Single page of a classify job result

Single page of a classify job result

Get classify job result, containing the predictions for a given page.

get

/api/{projectId}/classify/all/result/page

Authorizations

ApiKeyAuthenticationJWTAuthenticationJWTHttpHeaderAuthentication

Path parameters

projectIdintegerrequired

Project ID

Query parameters

limitinteger

Maximum number of results

offsetinteger

Offset in results, can be used in conjunction with LimitResultsParameter to implement paging.

variantstring · enum

Keras model variant

Options: int8, float32, akida

impulseIdinteger

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

Responses

application/json

cURL

JavaScript

Python

HTTP

cURL

curl -L \
  --url 'https://studio.edgeimpulse.com/v1/api/{projectId}/classify/all/result/page' \
  --header 'x-api-key: YOUR_API_KEY'

200

200

{
  "success": true,
  "error": "text",
  "result": [
    {
      "sampleId": 1,
      "sample": {
        "id": 2,
        "filename": "idle01.d8Ae",
        "signatureValidate": true,
        "signatureMethod": "HS256",
        "signatureKey": "text",
        "created": "2025-02-21T18:10:58.561Z",
        "lastModified": "2025-02-21T18:10:58.561Z",
        "category": "training",
        "coldstorageFilename": "text",
        "label": "healthy-machine",
        "intervalMs": 16,
        "frequency": 62.5,
        "originalIntervalMs": 16,
        "originalFrequency": 62.5,
        "deviceName": "text",
        "deviceType": "text",
        "valuesCount": 1,
        "totalLengthMs": 1,
        "added": "2025-02-21T18:10:58.561Z",
        "thumbnailVideo": "text",
        "thumbnailVideoFull": "text",
        "isDisabled": true,
        "isProcessing": true,
        "processingJobId": 1,
        "processingError": true,
        "processingErrorString": "text",
        "isCropped": true,
        "projectId": 1,
        "projectOwnerName": "text",
        "projectName": "text",
        "sha256Hash": "text",
        "createdBySyntheticDataJobId": 1,
        "boundingBoxesType": "object_detection",
        "chartType": "chart",
        "projectLabelingMethod": "single_label",
        "sensors": [
          {
            "name": "accX",
            "units": "text"
          }
        ],
        "boundingBoxes": [
          {
            "label": "text",
            "x": 1,
            "y": 1,
            "width": 1,
            "height": 1
          }
        ],
        "metadata": {
          "ANY_ADDITIONAL_PROPERTY": "text"
        },
        "structuredLabels": [
          {
            "startIndex": 1,
            "endIndex": 1,
            "label": "text"
          }
        ],
        "structuredLabelsList": [
          "text"
        ],
        "imageDimensions": {
          "width": 1,
          "height": 1
        }
      },
      "classifications": [
        {
          "learnBlock": {
            "id": 1,
            "type": "anomaly",
            "name": "NN Classifier",
            "title": "Classification (Keras)",
            "description": "Reduced learning rate and more layers",
            "createdBy": "createImpulse",
            "createdAt": "2025-02-21T18:10:58.561Z",
            "dsp": [
              27
            ]
          },
          "minimumConfidenceRating": 1,
          "objectDetectionLastLayer": "mobilenet-ssd",
          "anomalyResult": [
            {
              "meanScore": 1,
              "maxScore": 1,
              "boxes": [
                {
                  "label": "text",
                  "x": 1,
                  "y": 1,
                  "width": 1,
                  "height": 1,
                  "score": 1
                }
              ],
              "scores": [
                [
                  1
                ]
              ]
            }
          ],
          "structuredResult": [
            {
              "mAP": 1,
              "f1": 1,
              "precision": 1,
              "recall": 1,
              "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",
              "labels": [
                "text"
              ],
              "scores": [
                1
              ],
              "boxes": [
                [
                  1
                ]
              ]
            }
          ],
          "details": [
            {
              "mAP": 1,
              "f1": 1,
              "labels": [
                1
              ],
              "scores": [
                1
              ],
              "boxes": [
                [
                  1
                ]
              ]
            }
          ],
          "expectedLabels": [
            {
              "startIndex": 1,
              "endIndex": 1,
              "label": "text"
            }
          ],
          "result": [
            {
              "idle": 0.0002,
              "wave": 0.9998,
              "anomaly": -0.42
            }
          ]
        }
      ]
    }
  ],
  "predictions": [
    {
      "sampleId": 1,
      "startMs": 1,
      "endMs": 1,
      "label": "text",
      "prediction": "text",
      "predictionCorrect": true,
      "f1Score": 1,
      "anomalyScores": [
        [
          1
        ]
      ]
    }
  ]
}

OK