TinyML_Cloud - Data acquisition

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Swapnil Verma / TinyML_Cloud Public

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Swapnil Verma / TinyML_Cloud Public

Clone this project

Dataset Data explorer Data sources

Data collected

2,072 items

Train / test split

79% / 21%

Dataset

Training (1,636)

Test (436)

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By label

Clear_Sky (282)

Patterened_Cloud (289)

Thick_Dark_Cloud (271)

Thick_White_Cloud (264)

Thin_White_Cloud (268)

Veil_Cloud (262)

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By length

By frequency

By signature validity

Enabled & disabled samples

Sample name	Label	Signature	Sensors
F_img349.png.25ns2s7f	Veil_Cloud	HS256	image	Download
F_img345.png.25ns2s6q	Veil_Cloud	HS256	image	Download
F_img350.png.25ns2s7h	Veil_Cloud	HS256	image	Download
F_img346.png.25ns2s71	Veil_Cloud	HS256	image	Download
F_img341.png.25ns2s62	Veil_Cloud	HS256	image	Download
F_img343.png.25ns2s6d	Veil_Cloud	HS256	image	Download
F_img344.png.25ns2s6k	Veil_Cloud	HS256	image	Download
F_img338.png.25ns2s5h	Veil_Cloud	HS256	image	Download
F_img342.png.25ns2s63	Veil_Cloud	HS256	image	Download
F_img340.png.25ns2s5p	Veil_Cloud	HS256	image	Download
F_img339.png.25ns2s5o	Veil_Cloud	HS256	image	Download
F_img335.png.25ns2rtu	Veil_Cloud	HS256	image	Download

Add data

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Raw data

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Metadata

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updown

2m 4s

Crop sample ''

Zoom Add Segment Click on the chart to add a new segment

Set sample length (ms.):

Set segment length (ms.):

Visible range: ms - ms

Shift samples

Edit labels for ''

Use your mouse to drag a box around an object to add a label.

Edit labels for '''

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.

Add existing data

Upload data

Add storage bucket

Upload data

You can upload existing data to your project in the Data Acquisition Format (CBOR, JSON, CSV), or as WAV, JPG, PNG, AVI or MP4 files. We also support uploading image datasets with labels in various formats. When you include labels during upload, we attempt to convert your dataset into a format recognized by Studio. here.

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.

Upload mode

Select individual files

Select a folder

Select files

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.

Upload into category

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

Automatically split between training and testing

Training

Testing

Label

Infer from filename

Leave data unlabeled

Enter label:

You need to specify a label

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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Label format

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Example dataset

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Collect data directly from your phone, computer, device, or development board.

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Use the 'Record new data' section to collect data from this device.

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Connect your device or development board

You can connect almost any device over serial using our data forwarder:

$ edge-impulse-data-forwarder

Or connect a supported device or development board:

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Export data

Export raw data

Export type

Retain crops and splits

Export output

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Notification settings

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	Name
	Swapnil Verma

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Edit labels

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The selected samples contain the following labels. Which ones do you want to edit?

Set the label

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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