Any time machine learning is applied to solve a problem, in some way the goal is to fit a model to some data. For your model to perform well and generalize to unseen data, you need to make sure that you use a high quality dataset for training. Especially in a supervised learning setting, you need to make sure that your data is accurately labeled.
Data is the most important part of machine learning.
No matter how large you make your model, how many billion parameters you throw at it or how much augmentation you put the data set through, poor input will not magically turn into high quality output.
Depending on the task you’re trying to solve, there is not always an adequate public dataset available. In these cases, you might need to build your own dataset. However, in the beginning your data is most likely not labeled. Let me show you, how we can build a simple, quick annotation tool to classify your image data from an unlabeled dataset.
Image Dataset
To demonstrate the annotation tool, I will be using an image dataset from my phone recordings, where the goal is to classify…
