# set the input to our pre-trained network and obtain the output
# class label predictions
net.setInput(blob)
preds = net.forward()

# loop over the input images
for (i, p) in enumerate(imagePaths[1:]):
	# load the image from disk
	image = cv2.imread(p)

	# find the top class label from the `preds` list and draw it on
	# the image
	idx = np.argsort(preds[i])[::-1][0]
	text = "Label: {}, {:.2f}%".format(classes[idx],
		preds[i][idx] * 100)
	cv2.putText(image, text, (5, 25),  cv2.FONT_HERSHEY_SIMPLEX,
		0.7, (0, 0, 255), 2)

	# display the output image
	cv2.imshow("Image", image)
	cv2.waitKey(0)