license: mit
library_name: coreml
pipeline_tag: image-segmentation
tags:
- coreml
- core-ml
- ios
- macos
- apple
- on-device
- anomaly-detection
- industrial-inspection
- mvtec-ad
- defect-detection
- arxiv:2303.14535
EfficientAD (MVTec bottle) — Core ML
WACV 2024
Visual anomaly detection for industrial inspection. Outputs a per-pixel anomaly heatmap plus a scalar score from a 256x256 image, so defects are localised rather than just flagged.
PDN-Small teacher/student pair plus an autoencoder: the anomaly map is the disagreement between teacher-student and autoencoder-student. This checkpoint is trained on the MVTec AD bottle category — retrain per category for other objects.
Core ML conversion of openvinotoolkit/anomalib 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 | openvinotoolkit/anomalib |
| Packages | 1 |
| Download size | 14 MB |
| Minimum iOS | 17.0 |
Files
| File | Size | Compute units | SHA-256 |
|---|---|---|---|
EfficientAD_Bottle.mlpackage.zip |
14 MB | all |
- |
| Total | 14 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 "efficientad/*" --local-dir ./efficientad
unzip './efficientad/efficientad/*.zip' -d ./efficientad
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 EfficientAD_Bottle(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/EfficientADDemo, 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
- Script:
convert_efficientad.py - 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: MIT.
Credits
- Upstream authors: openvinotoolkit/anomalib, 2023
- Core ML conversion: john-rocky (Daisuke Majima)