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Specific version of MobileNet-SSD from TensorFlow

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This commit is contained in:
Dmitry Kurtaev 2017-11-24 13:40:35 +03:00
parent f071a48ec7
commit cdbbccbef8
2 changed files with 3 additions and 3 deletions
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2
samples/dnn/mobilenet_ssd_accuracy.py
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@ -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)

4
samples/dnn/mobilenet_ssd_python.py
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@ -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()