Beyond Bounding Boxes: Leverage Active Learning with Image Annotation Tools
Image annotation and machine learning model training are the most time-consuming processes in the machine learning pipeline. It is often observed that data provided during training can be inaccurate, unclear, or repetitive due to human error. This can make the model less effective and invalidate all efforts that have been put into annotations. To mitigate such issues, active learning methods can be utilized to help in image selection. This can, in turn, allow users to annotate and use datasets that are smaller, less time-consuming, and meaningful for the ML model to learn from. It can then increase the model training speed and provide insight into the diversity of the dataset. When it comes to machine learning and artificial intelligence, the accuracy and effectiveness of data labeling services often depend on the quality and quantity of data they are trained on. For image-based applications, such as computer vision and object detection, the role of accurate image annotations ca...