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    Why is Yolo not able to detect objects?The reason for this limitation is due to the YOLO algorithm itself: The YOLO object detector divides an input image into an SxS grid where each cell in the grid predicts only a single object. If there exist multiple, small objects in a single cell then YOLO will be unable to detect them, ultimately leading to missed object detections.
    Can Yolo predict object classes that don't have labeled detection data?The result is a YOLO model, called YOLO9000, that can predict detections for object classes that don’t have labeled detection data. While interesting and novel, YOLOv2’s performance was a bit underwhelming given the title and abstract of the paper.
    Why is Yolo a breakthrough in object detection?YOLO (you only look once) was a breakthrough in the object detection field as it was the first single-stage object detector approach that treated detection as a regression problem. The detection architecture only looked once at the image to predict the location of the objects and their class labels.
    Is Yolo a good Real-Time Object Detector?YOLO achieved 63.4 mAP (mean average precision), as shown in Table 1, more than double the other real-time detectors, making it even more special. We can see that both the YOLO and Fast YOLO outperforms the real-time object detector variants of DPM by a considerable margin in terms of mean average precision (nearly 2x) and FPS.
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