| --- |
| 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) |
|
|
