What is the recommended minimum number of images needed when creating a new IBM Watson Visual Recognition classifier?

When creating a new IBM Watson Visual Recognition classifier, the recommended minimum number of images needed is between 150 to 200. This range provides a sufficient dataset for the classifier to learn discernible features and patterns associated with different classes or categories. A well-curated set of images ensures that the model can generalize well to new, unseen images and improves its accuracy in making predictions about visual data.

Smaller datasets may not provide enough variability and detail for the model to learn effectively, which can lead to overfitting or poor performance in real-world scenarios. Additionally, using too few images might not cover the range of variations within a class, making it difficult for the model to correctly classify images it has not been specifically trained on.

In contrast, while larger datasets (like those in the other options) can enhance performance and were necessary in earlier models, 150 to 200 images represent an accessible starting point, especially for smaller, more targeted projects where the aim is to demonstrate concepts and achieve proof of concept without extensive resources. Thus, adhering to this minimum ensures a balance between feasibility and effectiveness in classifier training.