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README.md

LiteRT Image Segmentation Samples

This directory contains Android image segmentation samples demonstrating how to use LiteRT (Google's new runtime for TensorFlow Lite) with different hardware accelerators. The samples perform multi-class image segmentation to identify and classify objects in images.

Overview

Image segmentation is a computer vision task that involves partitioning an image into multiple segments or regions, where each pixel is assigned to a specific class. These samples demonstrate real-time segmentation on Android devices using optimized neural network models.

Available Implementations

1. kotlin_cpu_gpu

A standard implementation supporting CPU and GPU acceleration for broad device compatibility.

Features:

  • CPU and GPU delegate support

  • Real-time camera and gallery image segmentation

  • 21-class segmentation (person, car, bicycle, etc.)

  • Compatible with a wide range of Android devices

Performance on Samsung S25 ULtra:

  • CPU: 120-140ms per frame

  • GPU: 40-50ms per frame

2. kotlin_npu

An advanced implementation with Neural Processing Unit (NPU) support for significantly faster inference on compatible devices.

Features:

  • CPU, GPU, and NPU delegate support

  • Optimized for Qualcomm and MediaTek NPU hardware

  • Same 21-class segmentation as CPU/GPU version

Performance on Samsung S25 Ultra:

  • CPU: 120-140ms per frame

  • GPU: 40-50ms per frame

  • NPU: 6-12ms per frame (10-20x faster than CPU!)

Technical Details

Model Architecture

  • Input: 256x256x3 RGB image

  • Output: 256x256x6 segmentation mask

  • Model format: TensorFlow Lite (.tflite)

Key Dependencies

  • LiteRT (com.google.ai.edge.litert): 2.0.0-alpha

  • Android CameraX: For camera functionality

  • Jetpack Compose: For modern UI

  • Kotlin Coroutines: For asynchronous operations

Architecture Components

  • ImageSegmentationHelper: Core segmentation logic and model management

  • MainActivity: UI orchestration with camera/gallery tabs

  • MainViewModel: State management and data flow

  • TensorUtils: Utilities for tensor inspection and debugging

Contributing

When contributing to these samples:

  1. Follow existing code style and patterns

  2. Test on multiple devices and accelerators

  3. Update documentation for any new features

  4. Include performance metrics for optimizations