In supervised machine Learning tasks, data labelling is essential. The quality of the machine learning model will entirely depend on the training data quality. It works almost the same for annotations that are used for Data labelling. Data annotation is known for playing a very important role in the Machine Learning process. To effectively label images, you need to ensure that you have the highest quality image datasets.

Best practices for image annotation

The labelling instructions depend highly on the nature of your task. Each task can have only one labelled image. It would not be suitable anymore for the next task. Also, re-labelling is also not quite uncommon. Datasets and all their labels involve constant improving and changing to fit each task. For this, there are several best practices for image annotation. Some of these best practices include: 

  • Using bounding boxes:

 Tight bounding boxes can be useful in model learning. The learning needs to be accurate and relevant. However, the boxes need not be extra tight. It is important to make the boxes smaller for the objects to fit. 

  • Maintaining Image Consistency: 

Every object of interest can be sensitive when you are identifying them. This requires a higher level of image consistency during the process of annotation. 

  • Tag occluded objects: 

It can constitute an occlusion if any object is partially blocked in a particular image. In this case, it is important to ensure that the occluded objects are entirely labelled. 

  • Accurate Labeling Instructions: 

Labeling instructions should be shareable and clear. This will encourage any future improvements in the Machine Learning projects. Other data labellers who want to add data to the set will need to rely on these clear instructions. These stacked instructions can maintain and create all the high-quality datasets. 

Types of annotations

Data annotation or labelling is vital for machine learning projects. There are different types of annotations for image datasets. These annotations include semantic segmentation, 3D cuboids, bounding boxes, polygonal segmentation, and others. There are also more than a few tools for image annotation that can be used to create Machine Learning models.