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On this page
  • Add data to your project
  • Collect data
  • Upload existing datasets
  • Data sample preview
  • Time-series data samples
  • Non-time-series & pre-processed data samples
  • Images data samples
  • Video data samples
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  • Dataset train/test split ratio
  • Data acquisition filters
  • Data sample actions
  • Edit label(s)
  • Cropping samples
  • Splitting data sample
  • Labeling tools

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  1. Edge Impulse Studio

Data acquisition

PreviousDevicesNextUploader

Last updated 18 days ago

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All collected data for each project can be viewed on the Data acquisition tab. You can see how your data has been split for train/test set as well as the data distribution for each class in your dataset. You can also send new sensor data to your project either by file upload, WebUSB, Edge Impulse API, or Edge Impulse CLI.

Add data to your project

Organization data

Since the creation of Edge Impulse, we have been helping our customers deal with complex data pipelines, complex data transformation methods and complex clinical validation studies.

The organizational data gives you tools to centralize, validate and transform datasets so they can be easily imported into your projects.

Collect data

The panel on the right allows you to collect data directly from any fully supported platform:

When using the Edge Impulse for Linux CLI, run edge-impulse-linux --clean and it will add your platform to the device list of your project. You will then will be able to interact with it from the Collect data panel.

Need more?

Upload existing datasets

Edge Impulse Datasets

These datasets highlight specific use cases, helping you understand the types of data commonly encountered in projects like object detection, audio classification, and visual anomaly detection.

Data sample preview

Time-series data samples

For time-series data samples (including audio), you can visualize the time-series graphs on the right panel with a dark-blue background:

If you are dealing with multi-label data samples. Here is the corresponding preview:

Non-time-series & pre-processed data samples

Preview the values of tabular non-time-series & pre-processed data samples:

Images data samples

Raw images can be directly visualized from the preview:

For object detection projects, we can overlay the corresponding bounding boxes:

Video data samples

Raw videos (.mp4) can be directly visualized from the preview. Please note that you will need to split the videos into frames as we do not support training on videos files:

Dataset overview

List view

Grid view

Dataset train/test split ratio

The train/test split is a technique for training and evaluating the performance of machine learning algorithms. It indicates how your data is split between training and testing samples. For example, an 80/20 split indicates that 80% of the dataset is used for model training purposes while 20% is used for model testing.

This section also shows how your data samples in each class are distributed to prevent imbalanced datasets which might introduce bias during model training.

Data acquisition filters

Manually navigating to some categories of data can be time-consuming, especially when dealing with a large dataset. The data acquisition filter enables the user to filter data samples based on some criteria of choice. This can be based on:

  • Label - class to which a sample represents.

  • Sample name - unique ID representing a sample.

  • Signature validity

  • Enabled and disabled samples

  • Length of sample - duration of a sample.

The filtered samples can then be manipulated by editing labels, deleting, and moving from the training set to the testing set (and vice versa), a shown in the image above.

Data sample actions

The data manipulations above can also be applied at the data sample level by simply navigating to the individual data sample by clicking on "â‹®" and selecting the type of action you might want to perform on the specific sample. This might be renaming, editing its label, disabling, cropping, splitting, downloading, and even deleting the sample when desired.

Edit label(s)

  • Single label

  • Multi-label

Cropping samples

To crop a data sample, go to the sample you want to crop and click â‹®, then select Crop sample. You can specify a length, or drag the handles to resize the window, then move the window around to make your selection.

Made a wrong crop? No problem, just click Crop sample again and you can move your selection around. To undo the crop, just set the sample length to a high number, and the whole sample will be selected again.

Splitting data sample

Besides cropping you can also split data automatically. Here you can perform one motion repeatedly, or say a keyword over and over again, and the events are detected and can be stored as individual samples. This makes it easy to very quickly build a high-quality dataset of discrete events. To do so head to Data Acquisition, record some new data, click, and select Split sample. You can set the window length, and all events are automatically detected. If you're splitting audio data you can also listen to events by clicking on the window, the audio player is automatically populated with that specific split.

Samples are automatically centered in the window, which might lead to problems on some models (the neural network could learn a shortcut where data in the middle of the window is always associated with a certain label), so you can select "Shift samples" to automatically move the data a little bit around.

Splitting data is - like cropping data - non-destructive. If you're not happy with a split just click Crop sample and you can move the selection around easily.

Labeling tools

If you are not dealing with an object detection task, you can simply change the Labeling method configuration by going to Dashboard > Project info > Labeling method and clicking the dropdown and selecting "one label per data item" as shown in the image below.

See the documentation.

Through .

Using the .

From the .

The WebUSB and the Edge Impulse daemon work with any fully supported device by flashing the pre-built Edge Impulse firmware to your board. See the list of .

If your device is not in the officially supported list, you can also collect data using the by directly writing the sensor values over a serial connection. The "data forwarder" then signs the data and sends it to the ingestion service.

Edge Impulse also supports different and (Pascal VOC, YOLO TXT, COCO JSON, Edge Impulse Object Detection, OpenImage CSV) that you can import into your project to build your edge AI models:

(Enterprise feature)

Need inspiration? Check out our that contains publicly available datasets collected, generated or curated by Edge Impulse or its partners.

You can change the default view (list) to a grid view to quickly overview your datasets by clicking on the icon.

See for more information.

The will only appear on your data acquisition page if you are dealing with object detection tasks.

Also, see our tutorial to see how to leverage the power of LLMs to automatically label your data samples based on simple prompts.

Organization data
WebUSB
Edge Impulse CLI daemon
Edge Impulse for Linux CLI
fully supported boards
CLI data forwarder
data sample formats
dataset annotation formats
Studio uploader
CLI uploader
CSV Wizard
Ingestion API
Import from cloud storage
Upload portals
Edge Impulse datasets collection
labeling queue
Label image data using GPT-4o
Data Acquisition -> Multi-label -> Edit multi-label samples
Edge Impulse Studio - Data acquisition view.
Time-series data sample preview
Multi-label sample preview
Tabular data sample preview
Raw image sample preview
Object detection sample preview
Video samples preview
Edge Impulse Studio - Data acquisition view.
Edge Impulse Studio - Data acquisition view.
Rebalance panel
Filters
Actions
Edit label
Edit labels
Crop
Split
Labeling method