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Two-shot object detection uses two passes of the input image to make predictions about the presence and location of objects.

We will dive deeper into the YOLO model in the next section. YOLO is a single-shot detector that uses a fully convolutional neural network (CNN) to process an image. Such algorithms can be used to detect objects in real time in resource-constrained environments. However, single-shot object detection is generally less accurate than other methods, and it’s less effective in detecting small objects. It processes an entire image in a single pass, making them computationally efficient. Single-shot object detection uses a single pass of the input image to make predictions about the presence and location of objects in the image.