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David Schwarz
/
Acoustic Anomaly Detection - TI LaunchPad
Public
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2m 4s
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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.
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Labeling method
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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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(e.g. samples in a 'train' directory will be uploaded into training data).
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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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Dataset train / test split ratio
Training data is used to train your model, and testing data is used to test your model's accuracy after training.
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%
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