Camille Soetaert / BFRB-aware-motion-detection Public

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For samples that are longer than the window length, a sliding window is used to classify the data multiple times. This setting determines the increase of the sliding window in milliseconds, for each step. This affects both live classification and model testing.

Mirror the setting from 'Create impulse' (1000 ms.)

Enter a window increase (ms.):

Every learning block has a threshold. This can be the minimum confidence that a neural network needs to have, or the maximum anomaly score before a sample is tagged as an anomaly. You can configure these thresholds to tweak the sensitivity of these learning blocks. This affects both live classification and model testing.

NN Classifier

Minimum confidence rating

Should be numeric

You can upload CBOR, JSON, CSV, Parquet, WAV, JPG, PNG, AVI or MP4 files. You can also upload an annotation file named "info.labels" with your data to assign bounding boxes, labels, and/or metadata. View Uploader docs to learn more. Alternatively, you can use our Python SDK to programmatically ingest data in various formats, such as pandas or numpy; or import data directly from another project.

For CSV and Parquet files, configure the CSV Wizard to define how your files should be processed before uploading files.

Bounding boxes: You can upload object detection datasets in any supported format. Select both your images and the label file(s) when uploading to apply the labels.

Please give us some information about the data you are trying to upload.

Dataset annotation format

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Directory structure

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Label format

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Collect data directly from your phone, computer, device, or development board.

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Use the 'Classify new data' section to collect data from this device.

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Target device

Define your target device requirements to inform model optimizations and performance calculations. No device yet? Use the default settings which you can change at any time.

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Specify the available RAM and ROM for the model's operation, along with the maximum allowed latency for your specific application. Not sure yet? Start with the defaults and modify them later on.

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Classify new data

Classify existing test sample

Classifying ...

Classification result

Summary

Model variants

Detailed result

Raw data

Start classification to load...

Raw features

Spectral features

Processed features

Settings