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Abhinav Krishna N
/
gargoyle-props
Public
Crop sample ''
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Edit labels for '''
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startMs and endMs given as a multiple of the sample interval (ms).
Timestamps are inclusive - so
{"startMs": 200, "endMs": 202 } includes 200, 201 and 202.
Edit labels for '''
You'll need to provide string key-value pairs for each label in the sample, for example:
{ "color": "blue", "type": "car" }.
To remove a label, remove the key and value from the label map.
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Alternatively, you can use our
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to programmatically ingest data in various formats, such as pandas or numpy;
or import data directly from another project.
For CSV and Parquet files, configure the CSV Wizard to define how your files should be processed before uploading files.
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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Using the info.labels file for labels and category.
Image label format
Labeling method
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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)
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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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