# loop over each of the layer outputs
	for output in layerOutputs:
		# loop over each of the detections
		for detection in output:
			# extract the class ID and confidence (i.e., probability)
			# of the current object detection
			scores = detection[5:]
			classID = np.argmax(scores)
			confidence = scores[classID]

			# filter out weak predictions by ensuring the detected
			# probability is greater than the minimum probability
			if confidence > args["confidence"]:
				# scale the bounding box coordinates back relative to
				# the size of the image, keeping in mind that YOLO
				# actually returns the center (x, y)-coordinates of
				# the bounding box followed by the boxes' width and
				# height
				box = detection[0:4] * np.array([W, H, W, H])
				(centerX, centerY, width, height) = box.astype("int")

				# use the center (x, y)-coordinates to derive the top
				# and and left corner of the bounding box
				x = int(centerX - (width / 2))
				y = int(centerY - (height / 2))

				# update our list of bounding box coordinates,
				# confidences, and class IDs
				boxes.append([x, y, int(width), int(height)])
				confidences.append(float(confidence))
				classIDs.append(classID)