Kutluhan Aktar / Poultry Feeder and Unhatched Egg Tracker Public

Kutluhan Aktar / Poultry Feeder and Unhatched Egg Tracker

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

Object detection

About this project

Project Description

This object detection (FOMO) model tracks (counts) the unhatched eggs and detects the poultry feeder status in the coop by utilizing two labels:

  • Egg
  • Feeder

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.


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.


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 61 windows
Image training labels JSON file 61 windows
Image testing data NPY file 8 windows
Image testing labels JSON file 8 windows
Object detection model TensorFlow Lite (float32) 82 KB
Object detection model TensorFlow Lite (int8 quantized) 56 KB
Object detection model TensorFlow SavedModel 185 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.


Data collected
69 items

Project info

Project ID 134829
Project version 1
License No license attached