Classify sample by learn block

Classify sample by learn block

post

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.

Authorizations

ApiKeyAuthenticationJWTAuthenticationJWTHttpHeaderAuthenticationOAuth2

Path parameters

projectIdintegerRequired

Project ID

sampleIdintegerRequired

Sample ID

blockIdintegerRequired

Block ID

Query parameters

variantstring · enumOptional

Keras model variant

Possible values:

truncateStructuredLabelsbooleanOptional

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

Responses

200

OK

application/json

Responseany of

all ofOptional

or

all ofOptional

post

HTTPcURLJavaScriptPython

POST /v1/api/{projectId}/classify/anomaly-gmm/v2/{blockId}/{sampleId} HTTP/1.1
Host: studio.edgeimpulse.com
x-api-key: YOUR_API_KEY
Accept: */*

200

OK

{
  "success": true,
  "error": "text",
  "classifications": [
    {
      "learnBlock": {
        "id": 1,
        "type": "anomaly",
        "name": "NN Classifier",
        "dsp": [
          27
        ],
        "title": "Classification (Keras)",
        "createdBy": "createImpulse",
        "createdAt": "2025-07-12T02:47:14.503Z"
      },
      "result": [
        {
          "idle": 0.0002,
          "wave": 0.9998,
          "anomaly": -0.42
        }
      ],
      "anomalyResult": [
        {
          "boxes": [
            {
              "label": "text",
              "x": 1,
              "y": 1,
              "width": 1,
              "height": 1,
              "score": 1
            }
          ],
          "scores": [
            [
              1
            ]
          ],
          "meanScore": 1,
          "maxScore": 1
        }
      ],
      "structuredResult": [
        {
          "boxes": [
            [
              1
            ]
          ],
          "labels": [
            "text"
          ],
          "scores": [
            1
          ],
          "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"
        }
      ],
      "minimumConfidenceRating": 1,
      "details": [
        {
          "boxes": [
            [
              1
            ]
          ],
          "labels": [
            1
          ],
          "scores": [
            1
          ],
          "mAP": 1,
          "f1": 1
        }
      ],
      "objectDetectionLastLayer": "mobilenet-ssd",
      "expectedLabels": [
        {
          "startIndex": 1,
          "endIndex": 1,
          "label": "text"
        }
      ],
      "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": 1,
          "suggestedValueText": "text",
          "value": 0.5
        }
      ]
    }
  ],
  "sample": {
    "sample": {
      "id": 2,
      "filename": "idle01.d8Ae",
      "signatureValidate": true,
      "signatureMethod": "HS256",
      "signatureKey": "text",
      "created": "2025-07-12T02:47:14.503Z",
      "lastModified": "2025-07-12T02:47:14.503Z",
      "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"
        }
      ],
      "valuesCount": 1,
      "totalLengthMs": 1,
      "added": "2025-07-12T02:47:14.503Z",
      "boundingBoxes": [
        {
          "label": "text",
          "x": 1,
          "y": 1,
          "width": 1,
          "height": 1
        }
      ],
      "boundingBoxesType": "object_detection",
      "chartType": "chart",
      "thumbnailVideo": "text",
      "thumbnailVideoFull": "text",
      "isDisabled": true,
      "isProcessing": true,
      "processingJobId": 1,
      "processingError": true,
      "processingErrorString": "text",
      "isCropped": true,
      "metadata": {
        "ANY_ADDITIONAL_PROPERTY": "text"
      },
      "projectId": 1,
      "projectOwnerName": "text",
      "projectName": "text",
      "projectLabelingMethod": "single_label",
      "sha256Hash": "text",
      "structuredLabels": [
        {
          "startIndex": 1,
          "endIndex": 1,
          "label": "text"
        }
      ],
      "structuredLabelsList": [
        "text"
      ],
      "createdBySyntheticDataJobId": 1,
      "imageDimensions": {
        "width": 1,
        "height": 1
      },
      "videoUrl": "text",
      "videoUrlFull": "text"
    },
    "payload": {
      "device_name": "ac:87:a3:0a:2d:1b",
      "device_type": "DISCO-L475VG-IOT01A",
      "sensors": [
        {
          "name": "accX",
          "units": "text"
        }
      ],
      "values": [
        [
          1
        ]
      ],
      "cropStart": 0,
      "cropEnd": 128
    },
    "totalPayloadLength": 1
  },
  "windowSizeMs": 2996,
  "windowIncreaseMs": 10,
  "alreadyInDatabase": true,
  "warning": "text"
}