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Version: 0.8.x

Class: ClassificationModule<T>

Defined in: modules/computer_vision/ClassificationModule.ts:48

Generic classification module with type-safe label maps.

Extends​

  • VisionLabeledModule<Record<keyof ResolveLabels<T>, number>, ResolveLabels<T>>

Type Parameters​

T​

T extends ClassificationModelName | LabelEnum

Either a built-in model name (e.g. 'efficientnet-v2-s') or a custom LabelEnum label map.

Properties​

generateFromFrame()​

generateFromFrame: (frameData, ...args) => any

Defined in: modules/BaseModule.ts:53

Process a camera frame directly for real-time inference.

This method is bound to a native JSI function after calling load(), making it worklet-compatible and safe to call from VisionCamera's frame processor thread.

Performance characteristics:

  • Zero-copy path: When using frame.getNativeBuffer() from VisionCamera v5, frame data is accessed directly without copying (fastest, recommended).
  • Copy path: When using frame.toArrayBuffer(), pixel data is copied from native to JS, then accessed from native code (slower, fallback).

Usage with VisionCamera:

const frameOutput = useFrameOutput({
  pixelFormat: 'rgb',
  onFrame(frame) {
    'worklet';
    // Zero-copy approach (recommended)
    const nativeBuffer = frame.getNativeBuffer();
    const result = model.generateFromFrame(
      { nativeBuffer: nativeBuffer.pointer, width: frame.width, height: frame.height },
      ...args
    );
    nativeBuffer.release();
    frame.dispose();
  }
});

Parameters​

frameData​

Frame

Frame data object with either nativeBuffer (zero-copy) or data (ArrayBuffer)

args​

...any[]

Additional model-specific arguments (e.g., threshold, options)

Returns​

any

Model-specific output (e.g., detections, classifications, embeddings)

See​

Frame for frame data format details

Inherited from​

VisionLabeledModule.generateFromFrame

labelMap​

protected readonly labelMap: ResolveLabels

Defined in: modules/computer_vision/VisionLabeledModule.ts:42

Inherited from​

VisionLabeledModule.labelMap

nativeModule​

nativeModule: any = null

Defined in: modules/BaseModule.ts:16

Internal

Native module instance (JSI Host Object)

Inherited from​

VisionLabeledModule.nativeModule

Accessors​

runOnFrame​

Get Signature​

get runOnFrame(): (frame, ...args) => TOutput

Defined in: modules/computer_vision/VisionModule.ts:61

Synchronous worklet function for real-time VisionCamera frame processing.

Only available after the model is loaded.

Use this for VisionCamera frame processing in worklets. For async processing, use forward() instead.

Example​
const model = new ClassificationModule();
await model.load({ modelSource: MODEL });

// Use the functional form of setState to store the worklet — passing it
// directly would cause React to invoke it immediately as an updater fn.
const [runOnFrame, setRunOnFrame] = useState(null);
setRunOnFrame(() => model.runOnFrame);

const frameOutput = useFrameOutput({
  onFrame(frame) {
    'worklet';
    if (!runOnFrame) return;
    const result = runOnFrame(frame, isFrontCamera);
    frame.dispose();
  }
});
Throws​

If the model is not loaded.

Returns​

A worklet function for frame processing.

(frame, ...args): TOutput

Parameters​
frame​

Frame

args​

...any[]

Returns​

TOutput

Inherited from​

VisionLabeledModule.runOnFrame

Methods​

delete()​

delete(): void

Defined in: modules/BaseModule.ts:81

Unloads the model from memory and releases native resources.

Always call this method when you're done with a model to prevent memory leaks.

Returns​

void

Inherited from​

VisionLabeledModule.delete

forward()​

forward(input): Promise<Record<keyof ResolveLabels<T, { efficientnet-v2-s: { labelMap: typeof Imagenet1kLabel; }; efficientnet-v2-s-quantized: { labelMap: typeof Imagenet1kLabel; }; }>, number>>

Defined in: modules/computer_vision/ClassificationModule.ts:145

Executes the model's forward pass to classify the provided image.

Parameters​

input​

A string image source (file path, URI, or Base64) or a PixelData object.

string | PixelData

Returns​

Promise<Record<keyof ResolveLabels<T, { efficientnet-v2-s: { labelMap: typeof Imagenet1kLabel; }; efficientnet-v2-s-quantized: { labelMap: typeof Imagenet1kLabel; }; }>, number>>

A Promise resolving to an object mapping label keys to confidence scores.

Overrides​

VisionLabeledModule.forward

forwardET()​

protected forwardET(inputTensor): Promise<TensorPtr[]>

Defined in: modules/BaseModule.ts:62

Internal

Runs the model's forward method with the given input tensors. It returns the output tensors that mimic the structure of output from ExecuTorch.

Parameters​

inputTensor​

TensorPtr[]

Array of input tensors.

Returns​

Promise<TensorPtr[]>

Array of output tensors.

Inherited from​

VisionLabeledModule.forwardET

getInputShape()​

getInputShape(methodName, index): Promise<number[]>

Defined in: modules/BaseModule.ts:72

Gets the input shape for a given method and index.

Parameters​

methodName​

string

method name

index​

number

index of the argument which shape is requested

Returns​

Promise<number[]>

The input shape as an array of numbers.

Inherited from​

VisionLabeledModule.getInputShape

fromCustomModel()​

static fromCustomModel<L>(modelSource, config, onDownloadProgress?): Promise<ClassificationModule<L>>

Defined in: modules/computer_vision/ClassificationModule.ts:113

Creates a classification instance with a user-provided model binary and label map. Use this when working with a custom-exported model that is not one of the built-in presets.

Required model contract​

The .pte model binary must expose a single forward method with the following interface:

Input: one float32 tensor of shape [1, 3, H, W] — a single RGB image, values in [0, 1] after optional per-channel normalization (pixel − mean) / std. H and W are read from the model's declared input shape at load time.

Output: one float32 tensor of shape [1, C] containing raw logits — one value per class, in the same order as the entries in your labelMap. Softmax is applied by the native runtime.

Type Parameters​

L​

L extends Readonly<Record<string, string | number>>

Parameters​

modelSource​

ResourceSource

A fetchable resource pointing to the model binary.

config​

ClassificationConfig<L>

A ClassificationConfig object with the label map and optional preprocessing parameters.

onDownloadProgress?​

(progress) => void

Optional callback to monitor download progress, receiving a value between 0 and 1.

Returns​

Promise<ClassificationModule<L>>

A Promise resolving to a ClassificationModule instance typed to the provided label map.

fromModelName()​

static fromModelName<C>(namedSources, onDownloadProgress?): Promise<ClassificationModule<ModelNameOf<C>>>

Defined in: modules/computer_vision/ClassificationModule.ts:64

Creates a classification instance for a built-in model.

Type Parameters​

C​

C extends ClassificationModelSources

Parameters​

namedSources​

C

A ClassificationModelSources object specifying which model to load and where to fetch it from.

onDownloadProgress?​

(progress) => void

Optional callback to monitor download progress, receiving a value between 0 and 1.

Returns​

Promise<ClassificationModule<ModelNameOf<C>>>

A Promise resolving to a ClassificationModule instance typed to the chosen model's label map.

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