![]() ![]() This is in contrast to other annotation types such as classification or bounding boxes, which may be faster but usually convey less information. Image segmentation is usually chosen to support use cases in a model where you need to definitively know whether or not an image contains the object of interest as well as what isn’t an object of interest. They provide a balance between annotation speed and targeting items of interest. Whole-image classification is also a good option for abstract information such as scene detection or time of day.īounding boxes, on the other hand, are the standard for most object detection use cases and require a higher level of granularity than whole-image classification. It is by far the easiest and quickest to annotate out of the other common options. Whole image classification provides a broad categorization of an image and is a step up from unsupervised learning as it associates an entire image with just one label. This is also known as semantic segmentation. Every pixel in an image is assigned to at least one class, as opposed to object detection, where the bounding boxes of objects can overlap.
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