Errors in Training Data: How to Identify and Avoid Common Data Errors（Bias)

1. Labeling error
Labeling errors are among the most common errors in developing high-quality data, and there are several types of errors. For example, imagine if the task received is to draw a bounding box around objects in an image, the expected output is a tight bounding box around each object. The following are several types of errors that may occur in the process: