Specific version of MobileNet-SSD from TensorFlow

samples/dnn/mobilenet_ssd_accuracy.py

@@ -18,7 +18,7 @@ parser = argparse.ArgumentParser(
                 'COCO evaluation framework is required: http://cocodataset.org')
 parser.add_argument('--weights', required=True,
                     help='Path to frozen_inference_graph.pb of MobileNet-SSD model. '
-                         'Download it at https://github.com/tensorflow/models/tree/master/research/object_detection')
+                         'Download it from http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_11_06_2017.tar.gz')
 parser.add_argument('--prototxt', help='Path to ssd_mobilenet_v1_coco.pbtxt from opencv_extra.', required=True)
 parser.add_argument('--images', help='Path to COCO validation images directory.', required=True)
 parser.add_argument('--annotations', help='Path to COCO annotations file.', required=True)

samples/dnn/mobilenet_ssd_python.py

@@ -6,7 +6,7 @@
 #
 # Also TensorFlow model from TensorFlow object detection model zoo may be used to
 # detect objects from 90 classes:
-# https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
+# http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_11_06_2017.tar.gz
 # Text graph definition must be taken from opencv_extra:
 # https://github.com/opencv/opencv_extra/tree/master/testdata/dnn/ssd_mobilenet_v1_coco.pbtxt
 import numpy as np
@@ -40,7 +40,7 @@ if __name__ == "__main__":
     parser.add_argument("--num_classes", default=20, type=int,
                         help="Number of classes. It's 20 for Caffe model from "
                              "https://github.com/chuanqi305/MobileNet-SSD/ and 90 for "
-                             "TensorFlow model from https://github.com/tensorflow/models/tree/master/research/object_detection")
+                             "TensorFlow model from http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_11_06_2017.tar.gz")
     parser.add_argument("--thr", default=0.2, type=float, help="confidence threshold to filter out weak detections")
     args = parser.parse_args()