mlboydaisuke/YOLOE-S-CoreML

YOLOE-S — Core ML

Tsinghua, 2025

Open-vocabulary detection and instance segmentation. Type any text — "person", "forklift", "coffee cup" — and get boxes plus masks, with no fixed class list.

Unlike a baked-in text head, the detector emits a per-anchor region embedding before the class logits and the region-text similarity is computed on the client. The image branch never sees the text, so changing the query does not re-run the detector — only a cheap matmul against cached text embeddings.

Core ML conversion of THU-MIG/yoloe 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	zero shot object detection
Upstream	THU-MIG/yoloe
Packages	3
Download size	133 MB
Minimum iOS	17.0

Files

File	Size	Compute units	SHA-256
yoloe_detector_s.mlpackage.zip	18 MB	all	-
mobileclip_blt_text.mlpackage.zip	112 MB	all	-
reprta_s.mlpackage.zip	4 MB	all	-
Total	133 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 "yoloe/*" --local-dir ./yoloe
unzip './yoloe/yoloe/*.zip' -d ./yoloe

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 yoloe_detector_s(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)

This model is split into 3 Core ML packages that are driven in sequence from Swift. Load them one at a time, copy the outputs out of the MLMultiArray buffers and release each model before loading the next — two large Core ML models resident at once will OOM on an iPhone.

Demo

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

Conversion

• Pitfalls hit during conversion (FP16 overflow, ANE buffer limits, stride handling): docs/coreml_conversion_notes.md
• Model index: CoreML-Models

License

The conversion inherits the upstream license: AGPL-3.0.

Credits

• Upstream authors: THU-MIG/yoloe, 2025
• Core ML conversion: john-rocky (Daisuke Majima)