Plugins

• 1: OpenVINO™ Inference Interpreter

1 - OpenVINO™ Inference Interpreter

Models supported from interpreter samples

There are detection and image classification examples.

• Detection (SSD-based)

  • Intel Pre-trained Models > Object Detection

    • face-detection-0200
    • face-detection-0202
    • face-detection-0204
    • person-detection-0200
    • person-detection-0201
    • person-detection-0202
    • person-vehicle-bike-detection-2000
    • person-vehicle-bike-detection-2001
    • person-vehicle-bike-detection-2002
    • vehicle-detection-0200
    • vehicle-detection-0201
    • vehicle-detection-0202

  • Public Pre-Trained Models(OMZ) > Object Detection

    • ssd_mobilenet_v1_coco
    • ssd_mobilenet_v2_coco

• Image Classification

  • Public Pre-Trained Models(OMZ) > Classification
    • mobilenet-v2-pytorch

You can find more OpenVINO™ Trained Models here To run the inference with OpenVINO™, the model format should be Intermediate Representation(IR). For the Caffe/TensorFlow/MXNet/Kaldi/ONNX models, please see the Model Conversion Instruction

You need to implement your own interpreter samples to support the other OpenVINO™ Trained Models.

Model download

• Prerequisites

  • OpenVINO™ (To install OpenVINO™, please see the OpenVINO™ Installation Instruction)
  • OpenVINO™ models (To download OpenVINO™ models, please see the Model Downloader Instruction)
  • PASCAL VOC 2012 dataset (To download VOC 2012 dataset, please go VOC2012 download)

  # cd <openvino_dir>/deployment_tools/open_model_zoo/tools/downloader
  # ./downloader.py --name <model_name>
  #
  # Examples
  cd /opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader
  ./downloader.py --name face-detection-0200
  

Model inference

• Prerequisites:

  • OpenVINO™ (To install OpenVINO™, please see the OpenVINO™ Installation Instruction)
  • Datumaro (To install Datumaro, please see the User Manual)
  • OpenVINO™ models (To download OpenVINO™ models, please see the Model Downloader Instruction)
  • PASCAL VOC 2012 dataset (To download VOC 2012 dataset, please go VOC2012 download)

• To run the inference with OpenVINO™ models and the interpreter samples, please follow the instructions below.

  # source <openvino_dir>/bin/setupvars.sh
  # datum create -o <proj_dir>
  # datum model add -l <launcher> -p <proj_dir> --copy -- -d <path_to_xml> -w <path_to_bin> -i <path_to_interpreter_script>
  # datum add path -p <proj_dir> -f <format> <path_to_dataset>
  # datum model run -p <proj_dir> -m model-0
  #
  # Examples
  # Detection> ssd_mobilenet_v2_coco
  source /opt/intel/openvino/bin/setupvars.sh
  cd datumaro/plugins/openvino_plugin
  datum create -o proj_ssd_mobilenet_v2_coco_detection
  datum model add -l openvino -p proj_ssd_mobilenet_v2_coco_detection --copy -- \
      --output-layers=do_ExpandDims_conf/sigmoid \
      -d model/ssd_mobilenet_v2_coco.xml \
      -w model/ssd_mobilenet_v2_coco.bin \
      -i samples/ssd_mobilenet_coco_detection_interp.py
  datum add path -p proj_ssd_mobilenet_v2_coco_detection -f voc VOCdevkit/
  datum model run -p proj_ssd_mobilenet_v2_coco_detection -m model-0
  
  # Classification> mobilenet-v2-pytorch
  source /opt/intel/openvino/bin/setupvars.sh
  cd datumaro/plugins/openvino_plugin
  datum create -o proj_mobilenet_v2_classification
  datum model add -l openvino -p proj_mobilenet_v2_classification --copy -- \
      -d model/mobilenet-v2-pytorch.xml \
      -w model/mobilenet-v2-pytorch.bin \
      -i samples/mobilenet_v2_pytorch_interp.py
  datum add path -p proj_mobilenet_v2_classification -f voc VOCdevkit/
  datum model run -p proj_mobilenet_v2_classification -m model-0