Learn how to use webcam images as training data to create a Teachable Machine model that accurately detects and classifies Rock, Paper, or Scissors hand gestures.
Table of Contents
Question
What would you use to train the Teachable Machine to detect Rock, Paper, or Scissors?
A. The images recorded by the webcam are the data used to train the model.
B. The classes Rock, Paper, and Scissors are the data used to train the model.
Answer
A. The images recorded by the webcam are the data used to train the model.
Explanation
To train a Teachable Machine model to detect and classify Rock, Paper, or Scissors hand gestures, you need to provide the model with examples of each class. These examples serve as the training data that the model learns from to make accurate predictions.
In this case, you would use a webcam to capture images of your hand making the Rock, Paper, and Scissors gestures. For each class (Rock, Paper, and Scissors), you record multiple images showcasing variations in hand position, lighting, and background. The more diverse and representative the training images are for each class, the better the model will perform in real-world scenarios.
The Teachable Machine uses these recorded images as the input data to train the model. During training, the model analyzes the patterns, features, and characteristics of the images associated with each class. It learns to distinguish between Rock, Paper, and Scissors based on the visual information provided in the training images.
The class labels themselves (Rock, Paper, and Scissors) are not the data used to train the model. They serve as the target or ground truth that the model aims to predict correctly based on the input images. The model learns the mapping between the input images and their corresponding class labels during the training process.
In summary, to train a Teachable Machine model for Rock, Paper, Scissors detection, you use the images recorded by the webcam as the training data, while the class labels guide the model in learning the correct associations.
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