Louis Moreau / Face detection - FOMO - Embedded Online Conference

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

This project is using a subset of the FFHQ dataset.

The model has been trained on 96x96 greyscale images

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.

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.

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.

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 88 windows
Image testing labels JSON file 88 windows
Object detection model (version #1) TensorFlow Lite (float32) 82 KB
Object detection model (version #1) TensorFlow Lite (int8 quantized) 56 KB
Object detection model (version #1) TensorFlow SavedModel 187 KB
Object detection model (version #1) Keras h5 model 88 KB
Object detection model (version #2) TensorFlow Lite (float32) 82 KB
Object detection model (version #2) TensorFlow Lite (int8 quantized) 56 KB
Object detection model (version #2) TensorFlow SavedModel 187 KB
Object detection model (version #2) Keras h5 model 88 KB
Object detection model (version #3) TensorFlow Lite (float32) 47 KB
Object detection model (version #3) TensorFlow Lite (int8 quantized) 40 KB
Object detection model (version #3) TensorFlow SavedModel 149 KB
Object detection model (version #3) Keras h5 model 51 KB

Summary

Data collected
406 items

Project info

Project ID 97847
Project version 1