DLR image classification

(Optional) The object that contains the authorization policy that allows the component to publish messages to the default notifications topic.

Default:

{
   "aws.greengrass.ipc.mqttproxy": {
      "aws.greengrass.DLRImageClassification:mqttproxy:1": {
         "policyDescription": "Allows access to publish via topic ml/dlr/image-classification.",
         "operations": [
            "aws.greengrass#PublishToIoTCore"
         ],
         "resources": [
            "ml/dlr/image-classification"
         ]
      }
   }
}

(Optional) The topic on which you want to publish the inference results. If you modify this value, then you must also modify the value of resources in the accessControl parameter to match your custom topic name.

Default: ml/dlr/image-classification

The accelerator that you want to use. Supported values are cpu and gpu.

The sample models in the dependent model component support only CPU acceleration. To use GPU acceleration with a different custom model, create a custom model component to override the public model component.

Default: cpu

(Optional) The path of the folder on the device where inference components read images. You can modify this value to any location on your device to which you have read/write access.

Default: /greengrass/v2/packages/artifacts-unarchived/component-name/image_classification/sample_images/

(Optional) The name of the image that the inference component uses as an input to a make prediction. The component looks for the image in the folder specified in ImageDirectory. By default, the component uses the sample image in the default image directory. AWS IoT Greengrass supports the following image formats: jpeg, jpg, png, and npy.

Default: cat.jpeg

(Optional) The time in seconds between each prediction made by the inference code. The sample inference code runs indefinitely and repeats its predictions at the specified time interval. For example, you can change this to a shorter interval if you want to use images taken by a camera for real-time prediction.

Default: 3600

(Optional) The models that are used in the dependent public model component. Modify this parameter only if you override the public model component with a custom component.

Default:

{
    "armv7l": "DLR-resnet50-armv7l-cpu-ImageClassification",
    "aarch64": "DLR-resnet50-aarch64-cpu-ImageClassification",
    "x86_64": "DLR-resnet50-x86_64-cpu-ImageClassification",
    "windows": "DLR-resnet50-win-cpu-ImageClassification"
}

(Optional) String value that defines whether to use images from a camera connected to the Greengrass core device. Supported values are true and false.

When you set this value to true, the sample inference code accesses the camera on your device and runs inference locally on the captured image. The values of the ImageName and ImageDirectory parameters are ignored. Make sure that the user running this component has read/write access to the location where the camera stores captured images.

Default: false

(Optional) The path of the folder on Linux core devices where inference components read images and write inference results. You can modify this value to any location on your device to which the user running this component has read/write access.

Default: /greengrass/v2/work/variant.DLR/greengrass_ml

Default: /greengrass/v2/work/variant.TensorFlowLite/greengrass_ml

The accelerator that you want to use. Supported values are cpu and gpu.

The sample models in the dependent model component support only CPU acceleration. To use GPU acceleration with a different custom model, create a custom model component to override the public model component.

Default: cpu

(Optional) The name of the image that the inference component uses as an input to a make prediction. The component looks for the image in the folder specified in ImageDirectory. The default location is MLRootPath/images. AWS IoT Greengrass supports the following image formats: jpeg, jpg, png, and npy.

Default: cat.jpeg

(Optional) The time in seconds between each prediction made by the inference code. The sample inference code runs indefinitely and repeats its predictions at the specified time interval. For example, you can change this to a shorter interval if you want to use images taken by a camera for real-time prediction.

Default: 3600

(Optional) The models that are used in the dependent public model component. Modify this parameter only if you override the public model component with a custom component.

Default:

armv7l: "DLR-resnet50-armv7l-cpu-ImageClassification"
x86_64: "DLR-resnet50-x86_64-cpu-ImageClassification"