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Tutorial: object detection YOLOv5
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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.
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Using the info.labels file for labels and category.
Image label format
Labeling method
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Select both your images and any label files when uploading to apply the labels.
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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).
Label
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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%
Labels in your dataset
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