Converting Yolo v3 models to TensorFlow and OpenVINO(IR) models

To use Yolo v3 model on OpenVINO framework, you should do 2 steps:

1. Convert yolov3.cfg/yolov3.weights to TensorFlow model frozen_darknet_yolov3_model.pb
2. Convert frozen_darknet_yolov3_model.pb to OpenVINO model frozen_darknet_yolov3_model.xml/.bin/.mapping

More: https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_YOLO_From_Tensorflow.html

Converting a Yolo v3 model: Darknet -> TensorFlow

• Download this repository: https://github.com/mystic123/tensorflow-yolo-v3/archive/ed60b9087b04e1d9ca40f8a9d8455d5c30c7c0d3.zip

• un-pack it:

sudo apt-get install unzip
unzip file.zip -d tensorflow-yolo-v3
cd tensorflow-yolo-v3

• Download default Yolo v3 model: https://pjreddie.com/media/files/yolov3.weights

• Download coco.names file: https://raw.githubusercontent.com/AlexeyAB/darknet/master/data/coco.names

• run this command python3 convert_weights_pb.py --class_names coco.names --data_format NHWC --weights_file yolov3.weights

• you will get TensorFlow model frozen_darknet_yolov3_model.pb

Converting a Yolo v3 model: TensorFlow -> OpenVINO(IR)

After converting the Darknet Yolo v3 model to the TensorFlow model, you can convert it to the Openvino model.

• Install OpenVINO: https://software.intel.com/en-us/openvino-toolkit/choose-download

• Put these files to the one directory:

  • frozen_darknet_yolov3_model.pb - TensorFlow model that you got in previous stage
  • <OPENVINO_INSTALL_DIR>/deployment_tools/model_optimizer/extensions/front/tf/yolo_v3.json
  • <OPENVINO_INSTALL_DIR>/deployment_tools/model_optimizer/mo_tf.python

• Run the command: python3 mo_tf.py -b 1 --input_model ./frozen_darknet_yolov3_model.pb --tensorflow_use_custom_operations_config ./yolo_v3.json --data_type FP16

• You will get OpenVINO model that can be run on CPU, GPU, VPU (MyriadX), FPGA:

  • frozen_darknet_yolov3_model.xml - model structure
  • frozen_darknet_yolov3_model.bin - model weights
  • frozen_darknet_yolov3_model.mapping - mapping file

Run IR-OpenVINO model on CPU or VPU (Myriad X)

1. Build C++ examples - run: <OPENVINO_INSTALL_DIR>/inference_engine/samples/build_samples.sh

   • cd /user/home/inference_engine_samples_build/intel64/Release

   • on CPU: ./object_detection_demo_yolov3_async -i ./test.mp4 -m ./frozen_darknet_yolov3_model.xml -d CPU

   • on VPU: ./object_detection_demo_yolov3_async -i ./test.mp4 -m ./frozen_darknet_yolov3_model.xml -d MYRIAD

2. By using Python code:

   • go to <OPENVINO_INSTALL_DIR>/inference_engine/samples/python_samples

   • python3 object_detection_demo_yolov3_async.py -i test.mp4 -m ./frozen_darknet_yolov3_model.xml -d CPU

   • read: https://docs.openvinotoolkit.org/latest/_inference_engine_ie_bridges_python_sample_object_detection_demo_yolov3_async_README.html

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• More: https://docs.openvinotoolkit.org/latest/_inference_engine_samples_object_detection_demo_yolov3_async_README.html

• Also read: https://github.com/PINTO0309/OpenVINO-YoloV3