Core ML Model Zoo
Collection
PyTorch models converted to Core ML for on-device inference on iPhone, iPad and Mac. β’ 46 items β’ Updated β’ 1
BRIA AI, 2023
High-quality background removal. Outputs foreground with alpha mask. 1024Γ1024 input.

Core ML conversion of briaai/RMBG-1.4 for on-device inference on iPhone, iPad and Mac. Converted with coremltools; the packages are stateless, so all sequencing and buffering lives in your Swift code.
| Task | image segmentation |
| Upstream | briaai/RMBG-1.4 |
| Packages | 1 |
| Download size | 37 MB |
| Minimum iOS | 17.0 |
| Peak RAM | ~300 MB |
| File | Size | Compute units | SHA-256 |
|---|---|---|---|
RMBG_1_4.mlpackage.zip |
37 MB | cpuOnly |
a80dbb5f04c922a8β¦ |
| Total | 37 MB |
compute_units is not a suggestion -- it is the configuration the conversion was verified against. Moving a package to a different compute unit can silently change the numerics (FP16 attention overflow) or crash on the GPU.
hf download mlboydaisuke/coreml-zoo --include "rmbg/*" --local-dir ./rmbg_1_4
unzip './rmbg_1_4/rmbg/*.zip' -d ./rmbg_1_4
import CoreML
let config = MLModelConfiguration()
config.computeUnits = .cpuOnly // as converted β see the table above
// Unzip the .mlpackage, drop it into your Xcode target and Xcode compiles it
// at build time:
let model = try RMBG_1_4(configuration: config)
// ...or compile a downloaded .mlpackage at runtime:
let compiled = try await MLModel.compileModel(at: mlpackageURL)
let model = try MLModel(contentsOf: compiled, configuration: config)
sample_apps/RMBGDemo, a standalone SwiftUI project.convert_rmbg.pydocs/coreml_conversion_notes.mdThe conversion inherits the upstream license: Bria RMBG-1.4 License. See https://huggingface.co/briaai/RMBG-1.4.
Non-commercial without a separate agreement with Bria.
Base model
briaai/RMBG-1.4