What you would learn in Train Custom Object Detection Models for Android & IOS course?
If you want to build your object-based models specifically for Android or iOS, you should take this class.
This course will teach you how to use the internet. you'll be taught to
Create your custom model of object recognition to work with Android and IOS
Utilize these models with Android (Java/Kotlin) that include photos as well as live video footage
Utilize existing objects detection models such as YOLO, EfficientDet, and MobileNet models to create models in Android (Java/Kotlin)
This course's Android app development portion covers both Java and Kotlinprogramming languages.
After the completion of this course, you'll be in a position to
Collect datasets for training object detection models
Create annotations on datasets using various tools
Create object detection models for training on customized datasets designed to be used on Android and IOS
Convert models for object detection into Tflite (Tensorflow Light) format
Make use of the models that have been converted to run Android (Java/Kotlin) that include photos along with living footage from the camera
Make use of existing object detection models available in Android (Java/Kotlin) such as YOLO Version 4, SSD EfficientDet Models as well SSD MobileNet models
Ready to make use of Resources
The course comes with ready-to-use codes. If you already have a well-trained model for object detection, you can use it.
The course materials allow you to download the complete Android (Java/Kotlin) application codes.
Replace the model for object detection with your model
Use it to create your personal use case
If you'd like to utilize the existing object detection models available in Android to create your custom scenarios, then
you can get complete Android (Java/Kotlin) applications codes via the course sources
and personalize it to your requirements
What's the need for IOS designers?
Apart from Android, If you're looking to develop object detection model designs that you have created to be used in IOS applications, you could also enroll in this course; however, the integration of models for object detection within IOS applications is not covered in this course.
A computer vision method lets us identify and locate objects within videos or images.
Use Cases and Applications
Detection of abnormalities (i.e., in fields like agriculture or health care)
The course is broken down into sections.
The collection of information and annotation
Within this part, we'll go over the basics of data annotation and collection, and finally.
We will be able to gather the data needed to train an object detection model.
Following that, we'll learn how to annotate that dataset with Roboflow and other similar tools.
Training Object Detection Model
We will build an object recognition model with the data we took and annotated.
Testing, Conversion, and Test
After the model has been trained, we will test it to test the accuracy and performance of the model.
Then, we'll transform it into the tflite (Tensorflow light) format to be able to utilize it in mobile applications.
Android app Development
After training the model and conversion, we can utilize the model in Android software (Java/Kotlin) using both.
Live camera footage
Object Detection using Images
In the beginning, we will create the Android (Java/Kotlin) app in which
Users can select pictures from the gallery or take pictures with the camera
Then, those images will be sent to our custom-designed object detection model.
And then, based on the model's data, draw rectangles around the objects we detect.
Object Detection using Live camera video footage
Second, we will create an Android (Java/Kotlin) application which
We will show the live footage from the camera using the camera 2 API
We will then send frames from live footage from our cameras to our model of object detection
And draw rectangles on identified objects in real-time
Existing Object Detection Models
We will discover how to use existing models for object detection in Android (Java/Kotlin) Apps that incorporate both life and still footage from cameras.
In that section, we look at three well-known classes of object detection models and how to use them in Android (Java/Kotlin) Apps.
SSD MobileNet Models
Efficient Det Models
SSD MobileNet Models
This section will teach us how to use SSD MobileNet Models in Android (Java/Kotlin) using photos and live camera footage.
In the beginning, we will introduce the basic structure behind MobileNet models. Next, we will employ two of the most popular MobileNet models that are available in Android (Java/Kotlin), which are
SSD MobileNet V1
SSD MobileNet v3
Efficient Det Models
This section will teach us how to use EfficientDet Models on Android (Java/Kotlin) using photos and live camera footage.
We will first introduce the basic structure behind the EfficientDet models. We will employ two of the most popular EfficientDet models for Android (Java/Kotlin), which are
In this part, we will learn how to utilize the most recent YOLO V4 model in Android (Java/Kotlin) that includes both live and image footage. We will also discuss the YOLO model's structure and how inputs and outputs work by YOLO efficiently. In addition, we will cover the integration of the standard YOLO V4 model and the small YOLO V4 model on Android using both images and live video footage.
- Create object detection models for training on customized datasets designed available for Android and IOS
- Examine and optimize the trained object detection model
- Make use of object detection models using images in Android
- Make use of object detection models using live camera footage on Android
- Datasets should be collected and annotated to train object detection models.
- Utilize YOLO models on Android with live camera footage
- Make use of SSD Mobilenet models in Android with live camera and images footage
- Use Efficient Det models for Android with live camera footage
- Convert object detection model into tflite formats
- Learn more about object detection and the applications it has.
- Find out more about the tflite (TensorFlow Light) models and their integration into Android.
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