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  • 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 YOLOv4SSD 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
View More...
  • Section 1 : Introduction 4 Lectures 00:11:21

    • Lecture 1 :
    • 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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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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