sedna/examples/incremental_learning/helmet_detection/helmet_detection.yaml

apiVersion: sedna.io/v1alpha1
kind: IncrementalLearningJob
metadata:
  name: helmet-detection-demo
spec:
  initialModel:
    name: "initial-model"
  dataset:
    name: "incremental-dataset"
    trainProb: 0.8
  trainSpec:
    template:
      spec:
        nodeName: $WORKER_NODE
        containers:
          - image: $IMAGE
            name: train-worker
            imagePullPolicy: IfNotPresent
            args: [ "train.py" ]
            env:
              - name: "batch_size"
                value: "32"
              - name: "epochs"
                value: "1"
              - name: "input_shape"
                value: "352,640"
              - name: "class_names"
                value: "person,helmet,helmet-on,helmet-off"
              - name: "nms_threshold"
                value: "0.4"
              - name: "obj_threshold"
                value: "0.3"
    trigger:
      checkPeriodSeconds: 60
      timer:
        start: 02:00
        end: 20:00
      condition:
        operator: ">"
        threshold: 500
        metric: num_of_samples
  evalSpec:
    template:
      spec:
        nodeName: $WORKER_NODE
        containers:
          - image: $IMAGE
            name: eval-worker
            imagePullPolicy: IfNotPresent
            args: [ "eval.py" ]
            env:
              - name: "input_shape"
                value: "352,640"
              - name: "class_names"
                value: "person,helmet,helmet-on,helmet-off"
  deploySpec:
    model:
      name: "deploy-model"
      hotUpdateEnabled: true
      pollPeriodSeconds: 60
    trigger:
      condition:
        operator: ">"
        threshold: 0.1
        metric: precision_delta
    hardExampleMining:
      name: "IBT"
      parameters:
        - key: "threshold_img"
          value: "0.9"
        - key: "threshold_box"
          value: "0.9"
    template:
      spec:
        nodeName: $WORKER_NODE
        containers:
          - image: $IMAGE
            name: infer-worker
            imagePullPolicy: IfNotPresent
            args: [ "inference.py" ]
            env:
              - name: "input_shape"
                value: "352,640"
              - name: "video_url"
                value: "file://video/video.mp4"
              - name: "HE_SAVED_URL"
                value: "/he_saved_url"
            volumeMounts:
              - name: localvideo
                mountPath: /video/
              - name: hedir
                mountPath: /he_saved_url
            resources: # user defined resources
              limits:
                memory: 2Gi
        volumes: # user defined volumes
          - name: localvideo
            hostPath:
              path: /incremental_learning/video/
              type: DirectoryOrCreate
          - name: hedir
            hostPath:
              path: /incremental_learning/he/
              type: DirectoryOrCreate
  outputDir: "/output"