import RMBG2Swift

// Simple one-liner
let rmbg = try await RMBG2()
let result = try await rmbg.removeBackground(from: image)

// Access the result
let outputImage = result.image  // Image with transparent background
let mask = result.mask          // Grayscale segmentation mask

Swift Package: github.com/VincentGourbin/RMBG2Swift

Manual Integration

swift

import CoreML

// Load the model
let config = MLModelConfiguration()
config.computeUnits = .all  // Enable ANE

let model = try await MLModel.load(contentsOf: modelURL, configuration: config)

// Prepare input (1024x1024, NCHW format with ImageNet normalization)
let input = MLDictionaryFeatureProvider(dictionary: ["input": inputArray])

// Run inference
let output = try model.prediction(from: input)

// Get mask from output_3 (full resolution)
let mask = output.featureValue(for: "output_3")?.multiArrayValue

Model Details

Property	Value
Architecture	BiRefNet
Input Size	1024 x 1024
Input Format	RGB, NCHW [1, 3, 1024, 1024]
Normalization	ImageNet (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
Output	4 scales (output_0 to output_3), use output_3 for full resolution
Format	ML Program (.mlpackage)
Weight Quantization	INT8 (symmetric linear)
Model Size	~233 MB
Minimum OS	macOS 13+ / iOS 16+
Compute Units	All (CPU, GPU, ANE)

Performance

Tested on Apple Silicon:

Device	Compute Units	Inference Time
M1 Pro	.all (ANE)	~5s
M1 Pro	.cpuAndGPU	~3s

Note: ANE performance varies by device. The INT8 quantization improves memory efficiency and model size while maintaining quality.

Files

Two model versions are available:

File	Size	Description
`RMBG-2-native.mlpackage/`	461 MB	Original FP32 model
`RMBG-2-native-int8.mlpackage/`	233 MB	INT8 quantized model (recommended)

Recommended: Use the INT8 version for better memory efficiency and smaller download size with equivalent quality.

License

Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0)

Free for non-commercial use: Research, personal projects, education
Commercial use requires a separate license from BRIA AI

For commercial licensing, contact: bria.ai/contact-us

Attribution

This CoreML conversion is based on the RMBG-2.0 model by BRIA AI:

Original Model: briaai/RMBG-2.0
Architecture: BiRefNet
Creator: BRIA AI

sihai0506/rmbg2.0-coreml

RMBG-2 CoreML - Background Removal Model

Model Description

Key Features

Usage

With RMBG2Swift Package