Seven measures to improve the accuracy of AI.

The challenge after creating an AI model is "improving accuracy."

In recent years, tools and services that allow for the easy creation of Deep Learning models have been made available. With this ease of access, there may already be individuals who have begun developing Deep Learning models. The next challenge after creating a Deep Learning model is "improving accuracy." When the expected accuracy is not achieved, implementing the following can lead to performance improvements: 1. Changing various hyperparameters 2. Increasing the amount of training data 3. Increasing the "patterns" in the training data. For example, differences in lighting brightness, size, and speed 4. Performing preprocessing to clarify the features of the data 5. Comparing and considering multiple types of models to use 6. Reviewing the class design of the model 7. Building an ensemble model that combines multiple models However, carrying out these tasks requires specialized knowledge, as well as time and effort to prepare the data. By using our "DeepEye," you can easily implement these accuracy improvement strategies.

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