Classify sample

Classify sample

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.

POSThttps://studio.edgeimpulse.com/v1/api/{projectId}/classify/v2/{sampleId}

Path parameters

projectId*integer

Project ID

sampleId*integer

Sample ID

Query parameters

Response

200

OK

Body

application/json

any of

ClassifySampleResponse (all of)StartJobResponse (all of)

Request

JavaScriptCurlPython

const response = await fetch('https://studio.edgeimpulse.com/v1/api/{projectId}/classify/v2/{sampleId}', {
    method: 'POST',
    headers: {},
});
const data = await response.json();

Response

200

{
  "success": false,
  "error": "text",
  "classifications": [
    {
      "learnBlock": {
        "id": 0,
        "type": "anomaly",
        "name": "NN Classifier",
        "dsp": [
          27
        ],
        "title": "Classification (Keras)",
        "description": "Reduced learning rate and more layers",
        "createdBy": "createImpulse",
        "createdAt": "2024-12-21T11:33:30.993Z"
      },
      "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": [
        {
          "startIndex": 0,
          "endIndex": 0,
          "label": "text"
        }
      ]
    }
  ],
  "sample": {
    "sample": {
      "id": 2,
      "filename": "idle01.d8Ae",
      "signatureValidate": true,
      "signatureMethod": "HS256",
      "signatureKey": "text",
      "created": "2024-12-21T11:33:30.993Z",
      "lastModified": "2024-12-21T11:33:30.993Z",
      "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": 0,
      "totalLengthMs": 0,
      "added": "2024-12-21T11:33:30.993Z",
      "boundingBoxes": [
        {
          "label": "text",
          "x": 0,
          "y": 0,
          "width": 0,
          "height": 0
        }
      ],
      "boundingBoxesType": "object_detection",
      "chartType": "chart",
      "thumbnailVideo": "text",
      "thumbnailVideoFull": "text",
      "isDisabled": false,
      "isProcessing": false,
      "processingJobId": 0,
      "processingError": false,
      "processingErrorString": "text",
      "isCropped": false,
      "projectId": 0,
      "projectOwnerName": "text",
      "projectName": "text",
      "projectLabelingMethod": "single_label",
      "sha256Hash": "text",
      "structuredLabels": [
        {
          "startIndex": 0,
          "endIndex": 0,
          "label": "text"
        }
      ],
      "structuredLabelsList": [
        "text"
      ],
      "createdBySyntheticDataJobId": 0,
      "imageDimensions": {
        "width": 0,
        "height": 0
      }
    },
    "payload": {
      "device_name": "ac:87:a3:0a:2d:1b",
      "device_type": "DISCO-L475VG-IOT01A",
      "sensors": [
        {
          "name": "accX",
          "units": "text"
        }
      ],
      "values": [
        [
          0
        ]
      ],
      "cropStart": 0,
      "cropEnd": 128
    },
    "totalPayloadLength": 0
  },
  "windowSizeMs": 2996,
  "windowIncreaseMs": 10,
  "alreadyInDatabase": false,
  "warning": "text"
}