curl --request POST \
--url https://studio.edgeimpulse.com/v1/api/{projectId}/jobs/train/anomaly/{learnId} \
--header 'Content-Type: application/json' \
--header 'x-api-key: <api-key>' \
--data '{
"axes": [
0,
11,
22
],
"clusterCount": 32,
"minimumConfidenceRating": 0.3,
"skipEmbeddingsAndMemory": true,
"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
}
]
}'
{
"success": true,
"error": "<string>",
"id": 12873488112
}
Take the output from a DSP block and train an anomaly detection model using K-means or GMM. Updates are streamed over the websocket API.
curl --request POST \
--url https://studio.edgeimpulse.com/v1/api/{projectId}/jobs/train/anomaly/{learnId} \
--header 'Content-Type: application/json' \
--header 'x-api-key: <api-key>' \
--data '{
"axes": [
0,
11,
22
],
"clusterCount": 32,
"minimumConfidenceRating": 0.3,
"skipEmbeddingsAndMemory": true,
"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
}
]
}'
{
"success": true,
"error": "<string>",
"id": 12873488112
}
Project ID
Learn Block ID, use the impulse functions to retrieve the ID
OK
The response is of type object
.