diff --git a/detect_age.py b/detect_age.py
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+++ b/detect_age.py
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+# USAGE
+# python detect_age.py --image images/adrian.png --face face_detector --age age_detector
+
+# import the necessary packages
+import numpy as np
+import argparse
+import cv2
+import os
+
+# construct the argument parse and parse the arguments
+ap = argparse.ArgumentParser()
+ap.add_argument("-i", "--image", required=True,
+	help="path to input image")
+ap.add_argument("-f", "--face", required=True,
+	help="path to face detector model directory")
+ap.add_argument("-a", "--age", required=True,
+	help="path to age detector model directory")
+ap.add_argument("-c", "--confidence", type=float, default=0.5,
+	help="minimum probability to filter weak detections")
+args = vars(ap.parse_args())
+
+# define the list of age buckets our age detector will predict
+AGE_BUCKETS = ["(0-2)", "(4-6)", "(8-12)", "(15-20)", "(25-32)",
+	"(38-43)", "(48-53)", "(60-100)"]
+
+# load our serialized face detector model from disk
+print("[INFO] loading face detector model...")
+prototxtPath = os.path.sep.join([args["face"], "deploy.prototxt"])
+weightsPath = os.path.sep.join([args["face"],
+	"res10_300x300_ssd_iter_140000.caffemodel"])
+faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)
+
+# load our serialized age detector model from disk
+print("[INFO] loading age detector model...")
+prototxtPath = os.path.sep.join([args["age"], "age_deploy.prototxt"])
+weightsPath = os.path.sep.join([args["age"], "age_net.caffemodel"])
+ageNet = cv2.dnn.readNet(prototxtPath, weightsPath)
+
+# load the input image and construct an input blob for the image
+image = cv2.imread(args["image"])
+(h, w) = image.shape[:2]
+blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300),
+	(104.0, 177.0, 123.0))
+
+# pass the blob through the network and obtain the face detections
+print("[INFO] computing face detections...")
+faceNet.setInput(blob)
+detections = faceNet.forward()
+
+# loop over the detections
+for i in range(0, detections.shape[2]):
+	# extract the confidence (i.e., probability) associated with the
+	# prediction
+	confidence = detections[0, 0, i, 2]
+
+	# filter out weak detections by ensuring the confidence is
+	# greater than the minimum confidence
+	if confidence > args["confidence"]:
+		# compute the (x, y)-coordinates of the bounding box for the
+		# object
+		box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
+		(startX, startY, endX, endY) = box.astype("int")
+
+		# extract the ROI of the face and then construct a blob from
+		# *only* the face ROI
+		face = image[startY:endY, startX:endX]
+		faceBlob = cv2.dnn.blobFromImage(face, 1.0, (227, 227),
+			(78.4263377603, 87.7689143744, 114.895847746),
+			swapRB=False)
+
+		# make predictions on the age and find the age bucket with
+		# the largest corresponding probability
+		ageNet.setInput(faceBlob)
+		preds = ageNet.forward()
+		i = preds[0].argmax()
+		age = AGE_BUCKETS[i]
+		ageConfidence = preds[0][i]
+
+		# display the predicted age to our terminal
+		text = "{}: {:.2f}%".format(age, ageConfidence * 100)
+		print("[INFO] {}".format(text))
+
+		# draw the bounding box of the face along with the associated
+		# predicted age
+		y = startY - 10 if startY - 10 > 10 else startY + 10
+		cv2.rectangle(image, (startX, startY), (endX, endY),
+			(0, 0, 255), 2)
+		cv2.putText(image, text, (startX, y),
+			cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
+
+# display the output image
+cv2.imshow("Image", image)
+cv2.waitKey(0)
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