Aadarsh Kannan / continuous motion recognition Public

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Anomaly explorer

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' (240 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.

Classifier

Minimum confidence rating

Anomaly detection

Minimum score before tagging as anomaly

You can upload CBOR, JSON, CSV, 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.

For CSV 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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$ edge-impulse-data-forwarder

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Classify new data

Classify existing test sample

Model variants

Classifying ...

Classification result

Summary

Detailed result

Raw data

Start classification to load...

Raw features

Spectral features

Processed features

Settings

Anomaly explorer

Distance from closest cluster