![]() ![]() To train a model for this application, we supply a single image of a stop sign as a starter image. In this example, our goal is to localize instances of stop signs in images. The One-Shot Object Detector is not suitable for three-dimensional objects like faces, animals, and cars - such objects are better suited for the classical Object Detector. ![]() Examples include road signs, logos, playing cards, and clapperboards. The One-shot Object Detector is best suited for two-dimensional objects that have some regularity in the wild. Unlike the Object Detector which requires many varied examples of objects in the real world, the One-Shot Object Detector requires a very small (sometimes even just one) canonical example of the object. ![]() One-Shot object detection (OSOD) is the task of detecting an object from as little as one example per category. ![]()
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