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.
Learn more 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.
Using the info.labels file for labels and category.
Image label format
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).
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.