Data
Since the creation of Edge Impulse, we have been helping customers to deal with complex data pipelines, complex data transformation methods and complex clinical validation studies.
In most cases, before even thinking about machine learning algorithms, researchers need to build quality datasets from real-world data. These data come from various devices (prototype devices being developed vs clinical/industrial-grade reference devices), have different formats (excel sheets, images, csv, json, etc...), and be stored in various places (researchers' computers, Dropbox folders, Google Drive, S3 buckets, etc...).
Dealing with such complex data infrastructure is time-consuming and expensive to develop and maintain. With the organizational data, we want to give you tools to centralize, validate and transform datasets so they can be easily imported into your projects to train your machine learning models.
Health reference design
We have built a health reference design that describes an end-to-end ML workflow for building a wearable health product using Edge Impulse.
In this reference resign, we want to help you understand how to create a full clinical data pipeline by:
Before we get started, you must link your organization with one or several storage buckets. First, select where your data lives:
- AWS S3 buckets
- Google Cloud Storage
- Any S3-compatible bucket
And fill the form with your bucket name, region, endpoint access and secret keys:
Add a storage bucket
A green dot indicates that your bucket is connected:
Bucket connected to your organization
Two types of dataset structures can be used - Generic datasets (default) and Clinical datasets.
There is no required format for data files. You can upload data in any format, whether it's CSV, Parquet, or a proprietary data format.
However, to import data items to an Edge Impulse project, you will need to use the right format as our studio ingestion API only supports these formats:
- JPG, PNG images
- MP4, AVI video files
- WAV audio files
- CSV files
The default dataset structure is a file-based one, no matter the directory structure:
For example:
images/
├── testing/
│ ├── 1.jpg
│ ├── 2.jpg
│ ├── 3.jpg
│ ...
│ └── 200.jpg
└── training/
├── 1.jpg
├── 2.jpg
├── 3.jpg
...
└── 800.jpg
or:
keywords/
├── french-accent/
│ ├── hello.wav
│ ├── yes.wav
│ ├── no.wav
├── greek-accent/
│ ├── hello.wav
│ ├── yes.wav
│ ├── no.wav
└── unlabeled/
├── 1.wav
├── 2.wav
├── 3.wav
...
└── 20.wav
Note that you will be able to associate the labels of your data items from the file name or the directory name when importing your data in a project.
The clinical datasets structure in Edge Impulse has three layers:
- 1.The dataset, a larger set of data items, grouped together.
- 2.Data item, an item with metadata and files attached.
- 3.Data file, the actual files.
Datasets overview
Once you successfully linked your storage bucket to your organization, head to the Datasets tab and click on + Add new dataset:
Add new dataset
Fill out the following form:
Add dataset
Click on Create dataset
With your datasets imported, you can now navigate into your dataset, create folders, query your dataset, add data items and import your data to an Edge Impulse project.
The default view lets you navigate in your bucket following the directory structure. You can easily add data using the "+ New folder" button. To add new data, use the right panel - drag and drop your files and folders and it will automatically upload them to your bucket.
Data items overview
The clinical view is slightly different, see synchronizing clinical data with a bucket for more information. This view lets you easily query your clinical dataset but to import data, you will need to set up an upload portal or upload them directly to your bucket.
Tip: You can add two distinct datasets in Edge Impulse that point to the same bucket path, one generic and one clinical. This way you can leverage both the easy upload and the ability to query your datasets.
Clinical dataset overview
Go to the Actions...->Import data into a project, select the project you wish to import to and click Next, Configure how to label this data:
Uploading Files
This will import the data into the project and optionally create a new label for each file in the dataset. This labeling step helps you keep track of different classes or categories within your data.
After importing the data into the project, in the Next, post-sync actions step, you can configure a data pipeline to automatically retrieve and trigger actions in your project:
Label your files
We also have added a data preview feature, allowing you to visualize certain types of data directly within the organization data tab.
Supported data types include CSV, images, PDF, audio files, and text files. This feature gives you a quick overview of your data and helps ensure its integrity and correctness.
Data items overview - CVS type
Data items overview - image type
If you need to get data into your organization, you can now do this in a few simple steps. To go further and use advanced features, query your datasets or transform your dataset, please have a look at the health reference design tutorial
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Any questions, or interested in the enterprise version of Edge Impulse? Contact us for more information.
If you see the following message, make sure to add the CORS header to your bucket settings:
CORS
You can also add the CORS using the AWS S3 CLI:
aws s3api put-bucket-cors --bucket your-bucket --cors-configuration file://cors.json
with this file
cors.json:{
"CORSRules": [
{
"AllowedHeaders": ["*"],
"AllowedMethods": ["PUT", "POST"],
"AllowedOrigins": ["https://studio.edgeimpulse.com"],
"ExposeHeaders": []
}
]
}
Last modified 3d ago