Home AI News Empower Children with Teachable Machine: Learn AI through Fun Activities!

Empower Children with Teachable Machine: Learn AI through Fun Activities!

Introduction
Getting Started with Teachable Machine
- 2.1 Searching and opening Teachable Machine
- 2.2 Starting a new image project
Labeling and Recording Images
- 3.1 Labeling the classes
- 3.2 Recording images with the webcam
Training the Model
- 4.1 Uploading and training the data
- 4.2 Analyzing the detection percentages
testing the Model with Different Objects
- 5.1 Using a Boston Red Sox hat
- 5.2 Using an object from the face detection activity
Evaluating the System and Suggesting Improvements
- 6.1 Taking notes and analyzing results
- 6.2 Identifying pros and cons
- 6.3 Matrix of tested samples and objects
- 6.4 Suggestions for improvement

👉 Introduction

Teachable Machine is an exciting web-based tool that allows users to create machine learning models without any coding knowledge. In this article, we will guide you through the process of getting started with Teachable Machine. We will cover how to open the application, create a new image project, label and Record images, train the model, and test its accuracy with different objects. Additionally, we will discuss the system's limitations and suggest improvements for future iterations.

👉 Getting Started with Teachable Machine

2.1 Searching and Opening Teachable Machine

To begin, open your preferred browser and navigate to Google. Type "Teachable Machine" into the search bar. As you type, you will Notice that Teachable Machine is the first suggestion provided. Click on this suggestion or search for "teachablemachine.withgoogle.com" directly. This will take you to the official website of Teachable Machine.

2.2 Starting a New Image Project

Upon reaching the Teachable Machine website, you will be greeted with an opening screen. Click on the blue "Get started" button to initiate the process. Next, select the option to start a new image project. For most cases, the standard image model is recommended as it provides optimal results.

👉 Labeling and Recording Images

3.1 Labeling the Classes

Once you have started a new image project, it's time to label the classes. In this example, we have two stuffed animals, a kitty cat, and a chicken. Label the first class as "Mr. Kitty" and the Second class as "Mr. Chicken". This labeling will help the model differentiate between the two objects during training.

3.2 Recording Images with the Webcam

To train the model effectively, we need a diverse set of images for each class. Click on the "webcam" option for the first class, "Mr. Kitty". Allow the webcam to start, and then hold the "record" button. While recording, move the kitty to different corners of the camera, varying the angles and orientations. Aim to capture around 100 images to ensure a robust training dataset. Repeat the same process for the second class, "Mr. Chicken".

👉 Training the Model

4.1 Uploading and Training the Data

After recording the images for both classes, it's time to train the machine learning model. Click on the blue button in the center called "Train Model". The data will be uploaded to a server, where the training process will take place. It's important not to switch tabs during this process to avoid any potential data loss.

4.2 Analyzing the Detection Percentages

Once the training is complete, the model will showcase its detection capabilities. Place the objects within the camera's field of view to observe the detection percentages. Notice how the model identifies "Mr. Kitty" and "Mr. Chicken" separately. However, when both objects are Present, the model may struggle to determine which one is in focus. The detection percentages will fluctuate as you move the objects around, emphasizing the need for further evaluation.

👉 Testing the Model with Different Objects

5.1 Using a Boston Red Sox Hat

To examine the model's accuracy with different objects, introduce a test object that was not part of the training samples. In this case, a Boston Red Sox hat will be used. Place the hat at varying distances from the camera and observe the model's response. Initially, it may detect "Mr. Kitty" but as the hat approaches the camera, it may start indicating the presence of "Mr. Chicken". This potential flaw highlights areas for improvement in the model's object recognition capabilities.

5.2 Using an Object from the Face Detection Activity

Utilize an object from a previous activity involving face detection. In this example, a specific face is used. As the face moves closer to the camera, the model struggles to identify whether it belongs to "Mr. Kitty" or "Mr. Chicken". However, as the face moves further away, the model confidently detects "Mr. Chicken" at 100 percent. These observations provide insights into the model's strengths and limitations.

👉 Evaluating the System and Suggesting Improvements

6.1 Taking Notes and Analyzing Results

Whether working individually or as a team, it is essential to take detailed notes throughout the process. Record all the combinations of samples and objects tested, along with their respective detection results. This comprehensive documentation will aid in evaluating the system's performance and suggesting improvements.

6.2 Identifying Pros and Cons

While analyzing the results, it is crucial to identify the pros and cons of the Teachable Machine system. Consider its ease of use, the accuracy of image detection, and its overall functionality. Acknowledge any limitations or shortcomings that may affect its real-world application.

6.3 Matrix of Tested Samples and Objects

Create a matrix that summarizes the performance of the model with different samples and objects. Compare the detection percentages and note any Patterns or trends observed. This matrix will provide a visual representation of the system's strengths and weaknesses across various scenarios.

6.4 Suggestions for Improvement

Based on the evaluation and analysis, propose potential improvements to enhance the Teachable Machine system. Consider ideas such as refining the object recognition algorithm, implementing more diverse training datasets, or exploring Novel approaches to address detection inconsistencies. By suggesting improvements, we contribute to the continued development and advancement of machine learning technologies.

Highlights

Teachable Machine simplifies machine learning for users without coding knowledge.
Label and record images to train the model effectively.
Test the model's accuracy with different objects to evaluate its performance.
Take detailed notes and analyze the results to identify pros and cons.
Create a matrix summarizing the model's performance with various samples.
Suggest improvements to enhance the system's functionality and accuracy.

FAQs

Q: Can Teachable Machine be used for other types of projects besides Image Recognition? A: Yes, Teachable Machine supports audio and pose recognition projects as well.

Q: Is Teachable Machine suitable for advanced machine learning tasks? A: While Teachable Machine is a great tool for beginners and simpler projects, advanced users might benefit from more specialized platforms and frameworks.

Q: Can Teachable Machine be accessed on mobile devices? A: Yes, Teachable Machine is compatible with most modern browsers on both desktop and mobile devices.

Q: Are there any limitations to the number of classes or images that can be used in a project? A: Teachable Machine allows up to three classes and a maximum of 1000 images per class in the free version.

Q: Can Teachable Machine models be exported and used outside of the web application? A: Yes, Teachable Machine provides export options for TensorFlow.js and other frameworks, allowing integration into standalone applications.