Classify job result
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Get classify job result, containing the result for the complete testing dataset.
/api/{projectId}/classify/all/result
Project ID
Whether to get only the classification results relevant to the feature explorer.
Keras model variant
int8, float32, akidaImpulse ID. If this is unset then the default impulse is used.
curl -L \
--url 'https://studio.edgeimpulse.com/v1/api/{projectId}/classify/all/result' \
--header 'x-api-key: YOUR_API_KEY'{
"success": true,
"error": "text",
"result": [
{
"sampleId": 1,
"sample": {
"id": 2,
"filename": "idle01.d8Ae",
"signatureValidate": true,
"signatureMethod": "HS256",
"signatureKey": "text",
"created": "2025-02-21T18:03:04.858Z",
"lastModified": "2025-02-21T18:03:04.858Z",
"category": "training",
"coldstorageFilename": "text",
"label": "healthy-machine",
"intervalMs": 16,
"frequency": 62.5,
"originalIntervalMs": 16,
"originalFrequency": 62.5,
"deviceName": "text",
"deviceType": "text",
"valuesCount": 1,
"totalLengthMs": 1,
"added": "2025-02-21T18:03:04.858Z",
"thumbnailVideo": "text",
"thumbnailVideoFull": "text",
"isDisabled": true,
"isProcessing": true,
"processingJobId": 1,
"processingError": true,
"processingErrorString": "text",
"isCropped": true,
"projectId": 1,
"projectOwnerName": "text",
"projectName": "text",
"sha256Hash": "text",
"createdBySyntheticDataJobId": 1,
"boundingBoxesType": "object_detection",
"chartType": "chart",
"projectLabelingMethod": "single_label",
"sensors": [
{
"name": "accX",
"units": "text"
}
],
"boundingBoxes": [
{
"label": "text",
"x": 1,
"y": 1,
"width": 1,
"height": 1
}
],
"metadata": {
"ANY_ADDITIONAL_PROPERTY": "text"
},
"structuredLabels": [
{
"startIndex": 1,
"endIndex": 1,
"label": "text"
}
],
"structuredLabelsList": [
"text"
],
"imageDimensions": {
"width": 1,
"height": 1
}
},
"classifications": [
{
"learnBlock": {
"id": 1,
"type": "anomaly",
"name": "NN Classifier",
"title": "Classification (Keras)",
"description": "Reduced learning rate and more layers",
"createdBy": "createImpulse",
"createdAt": "2025-02-21T18:03:04.858Z",
"dsp": [
27
]
},
"minimumConfidenceRating": 1,
"objectDetectionLastLayer": "mobilenet-ssd",
"anomalyResult": [
{
"meanScore": 1,
"maxScore": 1,
"boxes": [
{
"label": "text",
"x": 1,
"y": 1,
"width": 1,
"height": 1,
"score": 1
}
],
"scores": [
[
1
]
]
}
],
"structuredResult": [
{
"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",
"labels": [
"text"
],
"scores": [
1
],
"boxes": [
[
1
]
]
}
],
"details": [
{
"mAP": 1,
"f1": 1,
"labels": [
1
],
"scores": [
1
],
"boxes": [
[
1
]
]
}
],
"expectedLabels": [
{
"startIndex": 1,
"endIndex": 1,
"label": "text"
}
],
"result": [
{
"idle": 0.0002,
"wave": 0.9998,
"anomaly": -0.42
}
]
}
]
}
],
"predictions": [
{
"sampleId": 1,
"startMs": 1,
"endMs": 1,
"label": "text",
"prediction": "text",
"predictionCorrect": true,
"f1Score": 1,
"anomalyScores": [
[
1
]
]
}
],
"availableVariants": [
"int8"
],
"additionalMetricsByLearnBlock": [
{
"learnBlockId": 1,
"learnBlockName": "text",
"additionalMetrics": [
{
"name": "text",
"value": "text",
"fullPrecisionValue": 1,
"tooltipText": "text",
"link": "text"
}
]
}
],
"accuracy": {
"accuracyScore": 1,
"mseScore": 1,
"allLabels": [
"text"
],
"totalSummary": {
"good": 1,
"bad": 1
},
"confusionMatrixValues": {
"ANY_ADDITIONAL_PROPERTY": {
"ANY_ADDITIONAL_PROPERTY": 1
}
},
"summaryPerClass": {
"ANY_ADDITIONAL_PROPERTY": {
"good": 1,
"bad": 1
}
}
}
}OK