Single page of a classify job result
Get classify job result, containing the predictions for a given page.
Project ID
Maximum number of results
Offset in results, can be used in conjunction with LimitResultsParameter to implement paging.
Keras model variant
Impulse ID. If this is unset then the default impulse is used.
If true, only a slice of labels will be returned for samples with multiple labels.
Only include samples with a label within the given list of labels, given as a JSON string
["idle", "snake"]Only include samples whose filename includes the given filename
Only include samples shorter than the given length, in milliseconds
Only include samples longer than the given length, in milliseconds
Only include samples with higher frequency than given frequency, in hertz
Only include samples with lower frequency than given frequency, in hertz
Include samples with either valid or invalid signatures
Only include samples with a label >= this value
Only include samples with a label < this value
Search query
<id> <name>Include only samples with a particular data type
Include only samples with an ID >= this value
Include only samples with an ID < this value
Filter samples by metadata key-value pairs, provided as a JSON string. Each filter item in the list is combined using a logical OR. To include samples without any metadata, use: { "no_metadata": true }.
["[{ \"key\": \"locationId\", \"op\": \"eq\", \"values\": [\"buildingA\"] }, { \"key\": \"deviceId\", \"op\": \"neq\", \"values\": [\"val1\", \"val2\"] }]","[{ \"key\": \"distance\", \"op\": \"eq\", \"values\": [\"1.5\", \"2.9\"], \"key\": \"comments\", exclude: [\"faulty\"] }]","[{ \"key\": \"nullKeyExample\", \"op\": \"eq\" }]","[{ \"key\": \"presentKeyExample\", \"op\": \"neq\" }]","[{ \"no_metadata\": true }]"]Only include samples that where added after the date given
2023-01-01T00:00:00.000ZOnly include samples that were added before the date given
2024-12-31T00:00:00.000ZGET /v1/api/{projectId}/classify/all/result/page HTTP/1.1
Host: studio.edgeimpulse.com
x-api-key: YOUR_API_KEY
Accept: */*
OK
{
"success": true,
"error": "text",
"result": [
{
"sampleId": 1,
"sample": {
"id": 2,
"filename": "idle01.d8Ae",
"signatureValidate": true,
"signatureMethod": "HS256",
"signatureKey": "text",
"created": "2025-08-04T17:20:52.017Z",
"lastModified": "2025-08-04T17:20:52.017Z",
"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": "2025-08-04T17:20:52.017Z",
"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"
},
"classifications": [
{
"learnBlock": {
"id": 1,
"type": "anomaly",
"name": "NN Classifier",
"dsp": [
27
],
"title": "Classification (Keras)",
"createdBy": "createImpulse",
"createdAt": "2025-08-04T17:20:52.017Z"
},
"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
}
]
}
]
}
],
"predictions": [
{
"sampleId": 1,
"startMs": 1,
"endMs": 1,
"label": "text",
"prediction": "text",
"predictionCorrect": true,
"f1Score": 1,
"anomalyScores": [
[
1
]
]
}
]
}Last updated
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