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)
```
# 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