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.
Project ID
Sample ID
Whether to return the debug information from FOMO classification.
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.
POST /v1/api/{projectId}/classify/v2/{sampleId} HTTP/1.1
Host: studio.edgeimpulse.com
x-api-key: YOUR_API_KEY
Accept: */*
OK
{
"success": true,
"error": "text",
"classifications": [
{
"learnBlock": {
"id": 1,
"type": "anomaly",
"name": "NN Classifier",
"dsp": [
27
],
"title": "Classification (Keras)",
"createdBy": "createImpulse",
"createdAt": "2025-07-05T10:55:49.547Z"
},
"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": "2025-07-05T10:55:49.547Z",
"lastModified": "2025-07-05T10:55:49.547Z",
"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": "2025-07-05T10:55:49.547Z",
"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,
"warning": "text"
}
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