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
MLMultiArraybuffers 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)
More models in this format: Core ML Model Zoo — 46 models, each with the recipe that produced it.
Want a different model on-device? Open a request — free, open weights only; the export and its measured numbers get published publicly.