Kite (Robot) - Data acquisition - Testing

Robin Kanattu / Kite (Robot) Public

Target: Arduino UNO Q (Qualcomm QRB2210) Clone this project

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Data collected

1,829 items

Dataset split

82% / 18%

Dataset

Training (1,502)

Test (327)

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Items per page: 100 Number of columns: 3

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anger (54)

fear (38)

joy (47)

natural (42)

sadness (48)

surprise (39)

unknown (59)

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unknown.0059

Show in live classification Download

ID: 3116491261

Label: unknown

unknown.0060

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ID: 3116491252

Label: unknown

unknown.0058

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ID: 3116491241

Label: unknown

unknown.0055

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ID: 3116491240

Label: unknown

unknown.0057

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ID: 3116491239

Label: unknown

unknown.0054

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ID: 3116491238

Label: unknown

unknown.0049

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ID: 3116491235

Label: unknown

unknown.0052

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ID: 3116491232

Label: unknown

unknown.0053

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ID: 3116491231

Label: unknown

unknown.0050

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ID: 3116491230

Label: unknown

unknown.0048

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ID: 3116491229

Label: unknown

unknown.0046

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ID: 3116491228

Label: unknown

unknown.0047

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ID: 3116491227

Label: unknown

unknown.0044

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ID: 3116491226

Label: unknown

unknown.0056

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ID: 3116491217

Label: unknown

unknown.0045

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ID: 3116491216

Label: unknown

unknown.0043

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ID: 3116491215

Label: unknown

unknown.0042

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ID: 3116491214

Label: unknown

unknown.0041

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ID: 3116491213

Label: unknown

unknown.0051

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ID: 3116491200

Label: unknown

unknown.0037

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ID: 3116491181

Label: unknown

unknown.0040

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ID: 3116491180

Label: unknown

unknown.0036

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ID: 3116491171

Label: unknown

unknown.0039

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ID: 3116491170

Label: unknown

unknown.0038

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ID: 3116491169

Label: unknown

unknown.0031

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ID: 3116491166

Label: unknown

unknown.0035

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ID: 3116491165

Label: unknown

unknown.0034

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ID: 3116491160

Label: unknown

unknown.0029

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ID: 3116491155

Label: unknown

unknown.0030

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ID: 3116491154

Label: unknown

unknown.0027

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ID: 3116491145

Label: unknown

unknown.0028

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ID: 3116491144

Label: unknown

unknown.0024

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ID: 3116491143

Label: unknown

unknown.0033

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ID: 3116491136

Label: unknown

unknown.0026

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ID: 3116491135

Label: unknown

unknown.0022

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ID: 3116491134

Label: unknown

unknown.0025

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ID: 3116491133

Label: unknown

unknown.0021

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ID: 3116491132

Label: unknown

unknown.0023

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ID: 3116491131

Label: unknown

unknown.0032

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ID: 3116491116

Label: unknown

unknown.0020

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ID: 3116491103

Label: unknown

unknown.0014

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ID: 3116491096

Label: unknown

unknown.0003

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ID: 3116491095

Label: unknown

unknown.0005

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ID: 3116491094

Label: unknown

unknown.0017

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ID: 3116491093

Label: unknown

unknown.0019

Show in live classification Download

ID: 3116491090

Label: unknown

unknown.0018

Show in live classification Download

ID: 3116491089

Label: unknown

unknown.0016

Show in live classification Download

ID: 3116491080

Label: unknown

unknown.0006

Show in live classification Download

ID: 3116491075

Label: unknown

unknown.0012

Show in live classification Download

ID: 3116491074

Label: unknown

unknown.0001

Show in live classification Download

ID: 3116491067

Label: unknown

unknown.0013

Show in live classification Download

ID: 3116491066

Label: unknown

unknown.0002

Show in live classification Download

ID: 3116491065

Label: unknown

unknown.0015

Show in live classification Download

ID: 3116491062

Label: unknown

unknown.0004

Show in live classification Download

ID: 3116491061

Label: unknown

unknown.0007

Show in live classification Download

ID: 3116491058

Label: unknown

unknown.0010

Show in live classification Download

ID: 3116491043

Label: unknown

unknown.0009

Show in live classification Download

ID: 3116491034

Label: unknown

unknown.0011

Show in live classification Download

ID: 3116491031

Label: unknown

surprise.0001

Show in live classification Download

ID: 3116489479

Label: surprise

surprise.0002

Show in live classification Download

ID: 3116489478

Label: surprise

sadness.0003

Show in live classification Download

ID: 3116489364

Label: sadness

sadness.0001

Show in live classification Download

ID: 3116489363

Label: sadness

sadness.0002

Show in live classification Download

ID: 3116489362

Label: sadness

sadness.0007

Show in live classification Download

ID: 3116489361

Label: sadness

sadness.0010

Show in live classification Download

ID: 3116489360

Label: sadness

sadness.0005

Show in live classification Download

ID: 3116489359

Label: sadness

sadness.0008

Show in live classification Download

ID: 3116489358

Label: sadness

sadness.0006

Show in live classification Download

ID: 3116489357

Label: sadness

sadness.0009

Show in live classification Download

ID: 3116489356

Label: sadness

sadness.0004

Show in live classification Download

ID: 3116489355

Label: sadness

natural.0002

Show in live classification Download

ID: 3116488883

Label: natural

natural.0001

Show in live classification Download

ID: 3116488882

Label: natural

joy.0009

Show in live classification Download

ID: 3116488417

Label: joy

joy.0013

Show in live classification Download

ID: 3116488410

Label: joy

joy.0011

Show in live classification Download

ID: 3116488409

Label: joy

joy.0002

Show in live classification Download

ID: 3116488408

Label: joy

joy.0005

Show in live classification Download

ID: 3116488407

Label: joy

joy.0012

Show in live classification Download

ID: 3116488406

Label: joy

joy.0008

Show in live classification Download

ID: 3116488401

Label: joy

joy.0006

Show in live classification Download

ID: 3116488400

Label: joy

joy.0010

Show in live classification Download

ID: 3116488399

Label: joy

joy.0007

Show in live classification Download

ID: 3116488386

Label: joy

joy.0001

Show in live classification Download

ID: 3116488375

Label: joy

joy.0004

Show in live classification Download

ID: 3116488358

Label: joy

fear.0001

Show in live classification Download

ID: 3116487563

Label: fear

fear.0002

Show in live classification Download

ID: 3116487562

Label: fear

anger.0011

Show in live classification Download

ID: 3116487291

Label: anger

anger.0010

Show in live classification Download

ID: 3116487290

Label: anger

anger.0009

Show in live classification Download

ID: 3116487289

Label: anger

anger.0014

Show in live classification Download

ID: 3116487288

Label: anger

anger.0013

Show in live classification Download

ID: 3116487287

Label: anger

anger.0006

Show in live classification Download

ID: 3116487286

Label: anger

anger.0007

Show in live classification Download

ID: 3116487285

Label: anger

anger.0005

Show in live classification Download

ID: 3116487284

Label: anger

anger.0001

Show in live classification Download

ID: 3116487283

Label: anger

anger.0002

Show in live classification Download

ID: 3116487282

Label: anger

anger.0008

Show in live classification Download

ID: 3116487281

Label: anger

anger.0012

Show in live classification Download

ID: 3116487280

Label: anger

anger.0003

Show in live classification Download

ID: 3116487279

Label: anger

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You'll need to provide continuous, non-overlapping labels from 0 to ms, with 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.

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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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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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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 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
	Robin Kanattu
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Editing ''

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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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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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