GET
/
api
/
{projectId}
/
training
/
anomaly
/
{learnId}
/
metadata
Anomaly metadata
curl --request GET \
  --url https://studio.edgeimpulse.com/v1/api/{projectId}/training/anomaly/{learnId}/metadata \
  --header 'x-api-key: <api-key>'
{
  "success": true,
  "error": "<string>",
  "created": "2023-11-07T05:31:56Z",
  "scale": [
    123
  ],
  "mean": [
    123
  ],
  "clusters": [
    {
      "center": [
        123
      ],
      "maxError": 123
    }
  ],
  "axes": "`[ 0, 11, 22 ]`",
  "defaultMinimumConfidenceRating": 123,
  "availableModelTypes": [
    "int8"
  ],
  "recommendedModelType": "int8",
  "modelValidationMetrics": [
    {
      "type": "int8",
      "loss": 123,
      "accuracy": 123,
      "confusionMatrix": [
        [
          31,
          1,
          0
        ],
        [
          2,
          27,
          3
        ],
        [
          1,
          0,
          39
        ]
      ],
      "report": {},
      "onDevicePerformance": [
        {
          "mcu": "<string>",
          "name": "<string>",
          "isDefault": true,
          "latency": 123,
          "tflite": {
            "ramRequired": 123,
            "romRequired": 123,
            "arenaSize": 123,
            "modelSize": 123
          },
          "eon": {
            "ramRequired": 123,
            "romRequired": 123,
            "arenaSize": 123,
            "modelSize": 123
          },
          "eon_ram_optimized": {
            "ramRequired": 123,
            "romRequired": 123,
            "arenaSize": 123,
            "modelSize": 123
          },
          "customMetrics": [
            {
              "name": "<string>",
              "value": "<string>"
            }
          ],
          "hasPerformance": true,
          "profilingError": "<string>"
        }
      ],
      "predictions": [
        {
          "sampleId": 123,
          "startMs": 123,
          "endMs": 123,
          "label": "<string>",
          "prediction": "<string>",
          "predictionCorrect": true,
          "f1Score": 123,
          "anomalyScores": [
            [
              123
            ]
          ],
          "boundingBoxes": [
            {
              "label": "<string>",
              "x": 123,
              "y": 123,
              "width": 123,
              "height": 123,
              "score": 123
            }
          ]
        }
      ],
      "visualization": "featureExplorer",
      "isSupportedOnMcu": true,
      "mcuSupportError": "<string>",
      "profilingJobId": 123,
      "profilingJobFailed": true,
      "additionalMetrics": [
        {
          "name": "<string>",
          "value": "<string>",
          "fullPrecisionValue": 123,
          "tooltipText": "<string>",
          "link": "<string>"
        }
      ]
    }
  ],
  "hasTrainedModel": true,
  "thresholds": [
    {
      "key": "min_score",
      "description": "Score threshold",
      "helpText": "Threshold score for bounding boxes. If the score for a bounding box is below this the box will be discarded.",
      "suggestedValue": 123,
      "suggestedValueText": "<string>",
      "value": 0.5
    }
  ]
}

Authorizations

x-api-key
string
header
required

Path Parameters

projectId
integer
required

Project ID

learnId
integer
required

Learn Block ID, use the impulse functions to retrieve the ID

Response

200 - application/json

OK

success
boolean
required

Whether the operation succeeded

created
string<date-time>
required

Date when the model was trained

scale
number[]
required

Scale input for StandardScaler. Values are scaled like this (where ix is axis index): input[ix] = (input[ix] - mean[ix]) / scale[ix];

mean
number[]
required

Mean input for StandardScaler. Values are scaled like this (where ix is axis index): input[ix] = (input[ix] - mean[ix]) / scale[ix];

clusters
object[]
required

Trained K-means clusters

axes
integer[]
required

Which axes were included during training (by index)

Example:

"[ 0, 11, 22 ]"

defaultMinimumConfidenceRating
number
required

DEPRECATED, see "thresholds" instead. Default minimum confidence rating required before tagging as anomaly, based on scores of training data (GMM only).

thresholds
object[]
required

List of configurable thresholds for this block.

error
string

Optional error description (set if 'success' was false)

availableModelTypes
enum<string>[]

The types of model that are available

The model type that is recommended for use

Available options:
int8,
float32,
akida,
requiresRetrain
modelValidationMetrics
object[]

Metrics for each of the available model types

hasTrainedModel
boolean