GET
/
api
/
{projectId}
/
classify
/
all
/
result
Classify job result
curl --request GET \
  --url https://studio.edgeimpulse.com/v1/api/{projectId}/classify/all/result \
  --header 'x-api-key: <api-key>'
{
  "success": true,
  "error": "<string>",
  "result": [
    {
      "sampleId": 123,
      "sample": {
        "id": 2,
        "filename": "idle01.d8Ae",
        "signatureValidate": true,
        "signatureMethod": "HS256",
        "signatureKey": "<string>",
        "created": "2023-11-07T05:31:56Z",
        "lastModified": "2023-11-07T05:31:56Z",
        "category": "training",
        "coldstorageFilename": "<string>",
        "label": "healthy-machine",
        "intervalMs": 16,
        "frequency": 62.5,
        "originalIntervalMs": 16,
        "originalFrequency": 62.5,
        "deviceName": "<string>",
        "deviceType": "<string>",
        "sensors": [
          {
            "name": "accX",
            "units": "<string>"
          }
        ],
        "valuesCount": 123,
        "totalLengthMs": 123,
        "added": "2023-11-07T05:31:56Z",
        "boundingBoxes": [
          {
            "label": "<string>",
            "x": 123,
            "y": 123,
            "width": 123,
            "height": 123
          }
        ],
        "boundingBoxesType": "object_detection",
        "chartType": "chart",
        "thumbnailVideo": "<string>",
        "thumbnailVideoFull": "<string>",
        "isDisabled": true,
        "isProcessing": true,
        "processingJobId": 123,
        "processingError": true,
        "processingErrorString": "<string>",
        "isCropped": true,
        "metadata": {},
        "projectId": 123,
        "projectOwnerName": "<string>",
        "projectName": "<string>",
        "projectLabelingMethod": "single_label",
        "sha256Hash": "<string>",
        "structuredLabels": [
          {
            "startIndex": 123,
            "endIndex": 123,
            "label": "<string>"
          }
        ],
        "structuredLabelsList": [
          "<string>"
        ],
        "createdBySyntheticDataJobId": 123,
        "imageDimensions": {
          "width": 123,
          "height": 123
        },
        "videoUrl": "<string>",
        "videoUrlFull": "<string>"
      },
      "classifications": [
        {
          "learnBlock": {
            "id": 2,
            "type": "anomaly",
            "name": "NN Classifier",
            "dsp": [
              27
            ],
            "title": "Classification (Keras)",
            "createdBy": "createImpulse",
            "createdAt": "2023-11-07T05:31:56Z"
          },
          "result": [
            {
              "idle": 0.0002,
              "wave": 0.9998,
              "anomaly": -0.42
            }
          ],
          "anomalyResult": [
            {
              "boxes": [
                {
                  "label": "<string>",
                  "x": 123,
                  "y": 123,
                  "width": 123,
                  "height": 123,
                  "score": 123
                }
              ],
              "scores": [
                [
                  123
                ]
              ],
              "meanScore": 123,
              "maxScore": 123
            }
          ],
          "structuredResult": [
            {
              "boxes": [
                [
                  123
                ]
              ],
              "labels": [
                "<string>"
              ],
              "scores": [
                123
              ],
              "mAP": 123,
              "f1": 123,
              "precision": 123,
              "recall": 123,
              "debugInfoJson": "{\n    \"y_trues\": [\n        {\"x\": 0.854, \"y\": 0.453125, \"label\": 1},\n        {\"x\": 0.197, \"y\": 0.53125, \"label\": 2}\n    ],\n    \"y_preds\": [\n        {\"x\": 0.916, \"y\": 0.875, \"label\": 1},\n        {\"x\": 0.25, \"y\": 0.541, \"label\": 2}\n    ],\n    \"assignments\": [\n        {\"yp\": 1, \"yt\": 1, \"label\": 2, \"distance\": 0.053}\n    ],\n    \"normalised_min_distance\": 0.2,\n    \"all_pairwise_distances\": [\n        [0, 0, 0.426],\n        [1, 1, 0.053]\n    ],\n    \"unassigned_y_true_idxs\": [0],\n    \"unassigned_y_pred_idxs\": [0]\n}\n"
            }
          ],
          "minimumConfidenceRating": 123,
          "details": [
            {
              "boxes": [
                [
                  123
                ]
              ],
              "labels": [
                123
              ],
              "scores": [
                123
              ],
              "mAP": 123,
              "f1": 123
            }
          ],
          "objectDetectionLastLayer": "mobilenet-ssd",
          "expectedLabels": [
            {
              "startIndex": 123,
              "endIndex": 123,
              "label": "<string>"
            }
          ],
          "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
            }
          ]
        }
      ]
    }
  ],
  "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
        }
      ]
    }
  ],
  "accuracy": {
    "totalSummary": {
      "good": 123,
      "bad": 123
    },
    "summaryPerClass": {},
    "confusionMatrixValues": {},
    "allLabels": [
      "<string>"
    ],
    "accuracyScore": 123,
    "balancedAccuracyScore": 123,
    "anomalyAccuracyScore": 123,
    "noAnomalyAccuracyScore": 123,
    "mseScore": 123
  },
  "additionalMetricsByLearnBlock": [
    {
      "learnBlockId": 123,
      "learnBlockName": "<string>",
      "additionalMetrics": [
        {
          "name": "<string>",
          "value": "<string>",
          "fullPrecisionValue": 123,
          "tooltipText": "<string>",
          "link": "<string>"
        }
      ]
    }
  ],
  "availableVariants": [
    "int8"
  ],
  "noResultsBecauseThresholdsChanged": "can_regenerate_model_summary"
}

Authorizations

​
x-api-key
string
header
required

Path Parameters

​
projectId
integer
required

Project ID

Query Parameters

​
featureExplorerOnly
boolean

Whether to get only the classification results relevant to the feature explorer.

​
variant
enum<string>

Keras model variant

Available options:
int8,
float32,
akida
​
impulseId
integer

Impulse ID. If this is unset then the default impulse is used.

​
truncateStructuredLabels
boolean

If true, only a slice of labels will be returned for samples with multiple labels.

Response

200 - application/json

OK

​
success
boolean
required

Whether the operation succeeded

​
result
object[]
required
​
predictions
object[]
required
​
accuracy
object
required
​
additionalMetricsByLearnBlock
object[]
required
​
availableVariants
enum<string>[]
required

List of all model variants for which classification results exist

​
error
string

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

​
noResultsBecauseThresholdsChanged
enum<string>

If set to true, there are currently no results because thresholds were changed (e.g. on live classification); and what action you can run to get new results the quickest. If the value is "can_regenerate_model_summary" you can run 'regenerateModelTestingSummary'. If the value is "should_rerun_full_job", you need to run 'startClassifyJob' or 'startEvaluateJob'.

Available options:
can_regenerate_model_summary,
should_rerun_full_job