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.
You can upload existing data to your project in the
Data Acquisition Format (CBOR, JSON, CSV),
or as WAV, JPG, PNG, AVI or MP4 files.
We also support uploading image datasets with labels in various formats. When you include labels during upload,
we attempt to convert your dataset into a format recognized by Studio.
here.
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.
Upload mode
Select files
Using the info.labels file for labels and category.
Image label format
Labeling method
Annotations in this format could not be found in the selected files.
Select both your images and any label files when uploading to apply the labels.
A label map file could not be detected.
This format requires a label map file, which maps keys to the label they represent.
You can fix these labels later by clicking 'Edit labels' on the data acquisition page.
Upload into category
Upload category will be derived from the structure of your dataset
(e.g. samples in a 'train' directory will be uploaded into training data).
Label
You need to specify a label
This dataset format uses bounding box labeling, used for object detection.
The project labeling method will switch to 'bounding boxes'.
This dataset format uses one label per sample. You may wish to change your project
labeling method to 'one label per data item' in the project dashboard.
We're always looking for ways to improve Edge Impulse. If you have any feedback, please let us know!
This field is required
This field is required
Get in touch with sales
We'll work with you on your setup and help you get the most out of Edge Impulse.
This field is required
Almost there!
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!