Learn what types of images make the best training data when building a model to recognize cell phones, including photos with partial views and other objects.
Table of Contents
Question
Consider the scenario where you have to train a model to detect cell phones. Identify the TRUE statement.
A. Using photographs of mobile phones and hands holding mobile phones is bad training data.
B. Using photographs of mobile phones in partial view or along with other objects is good training data.
Answer
B. Using photographs of mobile phones in partial view or along with other objects is good training data.
Explanation
When training a machine learning model to detect cell phones in images, it’s important to use a diverse set of training data that reflects real-world scenarios. Using photographs of mobile phones in partial view or along with other objects is good training data.
Here’s why:
- In the real world, cell phones are often partially obscured, like when being held in a hand or pocket. Training on partial views helps the model learn to recognize phones even when not fully visible.
- Cell phones frequently appear alongside other objects, like on a table or desk. Including these objects in training images teaches the model to distinguish phones from other items.
- A variety of angles, lighting conditions, phone models, and backgrounds in the training set leads to a more robust model that generalizes well to new images.
In contrast, only using images of phones in isolation or fully visible would limit the model’s ability to handle realistic detection tasks. Aiming for diverse, real-world training data is key to developing an effective cell phone detection model.
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