Azure ML Training and Deployement on Intel Edge device (RRK)

##Pre-requisites:

• Intel powered Edge device (UP^2) with Ubuntu 16.04
• USB webcam (/dev/video0)
• OpenVINO installation with ORT (ONX Runtime) execution provider
• Python OpenCV

##Folders: #models (models.onnx, labesl.txt)

• ONNX pre-trained models for Image_classificaiton
• ONNX pre-trained models for object_detection #src
• Python Application code for Image Classification and object detection
• objdet/model.config - Model configuration file

##Testing

Image-classifcation

• Folder "models/image_classification" has few pre-trained onnx models
• Execute command: src/imgcls$ python3 onnx_image_classifciation.py model.config
• Expected Output: Predicted image classification result with label
• ##Example:model.config (for Image Classification)
  {
  "Network":0,
  "modeltype":"onnx model",
  "Input":"cam",
  "display":1,
  "mean_vec":[0.485, 0.456, 0.406],
  "stddev_vec":[0.229, 0.224, 0.225],
  "ScaleWidth":224,
  "ScaleHeight":224,
  "InputFormat":"RGB",
  "Runtime":1,
  "MODEL_FILENAME":"../../models/image_classification/dog_n_cat/model.onnx",
  "LABELS_FILENAME":"../../models/image_classification/dog_n_cat/labels.txt"
  }
  Classification results

Object Detection

• Folder "models/object_detect/" has face detection pre-trained onnx model
• Execute command: src/objdet$ python3 onnxruntime_predict.py model.config
• Expected Output: Renders webcam video frames with inference results (bounding box, detection label and score)
• ##Example:model.config (for Object detection)
  {
  "Network":0,
  "modeltype":"onnx model",
  "Input":"cam",
  "display":1,
  "Anchors": [[0.573, 0.677], [1.87, 2.06], [3.34, 5.47], [7.88, 3.53], [9.77, 9.17]],
  "ScaleWidth":416,
  "ScaleHeight":416,
  "InputFormat":"RGB",
  "ConfThreshold":0.5,
  "IOU_THRESHOLD":0.45,
  "Runtime":1,
  "MODEL_FILENAME":"../../models/object_detection/face_detect/model.onnx",
  "LABELS_FILENAME":"../../models/object_detection/face_detect/labels.txt"
  }

Face Detection output

Car and Traffic Light Detection