Classify a complete file against the current impulse. This will move the sliding window (dependent on
the sliding window length and the sliding window increase parameters in the impulse) over the complete
file, and classify for every window that is extracted. Depending on the size of your file, whether your
sample is resampled, and whether the result is cached you'll get either the result or a job back. If
you receive a job, then wait for the completion of the job, and then call this function again to receive
the results. The unoptimized (float32) model is used by default, and classification with an optimized
(int8) model can be slower.
Authorizations
Path parameters
projectIdintegerRequired
Project ID
sampleIdintegerRequired
Sample ID
Query parameters
includeDebugInfobooleanOptional
Whether to return the debug information from FOMO classification.
variantstring ยท enumOptional
Keras model variant
Possible values:
impulseIdintegerOptional
Impulse ID. If this is unset then the default impulse is used.
truncateStructuredLabelsbooleanOptional
If true, only a slice of labels will be returned for samples with multiple labels.
Responses
200
OK
application/json
Responseany of
all ofOptional
or
all ofOptional
post
POST /v1/api/{projectId}/classify/v2/{sampleId} HTTP/1.1
Host: studio.edgeimpulse.com
x-api-key: YOUR_API_KEY
Accept: */*
