After creating your Edge Impulse Studio project, you will be directed to the project's dashboard. The dashboard gives a quick overview of your project such as your project ID, the number of devices connected, the amount of data collected, the preferred labeling method, among other editable properties. You can also enable some additional capabilities to your project such as collaboration, making your project public, and showcasing your public projects using Markdown READMEs as we will see.
The figure below shows the various sections and widgets of the dashboard that we will cover here.
The project README enables you to explain the details of your project in a short way. Using this feature, you can add visualizations such as images, GIFs, code snippets, and text to your project in order to bring your colleagues and project viewers up to speed with the important details of your project. In your README you might want to add things like:
What the project does
Why the project is useful
Motivations of the project
How to get started with the project
What sensors and target deployment devices you used
How you plan to improve your project
Where users can get help with your project
To create your first README, navigate to the "about this project" widget and click "add README"
For more README inspiration, check out the public Edge Impulse project tutorials below:
To share your private project with the world, and click Make this project public.
By doing this, all of your data, block configurations, intermediate results, and final models will be shared with the world. Your project will be publicly accessible and can be cloned with a single click with the provided URL:
You can invite up to three collaborators to join and contribute to your project. To have unlimited collaborators, your project needs to be part of an organization to access unlimited team collaborations.
To add a collaborator, go to your project's dashboard and find the "Collaborators" widget. Click the '+' icon and type the username or e-mail address of the other user. The user will be invited to create an Edge Impulse account if it doesn't exist.
The user will be automatically added to the project and will get an email notification inviting them to start contributing to your project. To remove a user, simply click on the three dots besides the user then tap ‘Delete’ and they will be automatically removed.
The project info widget shows the project's specifications such as the project ID, labeling method, and latency calculations for your target device.
The project ID is a unique numerical value that identifies your project. Whenever you have any issue with your project on the studio, you can always share your project ID on the forum for assistance from edge Impulse staff.
On the labeling method dropdown, you need to specify the type of labeling your dataset and model expect. This can be either one label per data item or bounding boxes. Bounding boxes only work for object detection tasks in the studio. Note that if you interchange the labeling methods, learning blocks will appear to be hidden when building your impulse.
One of the amazing Edge Impulse superpowers is the latency calculation component. This is an approximate time in milliseconds that the trained model and DSP operations are going to take during inference based on the selected target device. This hardware in the loop approach ensures that the target deployment device compute resources are not underutilized or over-utilized. It also saves developers' time associated with numerous inference iterations back and forth the studio in search of optimum models.
In the Block Output section, you can download the results of the DSP and ML operations of your impulse.
The downloadable assets include the extracted features, Tensorflow SavedModel, and both quantized and unquantized TensorFlow lite models. This is particularly helpful when you want to perform other operations to the output blocks outside the Edge Impulse studio. For example, if you need a TensorflowJS model, you will just need to download the TensorFlow saved model from the dashboard and convert it to TensorFlowJS model format to be served on a browser.
Changing Performance Settings is only available for enterprise customers
Organizational features are only available for enterprise customers. View our pricing for more information.
This section consists of editable parameters that directly affect the performance of the studio when building your impulse. Depending on the selected or available settings, your jobs can either be fast or slow.
The use of GPU for training and Parallel DSP jobs is currently an internal experimental feature that will be soon released.
To bring even more flexibility in projects, the administrative zone gives developers the power to enable other additional features that are not found in edge impulse projects by default. Most of these features are usually advanced features intended for organizations or sometimes experimental features.
To activate these features you just need to check the boxes against the specific features you want to use and click save experiments.
The danger zone widget consists of irrevocable actions that let you to:
Delete your project. This action removes all devices, data, and impulses from your project.
Delete all data in this project.
Perform train/test split. This action re-balances your dataset by splitting all your data automatically between the training and testing set and resets the categories for all data
Launch the getting started wizard. This will remove all data, and clear out your impulse.
