Zin / basketviewer 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' (0 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.

Object detection

Score threshold

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.

Device "" is now connected

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

You can connect almost any device over serial using our data forwarder:

$ edge-impulse-data-forwarder

Or connect a supported device or development board:

We're always looking for ways to improve Edge Impulse. If you have any feedback, please let us know!

Subject

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How was your experience?

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You'll need a free Edge Impulse account to clone this project. Creating an account lets you add your own data, modify models, and join a community of thousands of embedded machine learning developers!

Enter the name for your new project:

Enter a name for your new project

Choose your project type:

Personal

20 min job limit, 4GB or 4 hours of data, limited collaboration.

Enterprise

No job or data size limits, higher performance, custom blocks.

Choose your project setting:

Public

Anyone on the internet can view and clone this project under the licence: Apache 2.0. Only invited users will be able to edit.

Private

( of remaining)

Only invited users can edit and view your project.

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.

Target device

Processor family

Clock rate

| MHz

Max

Custom device name

Application budget

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.

RAM

| KB

Max

ROM

| KB

Max

Latency

| ms

Max

YEARLY

$400 /month

billed annually

SAVE 15%

MONTHLY

$475 /month

billed monthly

Classify new data

Classify existing test sample

Model variants

Classifying ...

Classification result

Summary

Detailed result

Raw data / Classification result

Start classification to load...

Raw features

View

Ground truth

Predictions

Image

Processed features

Settings