Learn how to improve your image classification model accuracy by adding more images to the training set.
Table of Contents
Question
You are training your image classification model and you realize that some images are not classified correctly. What should you do to improve the model?
A. Reduce the number of images used for the training set
B. Add new labels to the model
C. Add additional images to the training set
Answer
C. Add additional images to the training set
Explanation
The more images in the training set, the better the model will understand patterns and the more accurate its predictions will be.
The correct answer to the question is C. Add additional images to the training set. Here is why:
Image classification is a type of supervised learning, where the model learns to associate images with labels based on the examples provided in the training set. The more images the model sees, the better it can learn the patterns and features that distinguish different classes of images. Adding additional images to the training set can help the model improve its accuracy and generalization, especially if the new images are diverse and representative of the real-world scenarios that the model will encounter.
Reducing the number of images used for the training set (option A) is not a good idea, because it will limit the amount of information that the model can learn from. This can lead to underfitting, where the model fails to capture the complexity of the data and performs poorly on both the training and the test sets.
Adding new labels to the model (option B) is also not a good idea, because it will change the task that the model is trying to solve. The model will have to learn to classify images into more categories, which will require more data and more training time. This can also introduce confusion and ambiguity to the model, especially if the new labels are not well-defined or overlap with the existing ones.
Therefore, the best way to improve the image classification model is to add additional images to the training set (option C), as long as they are relevant and high-quality.
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