---
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)