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Target: ST IoT Discovery Kit (Cortex-M4F 80MHz) Clone this project

Dataset Data explorer Data sources

Data collected

7m 20s

Train / test split

82% / 18%

Dataset

Training (72)

Test (16)

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crash (17)

idle (20)

Riding (35)

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Sample length (ms)

5000

Sample frequency (Hz)

100

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ID	Sample name	Label	Length	Device	Signature	Sensors
175018935	idle.3n24decd	idle	5s	ac:87:a3:0a:2d:1b	none	accX, accY, accZ @ 100Hz	Download
175018934	idle.3n24drhl	idle	5s	ac:87:a3:0a:2d:1b	none	accX, accY, accZ @ 100Hz	Download
175018933	idle.3n24f7e6	idle	5s	ac:87:a3:0a:2d:1b	none	accX, accY, accZ @ 100Hz	Download
175018932	idle.3n24gm4a	idle	5s	ac:87:a3:0a:2d:1b	none	accX, accY, accZ @ 100Hz	Download
175018931	idle.3n24i0c0	idle	5s	ac:87:a3:0a:2d:1b	none	accX, accY, accZ @ 100Hz	Download
175018930	idle.3n24jgds	idle	5s	ac:87:a3:0a:2d:1b	none	accX, accY, accZ @ 100Hz	Download
175018929	idle.3n24k9hb	idle	5s	ac:87:a3:0a:2d:1b	none	accX, accY, accZ @ 100Hz	Download
175018928	idle.3n28ag30	idle	5s	ac:87:a3:0a:2d:1b	none	accX, accY, accZ @ 100Hz	Download
175018927	idle.3n286vqd	idle	5s	ac:87:a3:0a:2d:1b	none	accX, accY, accZ @ 100Hz	Download
175018926	idle.3n33jgvd	idle	5s	ac:87:a3:0a:2d:1b	none	accX, accY, accZ @ 100Hz	Download
175018925	idle.3n33vqg5	Riding	5s	ac:87:a3:0a:2d:1b	none	accX, accY, accZ @ 100Hz	Download
175018924	idle.3n33vt21	Riding	5s	ac:87:a3:0a:2d:1b	none	accX, accY, accZ @ 100Hz	Download

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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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You can upload CBOR, JSON, CSV, Parquet, 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; 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.

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

Annotations in this format could not be found in the selected files. Select both your images and any label files when uploading to apply the labels.

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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Upload category will be derived from the structure of your dataset (e.g. samples in a 'train' directory will be uploaded into training data).

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Upload data in exact order of the info.labels file (slower)

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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	Name
	Pratyush Mallick

Editing ''

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Data distribution (Training)

Edit labels

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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

Perform train / test split

Use this option to rebalance your data, automatically splitting items between training and testing datasets. Warning: this action cannot be undone.

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