Data collection is a critical aspect of machine learning, as the performance and accuracy of models heavily rely on the quality and quantity of the data used for training. However, data collection poses numerous challenges that can hinder the progress of machine learning projects. One significant challenge is obtaining labeled data, particularly in tasks that require extensive human annotation, such as image recognition, natural language processing, or medical diagnosis. Manual labeling can be time-consuming, expensive, and prone to errors, especially when dealing with large datasets.
This is a companion discussion topic for the original entry at https://www.edgeimpulse.com/blog/object-detection-fake-it-to-make-it