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)

Open Model Zoo models can be downloaded with the Model Downloader tool from OpenVINO™ distribution:

cd <openvino_dir>/deployment_tools/open_model_zoo/tools/downloader
./downloader.py --name <model_name>

Example: download the “face-detection-0200” model

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)

Examples

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 import -p <proj_dir> -f <format> <path_to_dataset>
datum model run -p <proj_dir> -m model-0

Detection: ssd_mobilenet_v2_coco

source /opt/intel/openvino/bin/setupvars.sh
cd datumaro/plugins/openvino_plugin
datum create -o proj
datum model add -l openvino -p proj --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 import -p proj -f voc VOCdevkit/
datum model run -p proj -m model-0

Classification: mobilenet-v2-pytorch

source /opt/intel/openvino/bin/setupvars.sh
cd datumaro/plugins/openvino_plugin
datum create -o proj
datum model add -l openvino -p proj --copy -- \
    -d model/mobilenet-v2-pytorch.xml \
    -w model/mobilenet-v2-pytorch.bin \
    -i samples/mobilenet_v2_pytorch_interp.py
datum import -p proj -f voc VOCdevkit/
datum model run -p proj -m model-0