OpenVINO™ Inference Interpreter

Interpreter samples to parse OpenVINO™ inference outputs This section on GitHub’

Models supported from interpreter samples

There are detection and image classification examples.

Detection (SSD-based)
- Intel Pre-trained Models > Object Detection
- 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