GET
/
api
/
{projectId}
/
classify
/
all
/
result
/
page
Single page of a classify job result
curl --request GET \
  --url https://studio.edgeimpulse.com/v1/api/{projectId}/classify/all/result/page \
  --header 'x-api-key: <api-key>'
{
  "success": true,
  "error": "<string>",
  "result": [
    {
      "sampleId": 123,
      "sample": {
        "id": 2,
        "filename": "idle01.d8Ae",
        "signatureValidate": true,
        "signatureMethod": "HS256",
        "signatureKey": "<string>",
        "created": "2023-11-07T05:31:56Z",
        "lastModified": "2023-11-07T05:31:56Z",
        "category": "training",
        "coldstorageFilename": "<string>",
        "label": "healthy-machine",
        "intervalMs": 16,
        "frequency": 62.5,
        "originalIntervalMs": 16,
        "originalFrequency": 62.5,
        "deviceName": "<string>",
        "deviceType": "<string>",
        "sensors": [
          {
            "name": "accX",
            "units": "<string>"
          }
        ],
        "valuesCount": 123,
        "totalLengthMs": 123,
        "added": "2023-11-07T05:31:56Z",
        "boundingBoxes": [
          {
            "label": "<string>",
            "x": 123,
            "y": 123,
            "width": 123,
            "height": 123
          }
        ],
        "boundingBoxesType": "object_detection",
        "chartType": "chart",
        "thumbnailVideo": "<string>",
        "thumbnailVideoFull": "<string>",
        "isDisabled": true,
        "isProcessing": true,
        "processingJobId": 123,
        "processingError": true,
        "processingErrorString": "<string>",
        "isCropped": true,
        "metadata": {},
        "projectId": 123,
        "projectOwnerName": "<string>",
        "projectName": "<string>",
        "projectLabelingMethod": "single_label",
        "sha256Hash": "<string>",
        "structuredLabels": [
          {
            "startIndex": 123,
            "endIndex": 123,
            "label": "<string>"
          }
        ],
        "structuredLabelsList": [
          "<string>"
        ],
        "createdBySyntheticDataJobId": 123,
        "imageDimensions": {
          "width": 123,
          "height": 123
        },
        "videoUrl": "<string>",
        "videoUrlFull": "<string>"
      },
      "classifications": [
        {
          "learnBlock": {
            "id": 2,
            "type": "anomaly",
            "name": "NN Classifier",
            "dsp": [
              27
            ],
            "title": "Classification (Keras)",
            "createdBy": "createImpulse",
            "createdAt": "2023-11-07T05:31:56Z"
          },
          "result": [
            {
              "idle": 0.0002,
              "wave": 0.9998,
              "anomaly": -0.42
            }
          ],
          "anomalyResult": [
            {
              "boxes": [
                {
                  "label": "<string>",
                  "x": 123,
                  "y": 123,
                  "width": 123,
                  "height": 123,
                  "score": 123
                }
              ],
              "scores": [
                [
                  123
                ]
              ],
              "meanScore": 123,
              "maxScore": 123
            }
          ],
          "structuredResult": [
            {
              "boxes": [
                [
                  123
                ]
              ],
              "labels": [
                "<string>"
              ],
              "scores": [
                123
              ],
              "mAP": 123,
              "f1": 123,
              "precision": 123,
              "recall": 123,
              "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": 123,
          "details": [
            {
              "boxes": [
                [
                  123
                ]
              ],
              "labels": [
                123
              ],
              "scores": [
                123
              ],
              "mAP": 123,
              "f1": 123
            }
          ],
          "objectDetectionLastLayer": "mobilenet-ssd",
          "expectedLabels": [
            {
              "startIndex": 123,
              "endIndex": 123,
              "label": "<string>"
            }
          ],
          "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": 123,
              "suggestedValueText": "<string>",
              "value": 0.5
            }
          ]
        }
      ]
    }
  ],
  "predictions": [
    {
      "sampleId": 123,
      "startMs": 123,
      "endMs": 123,
      "label": "<string>",
      "prediction": "<string>",
      "predictionCorrect": true,
      "f1Score": 123,
      "anomalyScores": [
        [
          123
        ]
      ],
      "boundingBoxes": [
        {
          "label": "<string>",
          "x": 123,
          "y": 123,
          "width": 123,
          "height": 123,
          "score": 123
        }
      ]
    }
  ],
  "totalCount": 123
}

Authorizations

x-api-key
string
header
required

Path Parameters

projectId
integer
required

Project ID

Query Parameters

limit
integer

Maximum number of results

offset
integer

Offset in results, can be used in conjunction with LimitResultsParameter to implement paging.

variant
enum<string>

Keras model variant

Available options:
int8,
float32,
akida
impulseId
integer

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

truncateStructuredLabels
boolean

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

labels
string

Only include samples with a label within the given list of labels, given as a JSON string

Example:

"[\"idle\", \"snake\"]"

filename
string

Only include samples whose filename includes the given filename

maxLength
integer

Only include samples shorter than the given length, in milliseconds

minLength
integer

Only include samples longer than the given length, in milliseconds

minFrequency
number

Only include samples with higher frequency than given frequency, in hertz

maxFrequency
number

Only include samples with lower frequency than given frequency, in hertz

signatureValidity
enum<string>

Include samples with either valid or invalid signatures

Available options:
both,
valid,
invalid
minLabel
number

Only include samples with a label >= this value

maxLabel
number

Only include samples with a label < this value

Search query

Example:

"<id> <name>"

dataType
enum<string>

Include only samples with a particular data type

Available options:
audio,
image
minId
number

Include only samples with an ID >= this value

maxId
number

Include only samples with an ID < this value

metadata
string

Filter samples by metadata key-value pairs, provided as a JSON string. Each item in the filter list is an object with the following properties: - "key": Metadata key to filter on. - "op": Operator ("eq" for positive match, "neq" for negative match). - "values": (optional) Array of values to match/exclude. If omitted or empty, matches/excludes all values for the key. In addition to filter objects, the following option objects can be specified: - { "no_metadata": boolean } - If true, include samples without any metadata - { "filters_combinator": ("and" | "or") } - Specifies the combinator and matching mode: - "and": All filter items must match (logical AND). - "or": Any filter item may match (logical OR); samples with metadata keys not present in the filters are included.

Example:

"Example 1: returns samples where metadata key \"foo\" is 'bar' or 'baz' AND\nmetadata key \"k\" is \"v\".\n[\n { \"no_metadata\": true },\n { \"filters_combinator\": \"and\" },\n { \"key\": \"foo\", \"op\": \"eq\", \"values\": [\"bar\", \"baz\"] },\n { \"key\": \"k\", \"op\": \"eq\", \"values\": [\"v\"] }\n]\n\nExample 2: returns samples where metadata key \"foo\" is not 'bar'. Samples\nwithout any metadata are filtered out.\n[\n { \"no_metadata\": false },\n { \"filters_combinator\": \"or\" },\n { \"key\": \"foo\", \"op\": \"neq\", \"values\": [\"bar\"] }\n]\n"

minDate
string<date-time>

Only include samples that where added after the date given

Example:

"2023-01-01T00:00:00.000Z"

maxDate
string<date-time>

Only include samples that were added before the date given

Example:

"2024-12-31T00:00:00.000Z"

Response

200 - application/json

OK

The response is of type object.