Given the network output and a detection threshold (in range 0 to 1) determine the objects detected by
Supports minibatches - the returned DetectedObject instances have an example number index.
Note that the dimensions are grid cell units - for example, with 416x416 input, 32x downsampling by the network
(before getting to the Yolo2OutputLayer) we have 13x13 grid cells (each corresponding to 32 pixels in the input
image). Thus, a centerX of 5.5 would be xPixels=5.5x32 = 176 pixels from left. Widths and heights are similar:
in this example, a with of 13 would be the entire image (416 pixels), and a height of 6.5 would be 6.5/13 = 0.5
of the image (208 pixels).
boundingBoxPriors - as given to Yolo2OutputLayer
networkOutput - 4d activations out of the network
confThreshold - Detection threshold, in range 0.0 (least strict) to 1.0 (most strict). Objects are returned
where predicted confidence is >= confThreshold
nmsThreshold - passed to nms(List, double) (0 == disabled) as the threshold for intersection over union (IOU)