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Rousavy","email":"me@mrousavy.com","url":"https://github.com/mrousavy"},"license":"MIT","homepage":"https://github.com/mrousavy/react-native-fast-tflite#readme","keywords":["react-native","tensorflow","vision-camera","tflite","lite","tensorflowlite","tensorflow-lite","visioncamera","react-native-vision-camera","vision","camera","ios","android"],"repository":{"type":"git","url":"git+https://github.com/mrousavy/react-native-fast-tflite.git"},"description":"High-performance TensorFlow Lite library for React Native, built with Nitro Modules","maintainers":[{"name":"mrousavy","email":"marcrousavy@hotmail.com"}],"readme":"<a href=\"https://margelo.io\">\n  <picture>\n    <source media=\"(prefers-color-scheme: dark)\" srcset=\"./img/banner-dark.webp\" />\n    <source media=\"(prefers-color-scheme: light)\" srcset=\"./img/banner-light.webp\" />\n    <img alt=\"Fast TFLite\" src=\"./img/banner-light.webp\" />\n  </picture>\n</a>\n\nA high-performance [TensorFlow Lite](https://www.tensorflow.org/lite) library for React Native, built with [Nitro Modules](https://github.com/mrousavy/nitro).\n\n- ⚡ Powered by Nitro Modules\n- 💨 Zero-copy ArrayBuffers\n- 🔧 Uses the low-level C/C++ TensorFlow Lite core API for direct memory access\n- 🔄 Supports swapping out TensorFlow Models at runtime\n- 🖥️ Supports GPU-accelerated delegates (CoreML/Metal/OpenGL)\n- 📸 Easy [VisionCamera](https://github.com/mrousavy/react-native-vision-camera) integration\n\n## Migrating from v2\n\nIf you are upgrading from v2, see the [Migration Guide](./MIGRATION_V2_TO_V3.md) for breaking changes and upgrade steps.\n\n## Installation\n\n1. Add the npm packages:\n   ```sh\n   yarn add react-native-fast-tflite react-native-nitro-modules\n   ```\n2. In `metro.config.js`, add `tflite` as a supported asset extension:\n   ```js\n   module.exports = {\n     // ...\n     resolver: {\n       assetExts: ['tflite', // ...\n       // ...\n   ```\n   This allows you to drop `.tflite` files into your app and swap them out at runtime without rebuilding. 🔥\n3. (Optional) To enable GPU delegates, see [Using GPU Delegates](#using-gpu-delegates) below.\n4. Run your app (`yarn android` / `npx pod-install && yarn ios`)\n\n## Usage\n\n1. Find a TensorFlow Lite (`.tflite`) model. There are thousands of public models on [tfhub.dev](https://tfhub.dev).\n2. Drag your model into your app's asset folder (e.g. `src/assets/my-model.tflite`)\n3. Load the Model:\n\n   ```ts\n   // Option A: Standalone Function\n   const model = await loadTensorflowModel(require('assets/my-model.tflite'), [])\n\n   // Option B: Hook in a Function Component\n   const plugin = useTensorflowModel(require('assets/my-model.tflite'), [])\n   ```\n\n4. Call the Model:\n   ```ts\n   const inputData: ArrayBuffer = ...\n   const outputData = await model.run([inputData])\n   console.log(outputData)\n   ```\n\n### Loading Models\n\nModels can be loaded from the React Native bundle via `require(..)`, or any URI/URL (`http://..` or `file://..`):\n\n```ts\n// Asset from React Native Bundle\nloadTensorflowModel(require('assets/my-model.tflite'), [])\n// File on the local filesystem\nloadTensorflowModel({ url: 'file:///var/mobile/.../my-model.tflite' }, [])\n// Remote URL\nloadTensorflowModel(\n  { url: 'https://tfhub.dev/google/lite-model/object_detection_v1.tflite' },\n  []\n)\n```\n\nLoading a Model is asynchronous since buffers need to be allocated. Make sure to handle errors when loading.\n\n### Input and Output data\n\nTensorFlow uses _tensors_ as input and output. Since TensorFlow Lite is optimized for fixed-size byte buffers, you are responsible for interpreting the raw data yourself.\n\nInput and output values are passed as `ArrayBuffer`. To inspect tensor shapes, open your model in [Netron](https://netron.app).\n\nFor example, the `object_detection_mobile_object_localizer_v1_1_default_1.tflite` model on [tfhub.dev](https://tfhub.dev) has **1 input tensor** and **4 output tensors**:\n\n![Screenshot of netron.app inspecting the model](./img/netron-inspect-model.png)\n\nIn the description on [tfhub.dev](https://tfhub.dev) we can find the description of all tensors:\n\n![Screenshot of tfhub.dev inspecting the model](./img/tfhub-description.png)\n\nFrom that we know we need a 192 x 192 input image with 3 bytes per pixel (RGB).\n\n#### Usage (VisionCamera)\n\n> [!NOTE]\n> The example below targets **VisionCamera v4**. A VisionCamera v5 integration exists but is currently part of a private project — [sponsor Marc Rousavy](https://github.com/sponsors/mrousavy) to get access.\n\nIf you're using this model with a [VisionCamera](https://github.com/mrousavy/react-native-vision-camera) Frame Processor, you need to convert the Frame to the model's expected input size.\nUse [vision-camera-resize-plugin](https://github.com/mrousavy/vision-camera-resize-plugin) to do the conversion:\n\n```tsx\nimport { NitroModules } from 'react-native-nitro-modules'\n\nconst objectDetection = useTensorflowModel(require('object_detection.tflite'), [])\nconst model =\n  objectDetection.state === 'loaded' ? objectDetection.model : undefined\n\n// TfliteModel is a Nitro HybridObject (jsi::NativeState). VisionCamera v4's worklet\n// runtime cannot access jsi::NativeState directly — box it into a jsi::HostObject\n// before capture, then unbox() inside the worklet to run inference.\n// This will not be necessary in VisionCamera v5, which is itself a Nitro Module\n// and can access HybridObjects directly.\nconst boxedModel = useMemo(\n  () => (model != null ? NitroModules.box(model) : undefined),\n  [model]\n)\n\nconst { resize } = useResizePlugin()\n\nconst frameProcessor = useFrameProcessor(\n  (frame) => {\n    'worklet'\n    if (boxedModel == null) return\n\n    const tflite = boxedModel.unbox()\n\n    // 1. Resize 4k Frame to 192x192x3 using vision-camera-resize-plugin\n    const resized = resize(frame, {\n      scale: {\n        width: 192,\n        height: 192,\n      },\n      pixelFormat: 'rgb',\n      dataType: 'uint8',\n    })\n\n    // 2. Extract the exact slice of the underlying ArrayBuffer\n    //    (TypedArrays may have a non-zero byteOffset into a shared buffer)\n    const inputBuffer = resized.buffer.slice(\n      resized.byteOffset,\n      resized.byteOffset + resized.byteLength\n    )\n\n    // 3. Run model with given input buffer synchronously\n    const outputs = tflite.runSync([inputBuffer])\n\n    // 4. Interpret outputs accordingly\n    const detection_boxes = new Float32Array(outputs[0]!)\n    const detection_classes = new Float32Array(outputs[1]!)\n    const detection_scores = new Float32Array(outputs[2]!)\n    const num_detections = new Float32Array(outputs[3]!)\n    console.log(`Detected ${num_detections[0]} objects!`)\n  },\n  [boxedModel]\n)\n\nreturn <Camera frameProcessor={frameProcessor} {...otherProps} />\n```\n\n### Using GPU Delegates\n\nGPU Delegates offer faster, GPU-accelerated computation. There are multiple delegates available:\n\n#### CoreML (iOS)\n\n##### Expo\n\nUse the config plugin in your expo config (`app.json`, `app.config.json` or `app.config.js`):\n\n```json\n{\n  \"name\": \"my app\",\n  \"plugins\": [\n    [\n      \"react-native-fast-tflite\",\n      {\n        \"enableCoreMLDelegate\": true\n      }\n    ]\n  ]\n}\n```\n\n##### Bare React Native\n\n1. Set `$EnableCoreMLDelegate` to `true` in your `Podfile`:\n\n   ```ruby\n   $EnableCoreMLDelegate=true\n\n   # rest of your podfile...\n   ```\n\n2. Open your iOS project in Xcode and add the `CoreML` framework under **General → Frameworks, Libraries and Embedded Content**.\n3. Re-install Pods and build:\n   ```sh\n   cd ios && pod install && cd ..\n   yarn ios\n   ```\n4. Use the CoreML Delegate:\n   ```ts\n   const model = await loadTensorflowModel(\n     require('assets/my-model.tflite'),\n     ['core-ml']\n   )\n   ```\n\n> [!NOTE]\n> Not all model operations are supported on the CoreML delegate. Make sure your model is compatible.\n\n#### Android GPU/NNAPI (Android)\n\nTo enable GPU or NNAPI on Android, you **may** need to include native libraries, especially on Android 12+.\n\n##### Expo\n\nUse the config plugin in your expo config (`app.json`, `app.config.json` or `app.config.js`) with `enableAndroidGpuLibraries`:\n\n```json\n{\n  \"name\": \"my app\",\n  \"plugins\": [\n    [\n      \"react-native-fast-tflite\",\n      {\n        \"enableAndroidGpuLibraries\": true\n      }\n    ]\n  ]\n}\n```\n\nBy default, when enabled, `libOpenCL.so` will be included in your `AndroidManifest.xml`. You can also include more libraries by passing an array:\n\n```json\n{\n  \"name\": \"my app\",\n  \"plugins\": [\n    [\n      \"react-native-fast-tflite\",\n      {\n        \"enableAndroidGpuLibraries\": [\"libOpenCL-pixel.so\", \"libGLES_mali.so\"]\n      }\n    ]\n  ]\n}\n```\n\n> [!NOTE]\n> For Expo, remember to run prebuild if the library is not yet included in your `AndroidManifest.xml`.\n\n##### Bare React Native\n\nAdd any needed entries to your `AndroidManifest.xml`:\n\n```xml\n<uses-native-library android:name=\"libOpenCL.so\" android:required=\"false\" />\n<uses-native-library android:name=\"libOpenCL-pixel.so\" android:required=\"false\" />\n<uses-native-library android:name=\"libGLES_mali.so\" android:required=\"false\" />\n<uses-native-library android:name=\"libPVROCL.so\" android:required=\"false\" />\n```\n\nThen use the delegate:\n\n```ts\nconst model = await loadTensorflowModel(\n  require('assets/my-model.tflite'),\n  ['android-gpu']\n)\n// or\nconst model = await loadTensorflowModel(\n  require('assets/my-model.tflite'),\n  ['nnapi']\n)\n```\n\n> [!WARNING]\n> NNAPI is deprecated on Android 15. GPU delegate is preferred.\n\n> [!NOTE]\n> Android does not officially support OpenCL, but most GPU vendors do.\n\n## Community Discord\n\n[Join the Margelo Community Discord](https://discord.gg/6CSHz2qAvA) to chat about react-native-fast-tflite or other Margelo libraries.\n\n## Adopting at scale\n\n<a href=\"https://github.com/sponsors/mrousavy\">\n  <img align=\"right\" width=\"160\" alt=\"This library helped you? Consider sponsoring!\" src=\".github/funding-octocat.svg\">\n</a>\n\nThis library is provided _as is_, I work on it in my free time.\n\nIf you're integrating react-native-fast-tflite in a production app, consider [funding this project](https://github.com/sponsors/mrousavy) and <a href=\"mailto:me@mrousavy.com?subject=Adopting react-native-fast-tflite at scale\">contact me</a> to receive premium enterprise support, help with issues, prioritize bugfixes, request features, and more.\n\n## Contributing\n\n1. Clone the repo\n2. Make sure you have installed Xcode CLI tools such as `gcc`, `cmake` and `python`/`python3`. See the TensorFlow documentation on what you need exactly.\n3. Run `yarn bootstrap` and select `y` on all iOS and Android related questions.\n4. Open the example app and start developing\n   - iOS: `example/ios/TfliteExample.xcworkspace`\n   - Android: `example/android`\n\nSee the [contributing guide](CONTRIBUTING.md) to learn how to contribute to the repository and the development workflow.\n\n## License\n\nMIT\n","readmeFilename":"README.md"}