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Dmitry
/
audio_scene_MFE_Conv1D v2
Public
updown
2m 4s
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Alternatively, you can use our
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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.
Select both your images and the label file(s) when uploading to apply the labels.
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Image label format
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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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.
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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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