Classify sample by learn block

Classify sample by learn block

Classify a complete file against the specified learn block. This will move the sliding window (dependent on the sliding window length and the sliding window increase parameters in the impulse) over the complete file, and classify for every window that is extracted. Depending on the size of your file, whether your sample is resampled, and whether the result is cached you'll get either the result or a job back. If you receive a job, then wait for the completion of the job, and then call this function again to receive the results. The unoptimized (float32) model is used by default, and classification with an optimized (int8) model can be slower.

post

/api/{projectId}/classify/anomaly-gmm/v2/{blockId}//{sampleId}

Authorizations

ApiKeyAuthenticationJWTAuthenticationJWTHttpHeaderAuthentication

Path parameters

projectIdintegerrequired

Project ID

sampleIdintegerrequired

Sample ID

blockIdintegerrequired

Block ID

Query parameters

variantstring · enum

Keras model variant

Options: int8, float32, akida

Responses

application/json

cURL

JavaScript

Python

HTTP

cURL

curl -L \
  --request POST \
  --url 'https://studio.edgeimpulse.com/v1/api/{projectId}/classify/anomaly-gmm/v2/{blockId}/{sampleId}' \
  --header 'x-api-key: YOUR_API_KEY'

200

200

{
  "success": true,
  "error": "text",
  "sample": {
    "sample": {
      "id": 2,
      "filename": "idle01.d8Ae",
      "signatureValidate": true,
      "signatureMethod": "HS256",
      "signatureKey": "text",
      "created": "2025-02-21T18:11:44.904Z",
      "lastModified": "2025-02-21T18:11:44.904Z",
      "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": "2025-02-21T18:11:44.904Z",
      "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
      }
    },
    "payload": {
      "device_name": "ac:87:a3:0a:2d:1b",
      "device_type": "DISCO-L475VG-IOT01A",
      "cropStart": 0,
      "cropEnd": 128,
      "sensors": [
        {
          "name": "accX",
          "units": "text"
        }
      ],
      "values": [
        [
          1
        ]
      ]
    },
    "totalPayloadLength": 1
  },
  "windowSizeMs": 2996,
  "windowIncreaseMs": 10,
  "alreadyInDatabase": true,
  "warning": "text",
  "classifications": [
    {
      "learnBlock": {
        "id": 1,
        "type": "anomaly",
        "name": "NN Classifier",
        "title": "Classification (Keras)",
        "description": "Reduced learning rate and more layers",
        "createdBy": "createImpulse",
        "createdAt": "2025-02-21T18:11:44.904Z",
        "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
        }
      ]
    }
  ]
}

OK