Demo Team / Face detection - FOMO Public
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Demo Team / Face detection - FOMO

This is your Edge Impulse project. From here you acquire new training data, design impulses and train models.

Object detection

About this project

Face detection using FOMO

FOMO-Faces.png

Want to see a deeper explanation about FOMO and a live demo? See Arm AI Tech Talk about FOMO

Dataset Information

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)

Acquire data

Every Machine Learning project starts with data. You can capture data from a development board or your phone, or import data you already collected.

Let's collect some data

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.

Getting started: Continuous motion recognition
Getting started: Responding to your voice
Getting started: Adding sight to your sensors

Deploy

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.

Deploy your model

Download block output

Title Type Size
Image training data NPY file 314 windows
Image training labels JSON file 314 windows
Image testing data NPY file 99 windows
Image testing labels JSON file 99 windows
Object detection model TensorFlow Lite (float32) 83 KB
Object detection model TensorFlow Lite (int8 quantized) 56 KB
Object detection model TensorFlow SavedModel 188 KB
Object detection model Keras h5 model 90 KB

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Summary

Data collected
413 items

Project info

Project ID 87291
Project version 5
License No license attached