curl --request GET \
--url https://studio.edgeimpulse.com/v1/api/{projectId}/classify/all/result/page \
--header 'x-api-key: <api-key>'{
"success": true,
"result": [
{
"sampleId": 123,
"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>"
},
"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>"
}
],
"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"
}
]
}
],
"predictions": [
{
"sampleId": 123,
"startMs": 123,
"endMs": 123,
"prediction": "<string>",
"label": "<string>",
"predictionCorrect": true,
"f1Score": 123,
"anomalyScores": [
[
123
]
]
}
],
"error": "<string>"
}Get classify job result, containing the predictions for a given page.
curl --request GET \
--url https://studio.edgeimpulse.com/v1/api/{projectId}/classify/all/result/page \
--header 'x-api-key: <api-key>'{
"success": true,
"result": [
{
"sampleId": 123,
"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>"
},
"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>"
}
],
"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"
}
]
}
],
"predictions": [
{
"sampleId": 123,
"startMs": 123,
"endMs": 123,
"prediction": "<string>",
"label": "<string>",
"predictionCorrect": true,
"f1Score": 123,
"anomalyScores": [
[
123
]
]
}
],
"error": "<string>"
}Documentation Index
Fetch the complete documentation index at: https://docs.nordic.edgeimpulse.com/llms.txt
Use this file to discover all available pages before exploring further.
Project ID
Maximum number of results
Offset in results, can be used in conjunction with LimitResultsParameter to implement paging.
Keras model variant
int8, float32, akida 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.