Every time machine learning is applied to solve a problem, in some way the goal is to adapt to a model to some data. For your model to perform well and generalize to unseen data, you need to ensure that you use a high-quality data set for training. Especially in a supervised learning environment, you need to ensure that your data is accurately labeled.
Data is the most important part of machine learning.
No matter how big you make your model, how many billions of parameters you throw at it, or how much augmentation you apply to the data set, poor input don't magically turn into high-quality results.
Depending on the task you are trying to solve, a suitable public data set is not always available. In these cases, you may need to create your own data set. However, at first your data will most likely not be labeled. Let me show you how we can create a quick and easy annotation tool to classify your image data from an unlabeled data set.
Image Data Set
To demonstrate the annotation tool, I will use a dataset of images from my phone recordings, where the goal is to classify…
