the Model
9. testing the Model
10. Exporting the Model
11. Conclusion

Introduction

🎯 In this video, 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 is particularly Relevant in the time of the coronavirus pandemic, where personal hygiene and avoiding face touching are crucial preventive measures. We will be using Google's Teachable Machine to build the model without writing a single line of code. By the end of this video, you will have a working model capable of identifying whether an image contains a person touching their face or not.

Project Background

🧩 This project was inspired by a tweet from the founder of Kaos on Twitter. The tweet sparked the interest of many developers who wanted to contribute to the development of projects related to face detection. One such project is the work of Bike Race Kumar, who utilized the PI touch and Yolo v3 for his implementation. However, in this video, we will take a different approach, using Teachable Machine by Google to build our machine learning model.

Building a Machine Learning Model without Coding

🔧 Building a machine learning model can often be quite complex, requiring in-depth programming knowledge. However, thanks to tools like Teachable Machine, it is now possible to create sophisticated models without writing a single line of code. In this video, we will walk through the process of building a machine learning model that can detect whether someone is touching their face or not, using the power of computer vision.

Collecting Training Images

📷 Before we begin building our model, we need to Collect a set of images for training. These images will form the basis of our model's understanding of what constitutes a person touching their face and what does not. In this project, we have gathered a collection of training images from various sources, including images downloaded by Chris Comer and some by myself. It is important to note that for this demonstration, we have a limited number of training images. In a production setting, it is crucial to have a sufficient amount of diverse and representative training data.

Using Teachable Machine with Google

🖥️ Teaching a machine can be a challenging task, but Google's Teachable Machine simplifies the process by providing a user-friendly web interface. This platform allows users to train their own machine learning models and export them in various formats. Whether you need a TensorFlow Lite model for edge devices, a TensorFlow.js model for websites, or a standard TensorFlow model, Teachable Machine has got you covered. We will be leveraging the Teachable Machine web interface to build our face-touch detection model.

Setting up the Project

🔌 To get started with our project, we need to set up the environment in Teachable Machine. The Teachable Machine web interface requires us to have two classes: one for images where people are touching their faces and another for images where people are not touching their faces. We will create these classes and provide the respective images as training data.

Uploading and Classifying the Images

📂 In this step, we will upload the collected images to Teachable Machine and classify them into the appropriate classes. This step is crucial as it forms the foundation of our training process. We will organize our images into two categories: "face touch" and "no touch." Ensuring a clear distinction between these categories is essential for building an accurate and robust machine learning model.

Training the Model

🏋️ With our images uploaded and classified, we can now proceed to train our machine learning model. The training process involves exposing the model to our labeled datasets, allowing it to learn the Patterns and features that distinguish images of face-touching from those without. Teachable Machine simplifies this process, and training our model should only take a few minutes.

Testing the Model

🔍 After training our model, it is essential to evaluate its performance on unseen data. In this step, we will test our model's accuracy by providing it with images not used during the training process. By analyzing the output of our model, we can determine how well it can differentiate between images of face-touching and non-touching. We will examine different test images to get a holistic understanding of our model's performance.

Exporting the Model

💾 Once we are satisfied with the performance of our model, we can proceed to export the model. Teachable Machine offers several options for exporting the model, including downloading it locally or hosting it on the Teachable Machine website or cloud. We will explore these options and choose the one that best fits our use case and requirements.

Conclusion

🎉 In this video, we have explored the process of building a machine learning model to detect whether someone is touching their face or not. By utilizing Google's Teachable Machine, we were able to build a powerful model without writing any code. We discussed the importance of collecting training images, training the model, testing its performance, and exporting the final model. While this demonstration model serves an educational purpose, it is crucial to Gather more training data and fine-tune the model to achieve better accuracy before deploying it in real-world scenarios. Remember, during times of crisis like the COVID-19 pandemic, practicing good personal hygiene, avoiding face touching, and maintaining social distancing are paramount to staying healthy. Have fun exploring the possibilities of machine learning and continue to prioritize your well-being!

Highlights:

• Building a machine learning model without coding
• Utilizing Google's Teachable Machine for face-touch detection
• Collecting and organizing training images for optimal model performance
• Training the model using the Teachable Machine web interface
• Testing the accuracy and reliability of the trained model
• Exporting the model for future use
• Emphasizing the importance of personal hygiene and social distancing during the COVID-19 pandemic

FAQ

Q: Can Teachable Machine be used for other computer vision tasks? A: Absolutely! Teachable Machine is a versatile tool that can be used for various computer vision projects, including object recognition, gesture detection, and more.

Q: How can I improve the accuracy of my machine learning model? A: To improve model accuracy, consider increasing the size of your training dataset, fine-tuning hyperparameters such as learning rate and batch size, and ensuring a diverse and representative set of images for training.

Q: Can I use Teachable Machine to classify videos instead of images? A: Yes, Teachable Machine also supports video classification. By providing a labeled video dataset, you can train your model to classify different actions or behaviors captured in videos.

Q: Is Teachable Machine suitable for production-grade models? A: While Teachable Machine is a powerful tool for educational purposes and rapid prototyping, it may not be ideal for production-grade models that require extensive fine-tuning, scalability, and deployment considerations. For such cases, it is advisable to explore frameworks like TensorFlow and PyTorch.

Q: Are there any resources I can refer to for further learning? A: Absolutely! The following resources can be helpful for further learning and exploring the field of machine learning and computer vision:

• Teachable Machine website: https://teachablemachine.withgoogle.com/
• TensorFlow website: https://www.tensorflow.org/
• PyTorch website: https://pytorch.org/