• To ensure robust training of an algorithm, it is recommended to provide at least 5000 pixels of annotations for each class. This can be achieved with just 5 annotations of size 10*100 pixels, but in more difficult cases, several hundred annotations may be necessary.
  • While most use cases require annotations of the background class, there are some instances where it may not be necessary. For example, when segmenting a microscopic image of a composite material comprising two phases, both phases must be annotated but there is no background to annotate. However, such cases are rare, and in most cases, background annotations are required.

Tips:

  • The arivis AI toolkit's segmentation algorithm is a powerful tool, but it requires a significant number of annotations to accurately learn the different sizes, shapes, and colors of the classes being segmented. The complexity of segmentation tasks can vary greatly, making it difficult to determine the exact number of objects that must be annotated for the desired level of accuracy and robustness. Based on our experience, it's best to plan on annotating several hundred objects, but it's important not to attempt to annotate the entire dataset at once. Instead, try training the algorithm on a smaller set of data after annotating around 50 objects per class.
  • It's likely that your first training won't produce a highly accurate and robust segmentation. However, it's important not to give up as developing a high-performance algorithm is an iterative process!
  • Once you've completed your first training run, identify areas of low performance and create additional annotations in those regions. By iteratively improving your model, you can achieve better results. (See "Improve Your Segmentation" for more guidance.)
  • It's critical to annotate accurately, as you are the teacher of the ML segmentation algorithm. If inaccurate annotations are provided, the algorithm will learn to segment inaccurately.

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Have a question about annotations? Feel free to reach out to our support team.