Evaluating the model’s accuracy
The model’s accuracy experimented on the validation dataset seems to be promising. However, we can only confidently confirm the model’s suitability for our needs after evaluating its performance on unseen data.
In this recipe, we will carry out this evaluation with the Model testing and Live classification tools of Edge Impulse.
Getting ready
The test dataset provides an unbiased evaluation of the model’s accuracy since these samples are not used during training. Evaluating how the model behaves on unseen data is essential to determine its alignment with our expectations. For example, this assessment might unveil that the model struggles to identify objects against specific backgrounds. If such a situation arises, it could be due to the training dataset’s limited size. Therefore, we may retrain the model using additional images to address the issue.
However, what extra steps should we take if we have...
