Knowledge of visual inspection: Why is it necessary to prepare a lot of training data?
I will explain the importance of "training data," which is the first hurdle to overcome when introducing AI.
When implementing an image recognition and inspection system using AI (deep learning), we often encounter the question, "How much training data is needed?" To cut to the chase, the answer is "there is no fixed number," but the key to successful implementation lies in "using good data appropriately." This article explains the significance and utilization of training data as follows: - The roles of training data, validation data, and evaluation data - Designing to prevent data bias - The required amount of data depends on "variability factors" - Label accuracy and annotation design - Ongoing maintenance after operation *For more details, please see the related link (blog).
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