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license: apache-2.0
base_model: yunyangx/EfficientSAM
pipeline_tag: mask-generation
library_name: coreai
tags:
- coreai
- ios
- image-segmentation
- react-native
---
# EfficientSAM-Ti for Core AI
[EfficientSAM](https://github.com/yformer/EfficientSAM) ViT-Tiny (10M parameters) exported to Apple Core AI, unmodified, with the [`apple/coreai-models`](https://github.com/apple/coreai-models) recipe. It runs on iOS 27 and macOS 27 with the `ImageSegmenter` from the `CoreAISegmentation` product of `coreai-models`.
It segments the object at a point (a click) or in a box, one mask per prompt.
## Files
`efficient-sam/` is a segmenter bundle: `metadata.json` and the exported model, float32, static shape, up to two points per prompt (39 MB).
## Use
With [`@react-native-ai/core-ai`](https://github.com/callstackincubator/ai), download the folder and pass its path:
```typescript
import { CoreAI } from '@react-native-ai/core-ai'
const result = await CoreAI.runTask(
'segmentation',
{ id: 'efficient-sam', sourceType: 'file', sourceUri: efficientSAMBundlePath },
{ imageUri, data: { box: { x: 10, y: 50, width: 300, height: 400 } } },
{}
)
```
In Swift:
```swift
let segmenter = try await ImageSegmenter(resourcesAt: "efficient-sam")
let response = try await segmenter.segment(image: cgImage, pointQuery: query)
```
## Export
From [`apple/coreai-models`](https://github.com/apple/coreai-models) at `09ff139`, with `coreai-core` 1.0.0b3:
```bash
cd models/efficient-sam
uv run export.py
```
## License
Apache-2.0, as the original model. EfficientSAM is by Y. Xiong et al., [EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything](https://arxiv.org/abs/2312.00863).
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