The multi-label feature brings considerable value by preserving the context of longer data samples, simplifying data preparation, and enabling more efficient and effective data analysis.
The first improvement is in the way you can analyze and process complex datasets, especially for applications where context and continuity are crucial. With this feature, you can maintain the integrity of longer-duration samples, such as hour-long exercise sessions or night-long sleep studies, without the need to segment these into smaller fragments every time there is a change in activity. This holistic view not only preserves the context but also provides a richer data set for analysis.
Then, the ability to select window sizes directly in Edge Impulse addresses a common pain point - data duplication. Without the multi-label feature, you need to pre-process data, either externally or using transformation jobs, creating multiple copies of the same data with different window sizes to determine the optimal configuration. This process is not only time-consuming but also prone to errors and inefficiencies. With multi-label samples, adjusting the window size becomes a simple parameter change in the "Impulse design", streamlining the process significantly. This flexibility saves time, reduces the risk of errors, and allows for more dynamic experimentation with data, leading to potentially more accurate and insightful models.
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If your dataset is in the CSV format and contains a label column, the CSV Wizard is probably the easiest method to import your multi-label data.
For example:
Once your CSV Wizard is configured, you can use the Studio Uploader, the CLI Uploader or the Ingestion API:
info.labels description fileThe other way is to create a info.labels file, present in your dataset. Edge Impulse will automatically detect it when you upload your dataset and will use this file to set the labels.
The info.labels looks like the following:
Tip
You can export a public project dataset that uses the multi-label feature to understand how the info.labels is structured.
Check the Resources section for multi-label public projects.
Once you have your info.labels file available, to upload it, you can use:
The Studio Uploader:
The Studio Uploader will automatically detect the info.labels file:
The CLI Uploader:
The Ingestion API
Please note that you can also hide the sensors in the graph:
To edit the labels using the UI, click â‹® -> Edit labels. The following model will appear:
Please note that you will need to provide continuous and non-overlapping labels for the full length of your data sample.
The format is the like following:
In the Live classification tab, you can classify your multi-label test samples:
Labeling UI is available but is only text-based.
Overlapping labels are not supported
The entire data sample needs to have a label, you cannot leave parts unlabeled.
Please, leave us a note on the forum or feedback using the "?" widget (bottom-right corner) if you see a need or an issue. This can help us prioritize the development or improvement of the features.
