Demo Team / Face detection - FOMO
This is your Edge Impulse project. From here you acquire new training data, design impulses and train models.
About this project
Face detection using FOMO
Want to see a deeper explanation about FOMO and a live demo? See Arm AI Tech Talk about FOMO
This project is using a subset of the FFHQ dataset where the bounding boxes have been labeled manually by Edge Impulse team.
Sensor & Block Information
- Camera module with input images 96 x 96 pixels
- Image block to normalize the image data, and keep the color depth to RGB
- FOMO transfer learning block based on MobileNetV2 0.35
Interested in adding object detection capabilities to your constrained edge devices? Clone this project to build an object detection project to detect cans and bottles on the tiniest of devices!
Read more on our announcement blog to find out more about FOMO and to learn how to build your object detection project, from data collection to deployment on embedded devices.
Creating your first impulse (100% complete)
Every Machine Learning project starts with data. You can capture data from a development board or your phone, or import data you already collected.
Design an impulse
Teach the model to interpret previously unseen data, based on historical data. Use this to categorize new data, or to find anomalies in sensor readings.
Package the complete impulse up, from signal processing code to trained model, and deploy it on your device. This ensures that the impulse runs with low latency and without requiring a network connection.
Download block output
Data collected413 items
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