Image Classification TensorFlow Lite App

A simple Android app that uses TensorFlow Lite to classify images using the MobileNet v1 model. Built with Kotlin, MVVM architecture, Jetpack Compose, and Material 3.

Features

• Image Selection: Choose images from the device gallery
• Real-time Classification: Uses TensorFlow Lite MobileNet v1 model for image classification
• Modern UI: Built with Jetpack Compose and Material 3 design system
• MVVM Architecture: Clean separation of concerns with ViewModel and Repository pattern
• EXIF Support: Handles image rotation correctly
• Error Handling: Comprehensive error handling and retry functionality

Getting Started

Prerequisites

• Android Studio Arctic Fox or later
• Android SDK 23 (Android 6.0) or higher
• Kotlin 1.8+

Setup Instructions

1. Download the Model:

   • The current model file is a placeholder. To use the app, you need to download the actual MobileNet v1 model:
   • Visit TensorFlow Hub
   • Download the .tflite file
   • Replace the placeholder file at app/src/main/assets/models/mobilenet_v1_1.0_224.tflite

2. Build and Run:

   ./gradlew assembleDebug

3. Install on Device:

   adb install app/build/outputs/apk/debug/app-debug.apk

Project Structure

app/src/main/java/com/example/image/classification/tensorflowlite/
├── data/
│   ├── ClassificationResult.kt      # Data class for classification results
│   └── UiState.kt                   # UI state management
├── ml/
│   └── ImageClassifier.kt          # TensorFlow Lite model wrapper
├── repository/
│   └── ClassifierRepository.kt     # Data layer for image classification
├── ui/
│   └── MainScreen.kt               # Jetpack Compose UI
├── viewmodel/
│   └── MainViewModel.kt            # ViewModel for UI state management
└── MainActivity.kt                 # Main activity with Compose setup

Architecture

The app follows the MVVM (Model-View-ViewModel) architecture pattern:

• Model: TensorFlow Lite model and data classes
• View: Jetpack Compose UI components
• ViewModel: Manages UI state and business logic
• Repository: Handles data operations and ML model interactions

Key Components

ImageClassifier

• Loads the TensorFlow Lite model from assets
• Handles image preprocessing (resize to 224x224, normalization)
• Manages EXIF rotation data
• Performs inference and returns classification results

MainViewModel

• Manages UI state using StateFlow
• Handles image selection and classification workflow
• Provides error handling and retry functionality

MainScreen (Compose UI)

• Material 3 design system
• Image picker integration
• Real-time classification results display
• Loading states and error handling

How to Replace the Model

To use a different TensorFlow Lite model:

1. Replace Model File:

   • Place your .tflite model file in app/src/main/assets/models/
   • Update the filename in ImageClassifier.kt if different from mobilenet_v1_1.0_224.tflite

2. Update Labels (if needed):

   • Replace labels_mobilenet.txt with your model's labels
   • Ensure the number of labels matches your model's output classes

3. Adjust Input Size (if needed):

   • Modify the input size in ImageClassifier.preprocessImage() if your model expects different dimensions
   • Common sizes: 224x224, 299x299, 512x512

4. Update Classification Options:

   • Modify ImageClassifierOptions in ImageClassifier.initializeClassifier() if needed
   • Adjust setMaxResults() based on your requirements

Dependencies

• TensorFlow Lite: org.tensorflow:tensorflow-lite-task-vision:0.4.4
• Jetpack Compose: UI toolkit for modern Android development
• Material 3: Latest Material Design components
• Coil: Image loading library for Compose
• Lifecycle: ViewModel and LiveData/StateFlow support

Permissions

The app requires the following permissions:

• READ_EXTERNAL_STORAGE: For accessing images on Android 12 and below
• READ_MEDIA_IMAGES: For accessing images on Android 13 and above

Testing

The app has been designed to work on Android 6.0 (API 23) and above. Test on various devices to ensure compatibility.

Troubleshooting

1. Model Loading Issues: Ensure the .tflite file is not compressed (handled by aaptOptions.noCompress("tflite"))
2. Image Loading Issues: Check that the app has proper permissions for image access
3. Classification Errors: Verify that the model file is compatible with TensorFlow Lite Task Vision library

Future Enhancements

• Add support for camera capture
• Implement model quantization for smaller app size
• Add batch processing capabilities
• Implement custom model training integration
• Add confidence threshold filtering