Dataset
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Raw data
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Metadata
| Key | Value |
|---|
No metadata.
cosminovici
/
pm
Public
Crop sample ''
Set sample length (ms.):
Set segment length (ms.):
Visible range: ms - ms
Edit labels for '''
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.
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.
Add existing data
Upload data
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.
CSV and Parquet files will be processed using the active CSV Wizard configuration, which was created using "dataset.csv" (). Download config.
CSV and Parquet files will be processed using the active CSV Wizard configuration, which was created using "dataset.csv" (). Download config.
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 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).
Label
CSV and Parquet files will be labeled using the 'label' column, as configured in the
CSV wizard. Update configuration .
You need to specify a label
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.
Upload output
Click 'Upload data' to begin
Upload data - contact us
Please give us some information about the data you are trying to upload.
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Import data from an existing project
This will copy all data from the project into this project. Data that was already imported will be skipped.
Select project
Import progress
Collect new data
Collect data directly from your phone, computer, device, or development board.
Scan QR code to connect to your phone
Collect new data
Device "" is now connected
Use the 'Record new data' section to collect data from this device.
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:
Search supported development boards
Configure your dataset split
Use this modal to configure how your dataset is split
between training and testing,
while optionally preserving label or metadata distribution through stratification.
Choose your split and stratification options, click Preview to inspect the outcome, then click Split dataset to apply it.
Dataset split
Split output
Click 'Split dataset' to begin
Stratification
Data distribution (Test)
Edit labels
The selected items have no labels to edit
The selected samples contain the following labels. Which ones do you want to edit?
Set the label (leave empty to remove these labels):
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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