End-to-end object detection using EfficientDet on Raspberry Pi 3

End-to-end object detection using EfficientDet on Raspberry Pi 3

Computer vision deals with giving computers or machines a visual understanding of the environment. It’s a broad field that could be broken down into the following tasks (not an exhaustive list): Image Classification Object Detection (*this is what we’ll focus on here!) Image Segmentation (semantic and instance-based). Pose estimation Style Transfer Generative Adversarial Networks

Computer vision deals with giving computers or machines a visual understanding of the environment. It’s a broad field that could be broken down into the following tasks (not an exhaustive list):

  1. Image Classification
  2. Object Detection (*this is what we’ll focus on here!)
  3. Image Segmentation (semantic and instance-based).
  4. Pose estimation
  5. Style Transfer
  6. Generative Adversarial Networks

In this series of tutorials, I’ll be demonstrating how to use computer vision specifically for object detection, to identify and locate instances of both onions and peppers in images and video frames. We’ll do this with the help of TensorFlow’s object detection API. The full link to this work is available here on GitHub:

elishatofunmi/Computer-Vision

A custom object identification and classification of onions and peppers using EfficientDet Project Description This…

github.com

*Object detection *deals with real-time identification and classification of objects present in an image. Object detection essentially combines object localization (identification) and labeling (classification). This work is fully implemented on a raspberry pi3 because of its flexibility with machine learning architectures; essentially, it’s affordable and easy to work with as hardware.

Below is the table of contents for the entire series:

  1. Data Gathering (part 1)
  2. Data Labelling (part 1)
  3. Conversion to TensorFlow records (part 1)
  4. EfficientDet — Architecture overview (part 2)
  5. Setting up colab (part 2)
  6. Prepare TensorFlow 2 Object Detection Training data (part 2)
  7. Testing the model’s performance (part 2)
  8. Implementing custom object detectors on test images (part 2)
  9. Introduction and setting up of your raspberry pi 3 (part 3)
  10. Loading the models and implementation (part 3)
  11. Conclusion (part 3)
  12. References (part 3)

raspberry-pi heartbeat computer-vision deep-learning object-detection

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

How to Find Ulimit For user on Linux

Explains how to find ulimit values of currently running process or given user account under Linux using the 'ulimit -a' builtin command.

MEAN Stack Tutorial MongoDB ExpressJS AngularJS NodeJS

MEAN Stack Tutorial MongoDB ExpressJS AngularJS NodeJS - We are going to build a full stack Todo App using the MEAN (MongoDB, ExpressJS, AngularJS and NodeJS). This is the last part of three-post series tutorial.

Why you should learn Computer Vision and how you can get started

A few compelling reasons for you to starting learning Computer. In today’s world, Computer Vision technologies are everywhere.

Creating RESTful APIs with NodeJS and MongoDB Tutorial

Creating RESTful APIs with NodeJS and MongoDB Tutorial - Welcome to this tutorial about RESTful API using Node.js (Express.js) and MongoDB (mongoose)! We are going to learn how to install and use each component individually and then proceed to create a RESTful API.

systemctl List All Failed Units/Services on Linux

Explains how to use the systemctl command to list all failed units or services on Debian, Ubuntu, CentOS, Arch, Fedora, and other Linux distros.