Classify job result

Classify job result

Get classify job result, containing the result for the complete testing dataset.

GEThttps://studio.edgeimpulse.com/v1/api/{projectId}/classify/all/result

Path parameters

projectId*integer

Project ID

Query parameters

Response

200

OK

Body

application/json

success*boolean

Whether the operation succeeded

errorstring

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

result*array of ModelResult (object)

predictions*array of ModelPrediction (object)

accuracy*object

additionalMetricsByLearnBlock*array of object

availableVariants*array of KerasModelVariantEnum (enum)

List of all model variants for which classification results exist

Request

JavaScriptCurlPython

const response = await fetch('https://studio.edgeimpulse.com/v1/api/{projectId}/classify/all/result', {
    method: 'GET',
    headers: {},
});
const data = await response.json();

Response

200

{
  "success": false,
  "error": "text",
  "result": [
    {
      "sample": {
        "id": 2,
        "filename": "idle01.d8Ae",
        "signatureValidate": true,
        "signatureMethod": "HS256",
        "signatureKey": "text",
        "created": "2024-11-21T08:49:37.412Z",
        "lastModified": "2024-11-21T08:49:37.412Z",
        "category": "training",
        "coldstorageFilename": "text",
        "label": "healthy-machine",
        "intervalMs": 16,
        "frequency": 62.5,
        "originalIntervalMs": 16,
        "originalFrequency": 62.5,
        "deviceName": "text",
        "deviceType": "text",
        "sensors": [
          {
            "name": "accX",
            "units": "text"
          }
        ],
        "totalLengthMs": 0,
        "added": "2024-11-21T08:49:37.412Z",
        "boundingBoxes": [
          {
            "label": "text"
          }
        ],
        "boundingBoxesType": "object_detection",
        "chartType": "chart",
        "thumbnailVideo": "text",
        "thumbnailVideoFull": "text",
        "isDisabled": false,
        "isProcessing": false,
        "processingError": false,
        "processingErrorString": "text",
        "isCropped": false,
        "projectOwnerName": "text",
        "projectName": "text",
        "projectLabelingMethod": "single_label",
        "sha256Hash": "text",
        "structuredLabels": [
          {
            "label": "text"
          }
        ],
        "structuredLabelsList": [
          "text"
        ],
        "imageDimensions": {}
      },
      "classifications": [
        {
          "learnBlock": {
            "type": "anomaly",
            "name": "NN Classifier",
            "dsp": [
              27
            ],
            "title": "Classification (Keras)",
            "description": "Reduced learning rate and more layers",
            "createdBy": "createImpulse",
            "createdAt": "2024-11-21T08:49:37.412Z"
          },
          "result": [
            {
              "idle": 0.0002,
              "wave": 0.9998,
              "anomaly": -0.42
            }
          ],
          "anomalyResult": [
            {
              "boxes": [
                {
                  "label": "text",
                  "x": 0,
                  "y": 0,
                  "width": 0,
                  "height": 0,
                  "score": 0
                }
              ],
              "scores": [
                [
                  0
                ]
              ],
              "meanScore": 0,
              "maxScore": 0
            }
          ],
          "structuredResult": [
            {
              "boxes": [
                [
                  0
                ]
              ],
              "labels": [
                "text"
              ],
              "scores": [
                0
              ],
              "mAP": 0,
              "f1": 0,
              "precision": 0,
              "recall": 0,
              "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": 0,
          "details": [
            {
              "boxes": [
                [
                  0
                ]
              ],
              "labels": [
                0
              ],
              "scores": [
                0
              ],
              "mAP": 0,
              "f1": 0
            }
          ],
          "objectDetectionLastLayer": "mobilenet-ssd",
          "expectedLabels": [
            {
              "label": "text"
            }
          ]
        }
      ]
    }
  ],
  "predictions": [
    {
      "startMs": 0,
      "endMs": 0,
      "label": "text",
      "prediction": "text",
      "predictionCorrect": false,
      "f1Score": 0,
      "anomalyScores": [
        [
          0
        ]
      ]
    }
  ],
  "accuracy": {
    "totalSummary": {},
    "allLabels": [
      "text"
    ],
    "accuracyScore": 0,
    "mseScore": 0
  },
  "additionalMetricsByLearnBlock": [
    {
      "learnBlockName": "text",
      "additionalMetrics": [
        {
          "name": "text",
          "value": "text",
          "fullPrecisionValue": 0,
          "tooltipText": "text",
          "link": "text"
        }
      ]
    }
  ],
  "availableVariants": [
    "int8"
  ]
}