| --- |
| 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). |
|
|
