If the image is divided into SxS grid cells each having 2 anchor boxes and 3 classes to be predicted, then what would bethe output dimension of YOLO before non-max suppression?
Answers
Answer:
Ideally, the predicted box and the ground-truth have an IOU of 100% but in practice anything over 50% is usually considered to be a correct prediction. For the above example the IOU is 74.9% and you can see the boxes are a good match
Answer:
The output is stated below in explanation in detail.
Explanation:
YOLO (You Only Look Once) is a popular object detection algorithm that uses a single neural network to predict object bounding boxes and class probabilities for an entire image. The algorithm divides the image into a grid of SxS cells, and for each cell, it predicts 2 anchor boxes and the class probabilities for 3 classes.
Before non-max suppression, YOLO's output dimension is determined by the number of grid cells, anchor boxes, and classes. In this case, the image is divided into an SxS grid, each cell has 2 anchor boxes, and there are 3 classes to be predicted. So the output dimension of YOLO before non-max suppression would be (SxSx(2x5+3)) where 5 is the number of parameters in the bounding box prediction (x, y, width, height, confidence score).
For example, if the image is divided into a 13x13 grid, the output dimension of YOLO before non-max suppression would be
In this case, YOLO will output 2,739 values, representing the bounding box predictions and class probabilities for each cell in the grid.
It's worth mentioning that in YOLOv3, the grid size of an image is not fixed and can be changed depending on the size of the image. Also, YOLOv3 uses anchor boxes of different shapes and sizes, so the number of anchor boxes per grid cell is more than 2.
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