This Android application demonstrates image classification using TensorFlow Lite. Users can select an image from the gallery, preprocess it, run inference using a TensorFlow Lite model, and display the predicted label along with probabilities.

The project utilizes TensorFlow Lite to perform image classification tasks on Android devices. It demonstrates how to integrate a pre-trained model into an Android app, handle image preprocessing, and display classification results to the user.

• Select an image from the gallery for classification.

• Preprocess the image to match the input requirements of the TensorFlow Lite model.

• Run inference using a TensorFlow Lite model stored locally in the assets folder.

• Display the predicted label and probabilities of the image classification.

• Android Studio (with Android SDK installed)
• Android device or emulator running API level 21 (Android 5.0) or higher
• Basic understanding of Android development and TensorFlow Lite

1. Clone Repository: Clone this repository to your local machine using Git:

   git clone https://github.com/0oAVIRALo0/object-prediction-v1.git

2. Open Project in Android Studio: Open the cloned project in Android Studio.

3. Build Project: Build the project to ensure that all dependencies are resolved.

1. Set Up TensorFlow Lite Model:
   • Place your TensorFlow Lite model file (classify.tflite) in the app/src/main/assets directory.
   • Create a labels.txt file containing the class labels corresponding to your model's output.
2. Run the App:
   • Connect your Android device or start an emulator.
   • Click on the "Run" button in Android Studio to install and launch the app on the device/emulator.
3. Select an Image:
   • Click the "Select Image" button to choose an image from the gallery.
4. View Results:
   • Once the image is selected and processed, the predicted label and probabilities will be displayed on the screen.

• MainActivity.kt: Contains the main activity code for image selection, preprocessing, inference, and result display.
• activity_main.xml: Layout file defining the UI components for the main activity.
• classify.tflite: TensorFlow Lite model file (to be placed in the assets directory).
• labels.txt: Text file containing class labels for the model's output.

• Glide: For loading and displaying images efficiently.
• TensorFlow Lite: For running inference with a pre-trained model.
• OpenCV (via org.opencv.android): For image preprocessing (if applicable).

• Model Loading Error: If the app reports a model loading error, ensure that the classify.tflite model file exists in the assets directory and is correctly named.
• Image Processing Issues: Check the logcat output for any errors related to image processing, inference, or model output.

• TensorFlow Lite Documentation
• Glide Documentation
• Android Developers Guide