| --- |
| license: apache-2.0 |
| pipeline_tag: image-classification |
| library_name: coreai |
| tags: |
| - core-ai |
| - ios |
| - imagenet |
| --- |
| |
| # PVT v2 b0 for Core AI |
|
|
| PVT v2 b0 (3.7M parameters, ImageNet-1k) from [timm](https://huggingface.co/docs/timm), exported to Apple Core AI with the [`apple/coreai-models`](https://github.com/apple/coreai-models/tree/main/models/pvt) recipe: |
|
|
| ```sh |
| uv run models/pvt/export.py --dtype float16 |
| ``` |
|
|
| | Folder | Contents | Size | Platforms | |
| | ------------ | ----------------------------------------------------------------------------------------- | ------ | ---------- | |
| | `pvt-v2-b0/` | `pvt-v2-b0.aimodel`: `main(x) -> logits`, `x` is `[1, 3, 224, 224]` float16 RGB normalized with the ImageNet mean and std. `labels.json`: the 1000 ImageNet class names, by logit index | 7.1 MB | iOS, macOS | |
|
|
| ## Use |
|
|
| With [`@react-native-ai/core-ai`](https://github.com/callstackincubator/ai/tree/main/packages/core-ai), download `pvt-v2-b0/` into app storage, then: |
|
|
| ```ts |
| const { output } = await CoreAI.runTask( |
| 'classification', |
| { id: 'pvt-v2-b0', sourceType: 'file', sourceUri: pathToFolder }, |
| { imageUri }, |
| { topK: 5 } |
| ) |
| // output.classifications: [{ label: 'tabby, tabby cat', index: 281, confidence: 0.36 }, ...] |
| ``` |
|
|
| The Neural Engine compiler rejects this export on macOS 27.0, so load it on the GPU there (`computeUnits: 'gpu'`). |
|
|
| ## License |
|
|
| PVT v2 and its timm weights are Apache-2.0 ([paper](https://arxiv.org/abs/2106.13797)). The labels come from timm's ImageNet metadata. This repository only converts the model to Core AI format. |
|
|
