Frequently Asked Questions

How do I create a labeled image datasets?

All – QuestionsCategory: QualityHow do I create a labeled image datasets? Staff asked 7 months ago
Image labeling is one of the most commonly used labeling processes in the Machine Learning training process. The structure and flow of the labeling process are equal to the human body skeletal system. First, you must precisely determine the labels you will need in your classification process and the desirable data outcome of it. There are additional 2 key aspects you should consider when filtering the labels for your project development – the number of labels that you consider sufficient for your model training, and image parts and angles that fall within the selected labeling procedure.

There is no golden number/rule for the amount of data incorporated in model training, but we can say that the richer the dataset is the performance of the model are better. It’s advisable that the number of data points is the same or similar across all classes included.  The way you define your labels will have a great impact on the minimum requirements needed for dataset sizes. The minimum number for one class is 100 images. But keep in mind that more data per included in class results in higher achievements through performing systems. To be sure that your model is optimized for valuable results you should include more items into the class and way more images than the minimum requirement is. Because if you want to learn a machine to recognize and understand the meaning/context better, you need to feed it with a diversity of data that contains different points of interest (objects, different points of view, details, etc) so it can remember it for further processing or some new projects in the future. You can make a list of desirable elements for your project and then incorporate as many as possible images which contain those elements.

Steps to follow when creating images datasets:

  • Create a list of desirable elements in datasets
  • Research images related to desirable data outcomes and elements of the dataset you want to incorporate in your ML model training.
  • Be aware of biased data when filtering the gathered information. It’s not always easy to find and recognize it, but in order to avoid it maybe you should hire or consult  a professional
  • The more data you include in training, the higher and more precious results will be made.

Good Luck 🙂