Classify sample for the given set of variants

Classify sample for the given set of variants

post

Classify a complete file against the current impulse, for all given variants. Depending on the size of your file and whether the sample is resampled, you may get a job ID in the response.

Authorizations

ApiKeyAuthenticationJWTAuthenticationJWTHttpHeaderAuthenticationOAuth2

Path parameters

projectIdintegerRequired

Project ID

sampleIdintegerRequired

Sample ID

Query parameters

includeDebugInfobooleanOptional

Whether to return the debug information from FOMO classification.

variantsstringRequired

List of keras model variants, given as a JSON string

Example: ["int8", "float32"]

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

200

OK

application/json

Responseany of

all ofOptional

or

all ofOptional

post

HTTPcURLJavaScriptPython

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

200

OK

{
  "success": true,
  "error": "text",
  "results": [
    {
      "variant": "int8",
      "classifications": [
        {
          "learnBlock": {
            "id": 1,
            "type": "anomaly",
            "name": "NN Classifier",
            "dsp": [
              27
            ],
            "title": "Classification (Keras)",
            "createdBy": "createImpulse",
            "createdAt": "2025-07-12T02:33:48.554Z"
          },
          "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": "2025-07-12T02:33:48.554Z",
      "lastModified": "2025-07-12T02:33:48.554Z",
      "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": "2025-07-12T02:33:48.554Z",
      "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
}