README.md updated

Author: tharushaudana

README.md

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+# YOLO11s Android App
+
+This is an Android application (Java) that runs the **YOLO11s** object detection model on mobile devices using **TensorFlow Lite (TFLite)**. The app allows object detection on captured frames using the YOLO11s model optimized for edge devices.
+
+## Features
+- Runs **YOLO11s** model on Android devices using **TFLite**.
+- Live camera feed with manual detection.
+- Captures the current frame when the **Detect** button is clicked.
+- Displays detected objects with bounding boxes, class names, and confidence scores.
+
+## Model Conversion
+To convert a YOLO model into the **TFLite** format, refer to the **YOLO11S_Convert_to_TFLite.ipynb** notebook included in this repository.
+
+## Pretrained Model
+This repository includes a YOLO11s model pretrained for playing card symbol detection. However, you can use your own trained model by converting it to TFLite format and replacing the provided model.
+
+## Screenshots
+### Detection Output
+![Detection Screenshot](screenshots/screenshot1.jpg)
+
+### Tensor Shape Output
+The output tensor shape and its details can be found in the following GitHub discussion:
+[Ultralytics Discussion #17254](https://github.com/orgs/ultralytics/discussions/17254)
+
+![Tensor Output](screenshots/output_shape.png)
+
+## Requirements
+- Android 7.0 (API level 24) or higher
+- TensorFlow Lite dependencies added to `build.gradle`
+- A device with a camera for capturing frames
+
+## Setup & Installation
+1. Clone this repository:
+   ```sh
+   git clone https://github.com/tharushaudana/YOLO11S-TFLite-Android-Java.git
+   ```
+2. Open the project in **Android Studio**.
+3. Ensure that `TensorFlow Lite` dependencies are added in `build.gradle`:
+   ```gradle
+   implementation("org.tensorflow:tensorflow-lite:2.9.0")
+   implementation("org.tensorflow:tensorflow-lite-task-vision:0.3.1")
+   implementation("org.tensorflow:tensorflow-lite-gpu:2.9.0")
+   ```
+4. Place the **TFLite** model and `classes.txt` inside the `assets` folder.
+5. Run the app on an Android device/emulator.
+
+## Usage
+1. Launch the app.
+2. Grant camera permissions.
+3. View the live camera feed.
+4. Click the **Detect** button to capture the current frame.
+5. The detected objects will be displayed with bounding boxes, class names, and confidence scores.
+
+## Acknowledgments
+- [TensorFlow Lite](https://www.tensorflow.org/lite)
+- [Ultralytics](https://github.com/ultralytics)