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- Even though Edge Impulse works well for beginners and students, it is highly extensible for experts and engineers alike. The following guides can help you get started depending on your background: For beginners For ML practitioners For embedded engineers
docs.edgeimpulse.com/docs/concepts/edge-ai/what-is-edge-impulse
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Edge Impulse is the leading edge AI platform for collecting data, training models, and deploying them to your edge computing devices. It provides an end-to-end framework that easily plugs into your edge MLOps workflow.
- Frequently asked questions
Check out our documentation on Bringing your own model...
- Frequently asked questions
Check out our documentation on Bringing your own model (BYOM) into your Edge Impulse project, and using the Edge Impulse Python SDK! How does the feature explorer visualize data that has more that 3 dimensions?
Edge Impulse is a suite of tools that helps you build, test, and deploy edge AI algorithms. It covers the entire workflow, from data collection to model optimization and production monitoring. Edge Impulse increases the velocity of edge AI development while reducing costs and associated risks.
Edge Impulse empowers you to bring intelligence to your embedded projects by enabling devices to understand and respond to their environment. Whether you want to recognize sounds, identify objects, or detect motion, Edge Impulse makes it accessible and straightforward. Here's why beginners like you are diving into Edge Impulse:
- Choose Object Classes to Be Detected
- Label Bounding Boxes and Annotate Images
- Preprocess Image Data/Feature Extraction
- Choose A Model Architecture and Deep Learning Framework
- Setup Training Environment
- Setup Hyperparameters and Start Training
- Test and Validate The Model and Tune and Retrain as Required
The classes I am choosing for the project are an apple and a mug so that we will build an object detector that can classify between these two classes purely for illustrative purposes. The chosen labels we need to predict are therefore “mug” or “apple” and we want to create a model that will analyze an image frame and ideally highlight all apples an...
The labelling process is done using labelling software that allows you to visually draw the bounding box and indicate the label. There are both online and offline labelling tools available and the good news is that Edge Impulse includes a labelling tool built into the Studio. You can and may already have used an external labelling tool and not want...
Now its time to head to the Impulse design screen and create an Impulse that looks like below by clicking on the empty “add a processing block” fields, be sure to click “Save Impulse” and you will see an “Image” and “Object Detection” option appear under Impulse Design where you can tweak the parameters of the feature extraction and model respectiv...
Once again Edge Impulse helps make some sound design decisions on your behalf. In terms of the model architecture the image detection block at launch uses the MobileNetV2 SSD architecture. In the world of Object Detection there are few architectures that are popular due to their performance and you will most likely encounter YOLO, SSD and others. S...
There is no need to worry about things like Jupyter Notebooks, training environments etc as the Studio is your environment, lets move on.
The only thing that you need to consider are the Hyperparameters which are shown as Training Settings which is accessible by selecting “Object detection” under “Impulse Design”. The first option is how many epochs or training cycles which you can change, do take into account that increasing this increases training time. Learning rate refers to how ...
Now that you have your model trained its time to test it to see whether you are happy with its performance or not. The Live Classification tool as described in the next section is one such way to do quick testing but to do proper testing you need to run the model on the test data. Recall during the Data acquisition stage you split the training data...
Edge Impulse, the leading edge AI development platform, brings intelligence to any edge device. Our platform and in-house experts will accelerate your delivery of next generation products and solutions. Unlock sensor data. Reduce BOM cost, and time to market.
Jun 2, 2021 · Edge Impulse makes use of the TensorFlow ecosystem for training, optimizing, and deploying deep learning models to embedded devices.
