Classify sample

Classify sample

post

Classify a complete file against the current impulse. 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

ApiKeyAuthenticationJWTAuthenticationJWTHttpHeaderAuthentication

Path parameters

projectIdintegerrequired

Project ID

sampleIdintegerrequired

Sample ID

Query parameters

includeDebugInfobooleanoptional

Whether to return the debug information from FOMO classification.

variantstring · enumoptional

Keras model variant

Options: int8, float32, akida

impulseIdintegeroptional

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

truncateStructuredLabelsbooleanoptional

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

Responses

application/json

cURL

JavaScript

Python

HTTP

cURL

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

200

200

{
  "success": true,
  "error": "text",
  "classifications": [
    {
      "learnBlock": {
        "id": 1,
        "type": "anomaly",
        "name": "NN Classifier",
        "dsp": [
          27
        ],
        "title": "Classification (Keras)",
        "description": "Reduced learning rate and more layers",
        "createdBy": "createImpulse",
        "createdAt": "2025-03-26T10:22:34.390Z"
      },
      "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"
        }
      ]
    }
  ],
  "sample": {
    "sample": {
      "id": 2,
      "filename": "idle01.d8Ae",
      "signatureValidate": true,
      "signatureMethod": "HS256",
      "signatureKey": "text",
      "created": "2025-03-26T10:22:34.390Z",
      "lastModified": "2025-03-26T10:22:34.390Z",
      "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": "2025-03-26T10:22:34.390Z",
      "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
      }
    },
    "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"
}

OK