# Process YOLO resultsðŸ”—

Converts the output tensor of a YOLO deep learning model to
generally usable data types. The tool first reshapes an input tensor
to an N-by-(5 + *classCount*) matrix that has the parameters of one
bounding box on each row. It filters out detections that have too
low confidence and performs non-maximum suppression on the rest.

## InputsðŸ”—

`tensor`

: The output of a YOLO model. The shape is assumed to be (1 x K x S x S) or (K x S x S), where K = B * (*classCount*+ 5), S is the number of vertical and horizontal image subdivisions and B is the number of anchor boxes per subdivision. For example in YOLO v2, S=13, B=5, classCount=20, so K = 125.`image`

: The image to which the model was applied. Both width and height are assumed to be P * S, where S is the number of vertical and horizontal image subdivisions, P is the number of pixels per subdivision. For example in YOLO v2, S=13 and P=32, so the image size must be 416 x 416. The image is needed because the tensor produced by a YOLO model does not contain information about the coordinate system of the image the model was applied to.`confidenceThreshold`

: Minimum confidence for a bounding box to be accepted.`overlapRatioThreshold`

: Overlap ratio threshold for pruning overlapping detections (non-maximum suppression). If the overlap ratio (intersection over union) of two detections with the same class is greater than this value, the detection with a lower confidence will be discarded. Set to one to disable non-maximum suppression.`classCount`

: The number of classes in the one-hot encoded class vector.`anchorSizes`

: A B-by-2 matrix that contains the relative size of anchor boxes the YOLO model was trained with.

## OutputsðŸ”—

`frame`

: Upper left corner of each remaining detection as a coordinate frame that is aligned to the axes of the image coordinate system. A 4N-by-4 matrix.`size`

: The size of the bounding box in world coordinates. An N-by-2 matrix.`classIndex`

: The class index of each detection. An N-by-1 matrix.`confidence`

: The confidence of each detection. An N-by-1 matrix.