curl --request GET \
--url https://studio.edgeimpulse.com/v1/api/{projectId}/classify/{sampleId} \
--header 'x-api-key: <api-key>'{
"success": true,
"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
}
],
"minimumConfidenceRating": 123,
"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.",
"value": 0.5,
"suggestedValue": 123,
"suggestedValueText": "<string>",
"dropdownOptions": [
{
"description": "<string>",
"value": "<string>"
}
]
}
],
"anomalyResult": [
{
"boxes": [
{
"label": "<string>",
"x": 123,
"y": 123,
"width": 123,
"height": 123,
"score": 123
}
],
"scores": [
[
123
]
],
"meanScore": 123,
"maxScore": 123
}
],
"structuredResult": [
{
"boxes": [
[
123
]
],
"scores": [
123
],
"mAP": 123,
"f1": 123,
"precision": 123,
"recall": 123,
"labels": [
"<string>"
],
"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"
}
],
"details": [
{
"boxes": [
[
123
]
],
"labels": [
123
],
"scores": [
123
],
"mAP": 123,
"f1": 123
}
],
"objectDetectionLastLayer": "mobilenet-ssd"
}
],
"sample": {
"sample": {
"id": 2,
"filename": "idle01.d8Ae",
"signatureValidate": true,
"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,
"deviceType": "<string>",
"sensors": [
{
"name": "accX",
"units": "<string>"
}
],
"valuesCount": 123,
"added": "2023-11-07T05:31:56Z",
"boundingBoxes": [
{
"label": "<string>",
"x": 123,
"y": 123,
"width": 123,
"height": 123
}
],
"boundingBoxesType": "object_detection",
"chartType": "chart",
"isDisabled": true,
"isProcessing": true,
"processingError": true,
"isCropped": true,
"projectId": 123,
"sha256Hash": "<string>",
"signatureMethod": "HS256",
"signatureKey": "<string>",
"deviceName": "<string>",
"totalLengthMs": 123,
"thumbnailVideo": "<string>",
"thumbnailVideoFull": "<string>",
"processingJobId": 123,
"processingErrorString": "<string>",
"metadata": {},
"projectOwnerName": "<string>",
"projectName": "<string>",
"projectLabelingMethod": "single_label",
"structuredLabels": [
{
"startIndex": 123,
"endIndex": 123,
"label": "<string>"
}
],
"structuredLabelsList": [
"<string>"
],
"createdBySyntheticDataJobId": 123,
"imageDimensions": {
"width": 123,
"height": 123
},
"videoUrl": "<string>",
"videoUrlFull": "<string>",
"labelMap": {
"type": "key-values",
"labels": {}
}
},
"payload": {
"device_type": "DISCO-L475VG-IOT01A",
"sensors": [
{
"name": "accX",
"units": "<string>"
}
],
"values": [
[
123
]
],
"device_name": "ac:87:a3:0a:2d:1b",
"cropStart": 0,
"cropEnd": 128
},
"totalPayloadLength": 123
},
"windowSizeMs": 2996,
"windowIncreaseMs": 10,
"alreadyInDatabase": true,
"error": "<string>",
"warning": "<string>"
}This API is deprecated, use classifySampleV2 instead (/v1/api/{projectId}/classify/v2/{sampleId}). 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.
curl --request GET \
--url https://studio.edgeimpulse.com/v1/api/{projectId}/classify/{sampleId} \
--header 'x-api-key: <api-key>'{
"success": true,
"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
}
],
"minimumConfidenceRating": 123,
"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.",
"value": 0.5,
"suggestedValue": 123,
"suggestedValueText": "<string>",
"dropdownOptions": [
{
"description": "<string>",
"value": "<string>"
}
]
}
],
"anomalyResult": [
{
"boxes": [
{
"label": "<string>",
"x": 123,
"y": 123,
"width": 123,
"height": 123,
"score": 123
}
],
"scores": [
[
123
]
],
"meanScore": 123,
"maxScore": 123
}
],
"structuredResult": [
{
"boxes": [
[
123
]
],
"scores": [
123
],
"mAP": 123,
"f1": 123,
"precision": 123,
"recall": 123,
"labels": [
"<string>"
],
"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"
}
],
"details": [
{
"boxes": [
[
123
]
],
"labels": [
123
],
"scores": [
123
],
"mAP": 123,
"f1": 123
}
],
"objectDetectionLastLayer": "mobilenet-ssd"
}
],
"sample": {
"sample": {
"id": 2,
"filename": "idle01.d8Ae",
"signatureValidate": true,
"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,
"deviceType": "<string>",
"sensors": [
{
"name": "accX",
"units": "<string>"
}
],
"valuesCount": 123,
"added": "2023-11-07T05:31:56Z",
"boundingBoxes": [
{
"label": "<string>",
"x": 123,
"y": 123,
"width": 123,
"height": 123
}
],
"boundingBoxesType": "object_detection",
"chartType": "chart",
"isDisabled": true,
"isProcessing": true,
"processingError": true,
"isCropped": true,
"projectId": 123,
"sha256Hash": "<string>",
"signatureMethod": "HS256",
"signatureKey": "<string>",
"deviceName": "<string>",
"totalLengthMs": 123,
"thumbnailVideo": "<string>",
"thumbnailVideoFull": "<string>",
"processingJobId": 123,
"processingErrorString": "<string>",
"metadata": {},
"projectOwnerName": "<string>",
"projectName": "<string>",
"projectLabelingMethod": "single_label",
"structuredLabels": [
{
"startIndex": 123,
"endIndex": 123,
"label": "<string>"
}
],
"structuredLabelsList": [
"<string>"
],
"createdBySyntheticDataJobId": 123,
"imageDimensions": {
"width": 123,
"height": 123
},
"videoUrl": "<string>",
"videoUrlFull": "<string>",
"labelMap": {
"type": "key-values",
"labels": {}
}
},
"payload": {
"device_type": "DISCO-L475VG-IOT01A",
"sensors": [
{
"name": "accX",
"units": "<string>"
}
],
"values": [
[
123
]
],
"device_name": "ac:87:a3:0a:2d:1b",
"cropStart": 0,
"cropEnd": 128
},
"totalPayloadLength": 123
},
"windowSizeMs": 2996,
"windowIncreaseMs": 10,
"alreadyInDatabase": true,
"error": "<string>",
"warning": "<string>"
}Whether to return the debug information from FOMO classification.
Impulse ID. If this is unset then the default impulse is used.
OK
Whether the operation succeeded
Show child attributes
Show child attributes
Size of the sliding window (as set by the impulse) in milliseconds.
2996
Number of milliseconds that the sliding window increased with (as set by the impulse)
10
Whether this sample is already in the training database
Optional error description (set if 'success' was false)
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