curl --request GET \
--url https://studio.edgeimpulse.com/v1/api/{projectId}/classify/all/result \
--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>",
"labelMap": {
"type": "key-values",
"labels": {}
}
},
"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"
}
]
}
],
"predictions": [
{
"sampleId": 123,
"startMs": 123,
"endMs": 123,
"prediction": "<string>",
"label": "<string>",
"predictionCorrect": true,
"f1Score": 123,
"anomalyScores": [
[
123
]
],
"boundingBoxes": [
{
"label": "<string>",
"x": 123,
"y": 123,
"width": 123,
"height": 123,
"score": 123
}
]
}
],
"accuracy": {
"totalSummary": {
"good": 123,
"bad": 123
},
"summaryPerClass": {},
"confusionMatrixValues": {},
"allLabels": [
"<string>"
],
"accuracyScore": 123,
"balancedAccuracyScore": 123,
"anomalyAccuracyScore": 123,
"noAnomalyAccuracyScore": 123,
"mseScore": 123
},
"additionalMetricsByLearnBlock": [
{
"learnBlockId": 123,
"learnBlockName": "<string>",
"additionalMetrics": [
{
"name": "<string>",
"value": "<string>",
"fullPrecisionValue": 123,
"tooltipText": "<string>",
"link": "<string>"
}
]
}
],
"availableVariants": [
"int8"
],
"error": "<string>",
"noResultsBecauseThresholdsChanged": "can_regenerate_model_summary"
}Get classify job result, containing the result for the complete testing dataset.
curl --request GET \
--url https://studio.edgeimpulse.com/v1/api/{projectId}/classify/all/result \
--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>",
"labelMap": {
"type": "key-values",
"labels": {}
}
},
"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"
}
]
}
],
"predictions": [
{
"sampleId": 123,
"startMs": 123,
"endMs": 123,
"prediction": "<string>",
"label": "<string>",
"predictionCorrect": true,
"f1Score": 123,
"anomalyScores": [
[
123
]
],
"boundingBoxes": [
{
"label": "<string>",
"x": 123,
"y": 123,
"width": 123,
"height": 123,
"score": 123
}
]
}
],
"accuracy": {
"totalSummary": {
"good": 123,
"bad": 123
},
"summaryPerClass": {},
"confusionMatrixValues": {},
"allLabels": [
"<string>"
],
"accuracyScore": 123,
"balancedAccuracyScore": 123,
"anomalyAccuracyScore": 123,
"noAnomalyAccuracyScore": 123,
"mseScore": 123
},
"additionalMetricsByLearnBlock": [
{
"learnBlockId": 123,
"learnBlockName": "<string>",
"additionalMetrics": [
{
"name": "<string>",
"value": "<string>",
"fullPrecisionValue": 123,
"tooltipText": "<string>",
"link": "<string>"
}
]
}
],
"availableVariants": [
"int8"
],
"error": "<string>",
"noResultsBecauseThresholdsChanged": "can_regenerate_model_summary"
}Project ID
Whether to get only the classification results relevant to the feature explorer.
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.
OK
Whether the operation succeeded
Show child attributes
Show child attributes
Show child attributes
Show child attributes
List of all model variants for which classification results exist
int8, float32, akida Optional error description (set if 'success' was false)
If set to true, there are currently no results because thresholds were changed (e.g. on live classification); and what action you can run to get new results the quickest. If the value is "can_regenerate_model_summary" you can run 'regenerateModelTestingSummary'. If the value is "should_rerun_full_job", you need to run 'startClassifyJob' or 'startEvaluateJob'.
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