Learn the correct labeling technique for training Teachable Machine to detect Rock, Paper, and Scissors hand poses. Discover why class labels are crucial for accurate machine learning models.
Table of Contents
Question
What would be the labels, while training the Teachable Machine to detect Rock, Paper, or Scissors hand poses.
A. The images recorded by the webcam
B. The classes Rock, Paper, and Scissors
Answer
B. The classes Rock, Paper, and Scissors
Explanation
When training a Teachable Machine model to detect Rock, Paper, or Scissors hand poses, the labels used are the classes themselves: Rock, Paper, and Scissors. These labels represent the distinct categories or classes that the machine learning model needs to learn and distinguish between.
Here’s a detailed explanation:
- Class Labels: In machine learning, especially for classification tasks like this one, we use class labels to categorize the different types of data we want the model to recognize. In this case, “Rock,” “Paper,” and “Scissors” are the class labels.
- Purpose of Labels: These labels help the model understand what each set of images represents. When you provide examples of each hand pose, you’re essentially telling the machine, “This is what ‘Rock’ looks like,” “This is ‘Paper’,” and “This is ‘Scissors’.”
- Training Process: During the training process, you would collect multiple images of each hand pose and associate them with their respective labels. For example, you might take 100 images of a hand forming a rock, and all these images would be labeled as “Rock.”
- Model Learning: The Teachable Machine uses these labeled examples to learn the visual characteristics associated with each class. It learns to identify the distinctive features that differentiate a “Rock” pose from a “Paper” or “Scissors” pose.
- Prediction: Once trained, when the model encounters a new image through the webcam, it uses what it has learned to predict which class (Rock, Paper, or Scissors) the new hand pose belongs to.
- Why not Option A: The images recorded by the webcam are the input data, not the labels. These images are what the model analyzes to make its predictions, but they themselves are not the labels.
By using clear, distinct class labels like Rock, Paper, and Scissors, you’re providing the Teachable Machine with a structured way to categorize and learn from the visual data, enabling it to accurately classify new hand poses it encounters.
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