| --- |
| license: apache-2.0 |
| library_name: coreml |
| pipeline_tag: zero-shot-image-classification |
| base_model: google/siglip-base-patch16-224 |
| base_model_relation: quantized |
| tags: |
| - coreml |
| - core-ml |
| - ios |
| - macos |
| - apple |
| - on-device |
| - siglip |
| - clip |
| - zero-shot |
| - image-text |
| - arxiv:2303.15343 |
| --- |
| |
| # SigLIP — Core ML |
|
|
| *Zero-Shot Classification, 2023* |
|
|
| Zero-shot image classification. Dual encoder (image + text). 224×224 input. |
|
|
| Core ML conversion of [google-research/big_vision](https://github.com/google-research/big_vision) 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 image classification | |
| | Upstream | [google-research/big_vision](https://github.com/google-research/big_vision) | |
| | Packages | 2 | |
| | Download size | 358 MB | |
| | Minimum iOS | 17.0 | |
| | Peak RAM | ~800 MB | |
|
|
| ## Files |
|
|
| | File | Size | Compute units | SHA-256 | |
| |---|---:|---|---| |
| | `SigLIP_ImageEncoder.mlpackage.zip` | 162 MB | `cpuOnly` | `98f6abf5f4aa1451…` | |
| | `SigLIP_TextEncoder.mlpackage.zip` | 195 MB | `cpuOnly` | `9dead2d58705838a…` | |
| | `siglip_vocab.json` | 658 KB | `-` | `b94b3a58e04f6199…` | |
| | **Total** | **358 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 "siglip/*" --local-dir ./siglip |
| unzip './siglip/siglip/*.zip' -d ./siglip |
| ``` |
|
|
| ## Use in Swift |
|
|
| ```swift |
| import CoreML |
| |
| let config = MLModelConfiguration() |
| config.computeUnits = .cpuOnly // 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 SigLIP_ImageEncoder(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 2 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/SigLIPDemo`](https://github.com/john-rocky/CoreML-Models/tree/master/sample_apps/SigLIPDemo), 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_siglip.py`](https://github.com/john-rocky/CoreML-Models/blob/master/conversion_scripts/convert_siglip.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: **Apache-2.0**. |
|
|
| ## Credits |
|
|
| - Upstream authors: [google-research/big_vision](https://github.com/google-research/big_vision), 2023 |
| - Core ML conversion: john-rocky (Daisuke Majima) |
|
|
