---
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](https://github.com/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](https://github.com/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

```bash
hf download mlboydaisuke/coreml-zoo --include "efficientad/*" --local-dir ./efficientad
unzip './efficientad/efficientad/*.zip' -d ./efficientad
```

## Use in Swift

```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`](https://github.com/john-rocky/CoreML-Models/tree/master/sample_apps/EfficientADDemo), a standalone SwiftUI project.
- **Models Zoo** — this model is downloadable and runnable inside the [Models Zoo app](https://apps.apple.com/app/id6762083207) on the App Store, no build required.

## Conversion

- Script: [`convert_efficientad.py`](https://github.com/john-rocky/CoreML-Models/blob/master/conversion_scripts/convert_efficientad.py)
- Pitfalls hit during conversion (FP16 overflow, ANE buffer limits, stride handling): [`docs/coreml_conversion_notes.md`](https://github.com/john-rocky/CoreML-Models/blob/master/docs/coreml_conversion_notes.md)
- Model index: [CoreML-Models](https://github.com/john-rocky/CoreML-Models)

## License

The conversion inherits the upstream license: **MIT**.

## Credits

- Upstream authors: [openvinotoolkit/anomalib](https://github.com/openvinotoolkit/anomalib), 2023
- Core ML conversion: john-rocky (Daisuke Majima)