Kutluhan Aktar / IoT AI-driven Smart Grocery Cart Public

Kutluhan Aktar / IoT AI-driven Smart Grocery Cart

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

Object detection

About this project

This object detection (FOMO) model detects a small group of food retail products by utilizing the product brand names as labels:

  • Barilla
  • Milk
  • Nutella
  • Pringles
  • Snickers

After building my object detection (FOMO) model, I deployed my model as an OpenMV firmware and flashed OpenMV Cam H7 with the generated firmware so as to run inferences.

run_model_17.jpg

home_1.jpg

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 60 windows
Image training labels JSON file 60 windows
Image testing data NPY file 10 windows
Image testing labels JSON file 10 windows
Object detection model TensorFlow Lite (float32) 82 KB
Object detection model TensorFlow Lite (int8 quantized) 56 KB
Object detection model TensorFlow SavedModel 187 KB
Object detection model Keras h5 model 88 KB

Clone project

You are viewing a public Edge Impulse project. Clone this project to add data or make changes.

Summary

Data collected
70 items

Project info

Project ID 166688
Project version 1
License Apache 2.0