curl --request GET \
--url https://studio.edgeimpulse.com/v1/api/{projectId}/classify/all/result \
--header 'x-api-key: <api-key>'
{
"success": true,
"error": "<string>",
"result": [
{
"sampleId": 123,
"sample": {
"id": 2,
"filename": "idle01.d8Ae",
"signatureValidate": true,
"signatureMethod": "HS256",
"signatureKey": "<string>",
"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,
"deviceName": "<string>",
"deviceType": "<string>",
"sensors": [
{
"name": "accX",
"units": "<string>"
}
],
"valuesCount": 123,
"totalLengthMs": 123,
"added": "2023-11-07T05:31:56Z",
"boundingBoxes": [
{
"label": "<string>",
"x": 123,
"y": 123,
"width": 123,
"height": 123
}
],
"boundingBoxesType": "object_detection",
"chartType": "chart",
"thumbnailVideo": "<string>",
"thumbnailVideoFull": "<string>",
"isDisabled": true,
"isProcessing": true,
"processingJobId": 123,
"processingError": true,
"processingErrorString": "<string>",
"isCropped": true,
"metadata": {},
"projectId": 123,
"projectOwnerName": "<string>",
"projectName": "<string>",
"projectLabelingMethod": "single_label",
"sha256Hash": "<string>",
"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
}
],
"anomalyResult": [
{
"boxes": [
{
"label": "<string>",
"x": 123,
"y": 123,
"width": 123,
"height": 123,
"score": 123
}
],
"scores": [
[
123
]
],
"meanScore": 123,
"maxScore": 123
}
],
"structuredResult": [
{
"boxes": [
[
123
]
],
"labels": [
"<string>"
],
"scores": [
123
],
"mAP": 123,
"f1": 123,
"precision": 123,
"recall": 123,
"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": 123,
"details": [
{
"boxes": [
[
123
]
],
"labels": [
123
],
"scores": [
123
],
"mAP": 123,
"f1": 123
}
],
"objectDetectionLastLayer": "mobilenet-ssd",
"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.",
"suggestedValue": 123,
"suggestedValueText": "<string>",
"value": 0.5
}
]
}
]
}
],
"predictions": [
{
"sampleId": 123,
"startMs": 123,
"endMs": 123,
"label": "<string>",
"prediction": "<string>",
"predictionCorrect": true,
"f1Score": 123,
"anomalyScores": [
[
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"
],
"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,
"error": "<string>",
"result": [
{
"sampleId": 123,
"sample": {
"id": 2,
"filename": "idle01.d8Ae",
"signatureValidate": true,
"signatureMethod": "HS256",
"signatureKey": "<string>",
"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,
"deviceName": "<string>",
"deviceType": "<string>",
"sensors": [
{
"name": "accX",
"units": "<string>"
}
],
"valuesCount": 123,
"totalLengthMs": 123,
"added": "2023-11-07T05:31:56Z",
"boundingBoxes": [
{
"label": "<string>",
"x": 123,
"y": 123,
"width": 123,
"height": 123
}
],
"boundingBoxesType": "object_detection",
"chartType": "chart",
"thumbnailVideo": "<string>",
"thumbnailVideoFull": "<string>",
"isDisabled": true,
"isProcessing": true,
"processingJobId": 123,
"processingError": true,
"processingErrorString": "<string>",
"isCropped": true,
"metadata": {},
"projectId": 123,
"projectOwnerName": "<string>",
"projectName": "<string>",
"projectLabelingMethod": "single_label",
"sha256Hash": "<string>",
"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
}
],
"anomalyResult": [
{
"boxes": [
{
"label": "<string>",
"x": 123,
"y": 123,
"width": 123,
"height": 123,
"score": 123
}
],
"scores": [
[
123
]
],
"meanScore": 123,
"maxScore": 123
}
],
"structuredResult": [
{
"boxes": [
[
123
]
],
"labels": [
"<string>"
],
"scores": [
123
],
"mAP": 123,
"f1": 123,
"precision": 123,
"recall": 123,
"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": 123,
"details": [
{
"boxes": [
[
123
]
],
"labels": [
123
],
"scores": [
123
],
"mAP": 123,
"f1": 123
}
],
"objectDetectionLastLayer": "mobilenet-ssd",
"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.",
"suggestedValue": 123,
"suggestedValueText": "<string>",
"value": 0.5
}
]
}
]
}
],
"predictions": [
{
"sampleId": 123,
"startMs": 123,
"endMs": 123,
"label": "<string>",
"prediction": "<string>",
"predictionCorrect": true,
"f1Score": 123,
"anomalyScores": [
[
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"
],
"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
The response is of type object
.