For beginners
Welcome to Edge Impulse! If you're new to the world of edge machine learning, you've come to the right place. This guide will walk you through the essential steps to get started with Edge Impulse, a suite of engineering tools for building, training, and deploying machine learning models on edge devices.
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:
- No Coding Required: You don't need to be a coding expert to use Edge Impulse. Our platform provides a user-friendly interface that guides you through the process - this includes many optimized preprocessing and learning blocks, various neural network architectures, and pre-trained models and can generate ready-to-flash binaries to test your models on real devices.
- Edge Computing: Your machine learning models are optimized to run directly on your edge devices, ensuring low latency and real-time processing.
- Support for Various Sensors: Edge Impulse supports a wide range of sensors, from accelerometers and microphones to cameras, making it versatile for different projects.
- Community and Resources: You're not alone on this journey. Edge Impulse offers a supportive community and extensive documentation to help you succeed.
Ready to begin? Follow these simple steps to embark on your Edge Impulse journey:
Start by creating an Edge Impulse account. It's free to get started, and you'll gain access to all the tools and resources you need.
Once you're logged in, create your first project. Give it a name that reflects your project's goal, whether it's recognizing sounds, detecting objects, or something entirely unique.
To teach your device, you need data. Edge Impulse provides user-friendly tools for collecting data from your sensors, such as recording audio, capturing images, or reading sensor values. We recommend using an officially supported hardware target or your smartphone to start collecting data when you begin with Edge Impulse. You can also import existing datasets or clone a public project to get familiar with the platform.
Organize your data by labeling it. For example, if you're working on sound recognition, label audio clips with descriptions like "dog barking" or "car horn." You can label your data as you collect it or add labels later, our data explorer is also particularly useful to understand your data.
This is where the magic happens. Edge Impulse offers an intuitive model training process through processing blocks and learning blocks. You don't need to write complex code; the platform guides you through feature extraction, model creation, and training.
After training your model, you can easily export your model to run in a web browser or on your smartphone, but you can also run it on a wide variety of edge devices, whether it's a Raspberry Pi, Arduino, or other compatible hardware. We also provide ready-to-flash binaries for all the officially supported hardware targets. You don't even need to write embedded code to test your model on real devices!
If you have a device that is not supported, no problem, you can export your model as a C++ library that runs on any embedded device. See Running your impulse locally for more information.
Building Edge AI solutions is an iterative process. Feel free to try our organization hub to automate your machine-learning pipelines, collaborate with your colleagues, and create custom blocks.
The end-to-end tutorials are perfect for learning how to use Edge Impulse Studio. Try the tutorials:
These will let you build machine-learning models that detect things in your home or office.
Remember, you're not alone on your journey. Join the Edge Impulse community to connect with other beginners, experts, and enthusiasts. Share your experiences, ask questions, and learn from others who are passionate about embedded machine learning.
Now that you have a roadmap, it's time to explore Edge Impulse and discover the exciting possibilities of embedded machine learning. Let's get started!
Last modified 27d ago