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|---|
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Louis Moreau
/
TinyMLPerf - Speech Commands
Public
Crop sample ''
Set sample length (ms.):
Set segment length (ms.):
Visible range: ms - ms
Edit labels for '''
You'll need to provide continuous, non-overlapping labels from 0 to ms.
Timestamps are inclusive - so
{"startMs": 200, "endMs": 202 } includes 200, 201 and 202.
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Alternatively, you can use our
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or import data directly from another project.
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For CSV and Parquet 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.
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Image label format
Labeling method
Annotations in this format could not be found in the selected files.
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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.
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Advanced settings
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.
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Data distribution (Test)
Edit labels
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Dataset train / test split ratio
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We recommend an approximate 80/20 train/test split ratio for your data for every class (or label) in your dataset, although especially large datasets may require less testing data.
Suggested train / test split
80% / 20%
Labels in your dataset
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