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:

Follow existing code style and patterns
Test on multiple devices and accelerators
Update documentation for any new features
Include performance metrics for optimizations