Create a Cat Finder App Using ML and Jetpack Compose

Name: Create a Cat Finder App Using ML and Jetpack Compose
Rating: 5.0 (1 reviews)

YoLo for Cats: Create AI and Android App with Compose

Created byYago Caetano

Last updated 4/2025

English

What you'll learn

Create and deploy YoLo based models using free tools
Integrating AI models in Android applications
Kotlin for Android application
File Providers & Asset Management

Course content

2 sections • 16 lectures • 2h 6m total length

Introduction to Machine Learning5:53
Explore how machines learn from data to make predictions without explicit programming. Discover supervised, unsupervised, and reinforcement learning, and the data collection, train, test, and deploy the model.
Introduction to Machine Learning Quiz
Computer Vision6:27
Explore computer vision, from pixels and feature extraction to training CNN and real-time object detection with YOLO, applying to a cat detector project and deployment.
Yolo and Google Colab3:24
Learn YOLO, a real-time object detector that uses a single network to predict bounding boxes with confidence and postprocessing. Explore Google Colab for GPU access and collaboration.
Testing YoLo Pre-trained models5:08
Use yolo in Google Colab to run detections with a pre-trained coco model (yolo v5), testing on sample images and observing bounding boxes, confidence scores, and class probabilities.
Training Cat Detector model13:27
Train a cat detector model in Google Colab using Ultralytics, YOLOv5, and Roboflow, including dataset setup and YAML config, with GPU acceleration. Export to tflite for Android app use.

Project creation and initial UI12:41
Create a cat detector app by initializing a new Android project, adding TensorFlow Lite dependencies, configuring permissions, and building a composable image picker.
Include an empty image icon in the app UI5:51
Add an empty image icon in the cat finder app UI when no image is selected, using a vector asset no_img, a painter resource, and a centered 200dp placeholder.
Creating Cat Detector Class10:25
Create a cat detector class in Kotlin within a YOLO package, with a detection listener and a BoundingBox data class, and set up the model with four-thread interpreter options.
Creating detector methods20:17
Implement a cat detector by building a detect function that processes frames, normalizes input, runs inference, and returns best bounding boxes with non-maximum suppression.
Understanding NMS and IOU3:19
Understand how IOU measures overlap between bounding boxes and how non-maximum suppression ranks detections by confidence to remove duplicates in YOLO, selecting the best cat.
Importing model into Cat Detector app3:47
Import the exported best_float32.tflite model into the cat detector app, create labels.txt with 'cat', and wire gDetector and DetectionListener (emptyDetect and onDetect) in main activity.
Intial test with app8:40
Perform an initial test of the cat detector app using two images, rotate the captured bitmap, feed it to the detector, and display toasts for cat or no cat results.
Creating cat detection list17:35
Detect cat images from a model and manage them in a Jetpack Compose list; crop by bounding boxes, display in cards, and rotate, share, or delete each image.
Sharing cat detection images4:23
Enable sharing for all images detected by the cat detector model by configuring a file provider in the Android manifest, including grant uri permissions and file_paths for external-files-path.
Final adjustments4:35
Apply final adjustments to the cat detector app in Android Studio, generate a signed release APK for Google Play, and centralize the UI while testing image detection of multiple cats.
Congratulations!0:30

Requirements

Basic knowledge of Object Oriented Programming

Description

Do you want to build cutting-edge AI applications and integrate them seamlessly into Android apps?

In this course, you’ll learn how to detect cats using the powerful YoLo (You Only Look Once) model and create a user-friendly Android application using Jetpack Compose.

Whether you’re an aspiring AI enthusiast or an Android developer looking to add AI-powered features to your apps, this course is for you.

What You’ll Learn:

Develop a Cat Detector Model: Train and deploy a custom YoLo model to detect cats effectively.
Integrate AI into Android: Use Jetpack Compose to create a visually appealing app that showcases the AI model in action.
Advanced Image Handling: Work with Bitmap manipulation, cropping, rotation, and sharing directly from your app.
File Providers & Asset Management: Handle resources like images and share them seamlessly from within your app.

Who Should Enroll:

AI enthusiasts curious about real-world applications of machine learning.
Android developers are eager to incorporate AI into their projects.
Students and professionals looking to explore YoLo and modern Android development practices.

Why Take This Course?

In this hands-on course, you’ll not only learn the theory but also apply it to build a fully functional cat-detection app. By the end, you’ll have the confidence to train models, handle image data, and develop robust Android apps with cutting-edge AI features.

Join now and start your journey to creating AI-driven Android apps today!

Who this course is for:

AI enthusiasts curious about real-world applications of machine learning.
Android developers are eager to incorporate AI into their projects.
Students and professionals looking to explore YoLo and modern Android development practices.

Create a Cat Finder App Using ML and Jetpack Compose

What you'll learn

Explore related topics

Course content

Introduction5 lectures • 34min

Creating Android App11 lectures • 1hr 32min

Requirements

Description

Who this course is for: