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

Created by : Rated :4.3 719 views

Last Updated: 2023-03-20 05:03:58 English Course Type: Self Learning

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

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.

So what are you waiting for? Click the buy now button and join the world's best Object Detection course.

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)

# Object Detection Android

# Object Detection IOS

# Android Object Detection

# IOS Object detection

# object detection android

# object detection IOS

# android object detection

# IOS object detection

# Tensorflow lite object detection

# training object detection models for mobile

# yolo object detection

# YOLO object detection

# tensorflow object detection

Having some basic knowledge of Android App development will be a plus

Someone want to train custom Object Detection models and build mobile applications
Android Developers want to build smart Machine Learning based Android Applications
IOS Developers want to train custom Object Detection model for IOS applications( model integration for IOS is not included in this course)
Students who have basic knowledge of Android app development and want to build smart machine learning based Android Applications
Students who want to learn use of existing object detection models in Android (YOLO, EfficientDet, mobileNet)
Machine Learning Engineers want to use their existing object detection model in Android

Section 1 : Introduction 4 Lectures 00:11:21
Lecture 1 :
What is Object Detection Preview
Lecture 2 :
How an Object Detection Model is Trained
Lecture 3 :
What is there for IOS developers
Lecture 4 :
What is there for Machine Learning Engineers
Section 2 : Dataset Collection and Annotation 6 Lectures 00:38:45
Lecture 1 :
Dataset Collection Basics
Lecture 2 :
Collecting dataset for training Object Detection model
Lecture 3 :
Data Annotation Basics
Lecture 4 :
Tools for data annotation
Lecture 5 :
Annotating dataset for training object detection model
Lecture 6 :
Dataset version management and export formats
Section 3 : Training Custom Object Detection models 11 Lectures 00:46:25
Lecture 1 :
Testing object detection model on test dataset
Lecture 2 :
What is Tensorflow lite
Lecture 3 :
What is Google Colab!
Lecture 4 :
Uploading annotated data on Google drive
Lecture 5 :
Importing libraries and loading the dataset
Lecture 6 :
Training Object Detection Model
Lecture 7 :
Converting object detection model into Tensorflow lite(tflite) format
Lecture 8 :
Object Detection Model Evaluation Basics
Lecture 9 :
Introduction to the section
Lecture 10 :
Testing Object Detection model on images from internet
Lecture 11 :
Retraining other pretrained object detection models
Section 4 : App Development 1 Lectures 00:01:50
Lecture 1 :
Section Introduction
Section 5 : Java: Image Picker Section 4 Lectures 00:02:29
Lecture 1 :
Creating new Android Studio Project
Lecture 2 :
Capturing Images using Camera inside our Android Application
Lecture 3 :
Choosing Images from Gallery inside our Android Application
Lecture 4 :
Overview
Section 6 : Kotlin: Image Picker Section 4 Lectures 00:03:48
Lecture 1 :
Creating new Android Studio Project
Lecture 2 :
Capturing Images using Camera inside our Android Application
Lecture 3 :
Choosing Images from Gallery inside our Android Application
Lecture 4 :
Overview
Section 7 : Java: Object Detection with Images 10 Lectures 00:25:26
Lecture 1 :
Importing Starter Application Code
Lecture 2 :
Analyzing a Tensorflow Lite(.tflite) model
Lecture 3 :
Quantization
Lecture 4 :
Adding Object Detection model in Android Application
Lecture 5 :
Object Detection Module
Lecture 6 :
Performing Object Detection in Android
Lecture 7 :
Drawing Rectangles around detected objects on images
Lecture 8 :
Showing names of detected object with rectangle
Lecture 9 :
Handling Rotation of captured images in Android
Lecture 10 :
Overview
Section 8 : Kotlin: Object Detection with Images 10 Lectures 00:25:04
Lecture 1 :
Importing Starter Application Code
Lecture 2 :
Analyzing a Tensorflow Lite(.tflite) model
Lecture 3 :
Quantization
Lecture 4 :
Adding Object Detection model in Android Application
Lecture 5 :
Object Detection Module
Lecture 6 :
Performing Object Detection in Android
Lecture 7 :
Drawing Rectangles around detected objects on images
Lecture 8 :
Showing names of detected object with rectangle
Lecture 9 :
Handling Rotation of captured images in Android
Lecture 10 :
Overview
Section 9 : Java: Object Detection with Live Camera Footage / Real Time Object Detection 6 Lectures 00:17:09
Lecture 1 :
Setting up the Android Studio project
Lecture 2 :
Real Time Object Detection Application Demo
Lecture 3 :
Displaying live camera footage inside Android App
Lecture 4 :
Getting frames of live camera footage as bitmaps
Lecture 5 :
Performing object detection and drawing rectangles
Lecture 6 :
Overview
Section 10 : Kotlin: Object Detection with Live Camera Footage / Real Time Object Detection 6 Lectures 00:19:37
Lecture 1 :
Setting up the Android Studio project
Lecture 2 :
Real Time Object Detection Application Demo
Lecture 3 :
Displaying live camera footage inside Android App
Lecture 4 :
Getting frames of live camera footage as bitmaps
Lecture 5 :
Performing object detection and drawing rectangles
Lecture 6 :
Overview
Section 11 : Pretrained Object Detection Models 1 Lectures 00:01:45
Lecture 1 :
Types of Object Detection Models
Section 12 : Java: Using EfficientDet Models Family in Android 7 Lectures 00:27:29
Lecture 1 :
EfficientDet Models Introduction
Lecture 2 :
Using EfficientDet Models with Images in Android
Lecture 3 :
Images Application Code Explanation
Lecture 4 :
Using EfficientDet Models with Live Camera Footage
Lecture 5 :
Testing EfficientDet Lite0 model with live camera footage
Lecture 6 :
Testing EfficientDet Lite3 model with live camera footage
Lecture 7 :
Handling Efficient Det Models with live camera footage in Android
Section 13 : Kotlin: Using EfficientDet Models Family in Android 7 Lectures 00:22:55
Lecture 1 :
EfficientDet Models Introduction
Lecture 2 :
Using EfficientDet Models with Images in Android
Lecture 3 :
Images Application Code Explanation
Lecture 4 :
Using EfficientDet Models with Live Camera Footage
Lecture 5 :
Testing EfficientDet Lite0 model with live camera footage
Lecture 6 :
Testing EfficientDet Lite3 model with live camera footage
Lecture 7 :
Handling Efficient Det Models with live camera footage in Android
Section 14 : Java : Using SSD MobileNet Models in Android 10 Lectures 00:25:19
Lecture 1 :
SSD MobileNet Model Introduction
Lecture 2 :
Using SSD MobileNet V1 model with Images in Android
Lecture 3 :
Using SSD MobileNet V3 model with Images in Android
Lecture 4 :
Images Application Code Explanation
Lecture 5 :
How an object detection model works in Android (Classifier Class)
Lecture 6 :
Using SSD MobileNet V1 model with live camera footage in Android
Lecture 7 :
Testing SSD MobileNet V1 model with live camera footage
Lecture 8 :
Using SSD MobileNet V3 model with live camera footage in Android
Lecture 9 :
Testing SSD MobileNet V3 model with live camera footage
Lecture 10 :
Handling SSD MobileNet Models with live camera footage in Android
Section 15 : Kotlin : Using SSD MobileNet Models in Android 10 Lectures 00:17:34
Lecture 1 :
SSD MobileNet Model Introduction
Lecture 2 :
SSD MobileNet Model with images
Lecture 3 :
Images Application Code Explanation
Lecture 4 :
Using SSD MobileNet V3 model with Images in Android
Lecture 5 :
How an Object Detection Model Works (Classifier Class)
Lecture 6 :
Using SSD MobileNet V1 model with live camera footage in Android
Lecture 7 :
Testing SSD MobileNet V1 model with live camera footage
Lecture 8 :
Using SSD MobileNet V3 model with live camera footage in Android
Lecture 9 :
Testing SSD MobileNet V3 model with live camera footage
Lecture 10 :
Handling SSD MobileNet Models with live camera footage in Android
Section 16 : YOLO( Your Only Look Once) 10 Lectures 00:15:27
Lecture 1 :
YOLO Models Section Introduction
Lecture 2 :
Using YOLO V4 with live camera footage
Lecture 3 :
Handling YOLO V4 Models with live camera footage in Android
Lecture 4 :
Use your custom trained YOLO model with Live Camera Footage in Android
Lecture 5 :
Loading YOLO model in Android
Lecture 6 :
Passing Input Image to the model and getting output from model
Lecture 7 :
Non-maximum Suppression (NMS)
Lecture 8 :
Using YOLO V4 model with Images
Lecture 9 :
Using your custom YOLO V4 model with Images
Lecture 10 :
Images Application Code

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Hamza Asif,

7763 Course Views

3 Courses

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.