Thomas Garcia
/
vacuum-recognition-demo
Public
Set window increase
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.
Add existing data
Upload data
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.
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.
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.
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.
Upload output
Click 'Upload data' to begin
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