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Train Custom Object Detection Models for Android & IOS

person icon Hamza Asif

Train Custom Object Detection Models for Android & IOS

Integrate Object Detection models in Android like YOLO and train custom object detection models for Android and IOS

updated on icon Updated on Aug, 2023

language icon Language - English

person icon Hamza Asif

architecture icon IOS Development,Development,Android

Lectures -109

Resources -1

Duration -8.5 hours

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Course Description

If you want to train custom object detection models for Android and iOS then welcome to this course.

In this course, you will learn to

  • Train your custom object detection models for Android and IOS

  • Use those models in Android (Java/Kotlin) with images and live camera footage

  • Use existing object detection models like YOLO, EfficientDet, and MobileNet models in Android (Java/Kotlin)

The android app development section of this course is for both java and kotlin programming languages. 

So after completing this course you will be able to

  • Collect datasets for training object detection models

  • Annotate datasets using different tools

  • Train object detection models on custom datasets for Android and IOS ( TensorFlow object detection )

  • Convert object detection models into tflite / Tensorflow lite format

  • Use those converted models in Android (Java/Kotlin) with images and live camera footage

  • Use existing object detection models in Android (Java/Kotlin) like YOLOv4, SSD EfficientDet Models, and SSD MobileNet Models

Ready to use Resources

The course comes with ready-to-use codes which means if you have a trained object detection model then

  • You can take complete android (Java/Kotlin) application codes from course resources

  • Replace the object detection model with your custom model 

  • And use it for your custom use case

and if you want to use existing object detection models in Android for your custom use cases then 

  • you can take complete android (Java/Kotlin) application codes from course resources

  • and customize it as per your needs

What is there for IOS developers(Object Detection IOS) 

So apart from Android, If you want to train custom object detection models for IOS applications then you can also take this course but the integration of object detection models in IOS applications is not included in this course

Object Detection

Object detection is a computer vision technique that allows us to identify and locate objects in an image or video.

Use Cases & Applications

  • Video surveillance

  • Crowd counting

  • Anomaly detection (i.e. in industries like agriculture, and health care)

  • Self-driving cars

Course Curriculum

The course is divided into several sections

Data collection and Annotation

In this section, we will cover the basics of dataset collection and annotation and then

  • We will learn to collect the dataset for training an object detection model

  • After that, we will learn to annotate that dataset using Roboflow and other such tools

Training Object Detection Model / Tensorflow Object Detection

  • We will learn to train an object detection model using the dataset we collected and annotated.

Testing and Conversion

  • After training the model we will test it to check model performance and accuracy

  • Then we will convert it into tflite / Tensorflow lite format so that we can use it in mobile applications.

Android App Development (Object Detection Android)

After model training and conversion we will learn to use that model inside Android applications (Java/Kotlin) with both

  • Images

  • Live camera footage / Real-Time Object Detection 

Object Detection with Images (Object Detection Android)

So firstly we will build an Android (Java/Kotlin) application where 

  • users can choose images from the gallery or capture images using the camera

  • and then those images will be passed to our custom object detection model

  • and then based on the results returned by the model we will draw rectangles around detected objects.

Object Detection with live camera footage (Object Detection Android)

Secondly, we will build an Android (Java/Kotlin) application in which

  • we will display the live camera footage using camera 2 API

  • and then we will pass frames of live camera footage to our object detection model

  • and draw rectangles around the detected objects in real-time

Existing Object Detection Models (Object Detection Android)

We will learn to use existing object detection models inside Android (Java/Kotlin) Applications with both images and live camera footage.

So in that section, we explore three popular families of object detection models and use them inside Android (Java/Kotlin) Applications.

  • SSD MobileNet Models

  • Efficient Det Models

  • YOLO Models

SSD MobileNet Models

In this section, we will learn to use SSD MobileNet Models in Android (Java/Kotlin) with both images and live camera footage. 

Firstly we will learn about the structure of MobileNet models and then we will use two popular MobileNet models in Android (Java/Kotlin) which are

  • SSD MobileNet V1

  • SSD MobileNet v3

Efficient Det Models

In this section, we will learn to use EfficientDet Models in Android (Java/Kotlin) with both images and live camera footage. 

Firstly we will learn about the structure of EfficientDet models and then we will use two popular EfficientDet models in Android (Java/Kotlin) which are

  • EfficientDet Lite0

  • EfficientDet Lite1

  • EfficientDet Lite2

  • EfficientDet Lite3

YOLO Models / YOLO object detection

In this section

  • we will learn to use the latest YOLOV4 model in Android (Java/Kotlin) with both images and live camera footage

  • We will also cover the YOLO model structure and how input and outputs are handled in YOLO effectively

  • We will handle the integration of both the regular YOLOV4 model and the tiny YOLOv4 model in Android with both images and live camera footage. 

So a complete YOLO object detection package for android.

Sign up today, and look forwards to:

  • HD 1080p video content.

  • Training custom object detection models

  • Building fully-fledged Android (Java/Kotlin) applications using different object detection models.

  • All the knowledge you need to start building Object Detection-based Android (Java/Kotlin) application you want

  • $1000+ Source codes of Android (Java/Kotlin) Applications.

Who this course is for:

  • Anyone who wants to train object detection models for Android (Java/Kotlin)

  • Anyone who wants to use object detection models in Android (Java/Kotlin) with images and live camera footage

  • Beginner Android developer with very little knowledge of mobile app development in Android (Java/Kotlin)

  • An Intermediate Android developer wanted to build a powerful Machine Learning-based application for Android (Java/Kotlin)

  • Experienced Android (Java/Kotlin) developers wanted to use Machine Learning models inside their applications.

  • Machine Learning experts want to use their object detection models in Android (Java/Kotlin)

Goals

What will you learn in this course:

  • Train object detection models on custom datasets for Android and IOS
  • Test and optimize trained object detection model
  • Use object detection models with images in Android
  • Use object detection models with live camera footage in Android
  • Collect and annotate datasets for training object detection models
  • Use YOLO models in Android with images and live camera footage
  • Use SSD Mobilenet models in Android with images and live camera footage
  • Use Efficient Det models in Android with images and live camera footage
  • Convert object detection model into tflite formats
  • Learn about object detection and it's applications
  • Learn about tflite (TensorFlow lite) models integration in Android

Prerequisites

What are the prerequisites for this course?

  • Having some basic knowledge of Android App development will be a plus
Train Custom Object Detection Models for Android & IOS

Curriculum

Check out the detailed breakdown of what’s inside the course

Introduction
5 Lectures
  • play icon Course Introduction 04:01 04:01
  • play icon What is Object Detection 04:28 04:28
  • play icon How an Object Detection Model is Trained 03:38 03:38
  • play icon What is there for IOS developers 01:07 01:07
  • play icon What is there for Machine Learning Engineers 02:08 02:08
Dataset Collection and Annotation
7 Lectures
Tutorialspoint
Training Custom Object Detection models
10 Lectures
Tutorialspoint
Java: Image Picker Section
5 Lectures
Tutorialspoint
Kotlin: Image Picker Section
5 Lectures
Tutorialspoint
Java: Object Detection with Images
10 Lectures
Tutorialspoint
Kotlin: Object Detection with Images
10 Lectures
Tutorialspoint
Java: Object Detection with Live Camera Footage / Real Time Object Detection
6 Lectures
Tutorialspoint
Kotlin: Object Detection with Live Camera Footage / Real Time Object Detection
6 Lectures
Tutorialspoint
Pretrained Object Detection Models
1 Lectures
Tutorialspoint
Java: Using EfficientDet Models Family in Android
7 Lectures
Tutorialspoint
Kotlin: Using EfficientDet Models Family in Android
7 Lectures
Tutorialspoint
Java: Using SSD MobileNet Models in Android
10 Lectures
Tutorialspoint
Kotlin: Using SSD MobileNet Models in Android
10 Lectures
Tutorialspoint
YOLO (You Only Look Once)
10 Lectures
Tutorialspoint

Instructor Details

Hamza Asif

Hamza Asif

Experienced Mobile Developer, specialized in Mobile Machine Learning using Tensorflow lite, ML Kit, and Google cloud vision API. Leading Android Machine learning instructor with over 50,000 students from 150 countries.

I am an enthusiastic developer with a strong programming background and possess great app development skills. I have developed a bunch of native and cross-platform apps in the past and satisfied all of my clients. It has been +4 years doing Mobile development and providing support for Android Applications. Empowering mobile Applications using Machine Learning and Computer vision is my core skill.

Powering Android Application with ML really fascinates me. So I learned Android development and then Machine Learning. I developed Android applications for several multinational organizations. Now I want to spread the knowledge I have. I'm always thinking about how to make difficult concepts easy to understand, what kind of projects would make a fun tutorial, and how I can help you succeed through my courses.

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