YOLO11s — Core ML

Object Detection, 2024

YOLO11 small detection with Vision framework NMS. 640×640 input.

Core ML conversion of ultralytics/ultralytics 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	object detection
Upstream	ultralytics/ultralytics
Packages	1
Download size	17 MB
Minimum iOS	17.0
Peak RAM	~300 MB

Files

File	Size	Compute units	SHA-256
`yolo11s.mlpackage.zip`	17 MB	`all`	`79e82aacc3ad20fc…`
Total	17 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.

Download

hf download mlboydaisuke/coreml-zoo --include "yolov9/*" --local-dir ./yolo11s
unzip './yolo11s/yolov9/*.zip' -d ./yolo11s

Use in Swift

import CoreML

let config = MLModelConfiguration()
config.computeUnits = .all   // as converted — see the table above

// Unzip the .mlpackage, drop it into your Xcode target and Xcode compiles it
// at build time:
let model = try yolo11s(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)

Demo

Sample app — sample_apps/YOLOv9Demo, a standalone SwiftUI project.
Models Zoo — this model is downloadable and runnable inside the Models Zoo app on the App Store, no build required.