> For the complete documentation index, see [llms.txt](https://docs.roboflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roboflow.com/deploy/sdks/web-browser/web-inference.js/inferencejs-reference.md).

# inferencejs Reference

{% hint style="info" %}
Learn more about `inferencejs` , our web SDK, [here](/deploy/sdks/web-browser.md)
{% endhint %}

### Installation

This library is designed to be used within the browser, using a bundler such as vite, webpack, parcel, etc. Assuming your bundler is set up, you can install by executing:

`npm install inferencejs`

### Getting Started

Begin by initializing the `InferenceEngine`. This will start a background worker which is able to download and\
execute models without blocking the user interface.

```typescript
import { InferenceEngine } from "inferencejs";

const PUBLISHABLE_KEY = "rf_a6cd..."; // replace with your own publishable key from Roboflow

const inferEngine = new InferenceEngine();

// Load a model by its model id, copied from the model's page on Roboflow.
const workerId = await inferEngine.startWorkerByModelId("[MODEL ID]", PUBLISHABLE_KEY);

//make inferences against the model
const result = await inferEngine.infer(workerId, img);
```

The model id is shown on the model's page and has the form `workspace/model-slug` (ex: `my-workspace/hard-hat-abc-1-yolov8n-t1`). Legacy project-versioned models can be loaded with `startWorker` using a project slug and version number. For a version with several trained models, the legacy `{project}/{version}` id resolves through the version's alias when one exists; without an alias it works only if the version has a single model, otherwise the id is ambiguous and you should load the model by its own model id. See [how legacy IDs resolve](/train/model-ids.md).

## API

### InferenceEngine

**`new InferenceEngine()`**

Creates a new InferenceEngine instance.

**`startWorkerByModelId(modelId: string, publishableKey: string): Promise<number>`**

Starts a new worker for the given model id and returns the `workerId`. This is the recommended way to load a model. `modelId` is the full `workspace/model-slug` id shown on the model's page, and works for versionless and multi-model versions. `publishableKey` is required and can be obtained from Roboflow in your project settings folder.

**`startWorker(modelName: string, modelVersion: number, publishableKey: string): Promise<number>`**

Loads a legacy model addressed by project url slug and integer version, and returns the `workerId`. Use `startWorkerByModelId` for new integrations. `publishableKey` is required and can be obtained from Roboflow in your project settings folder.

**`infer(workerId: number, img: CVImage | ImageBitmap): Promise<Inference>`**

Infer on n image using the worker with the given `workerId`. `img` can be created using `new CVImage(HTMLImageElement | HTMLVideoElement | ImageBitmap | TFJS.Tensor)` or [`createImageBitmap`](https://developer.mozilla.org/en-US/docs/Web/API/createImageBitmap)

**`stopWorker(workerId: number): Promise<void>`**

Stops the worker with the given `workerId`.

### `YOLO Lite` `YOLOv8` `YOLOv5`

The result of making an inference using the `InferenceEngine` on a YOLO Lite, YOLOv8, or YOLOv5 object detection model is an array of the following type:

```typescript
type RFObjectDetectionPrediction = {
    class?: string;
    confidence?: number;
    bbox?: {
        x: number;
        y: number;
        width: number;
        height: number;
    };
    color?: string;
};
```

### `GazeDetections`

The result of making an inference using the `InferenceEngine` on a Gaze model. An array with the following type:

```typescript
type GazeDetections = {
    leftEye: { x: number; y: number };
    rightEye: { x: number; y: number };
    yaw: number;
    pitch: number;
}[];
```

**`leftEye.x`**

The x position of the left eye as a floating point number between 0 and 1, measured in percentage of the input image width.

**`leftEye.y`**

The y position of the left eye as a floating point number between 0 and 1, measured in percentage of the input image height.

**`rightEye.x`**

The x position of the right eye as a floating point number between 0 and 1, measured in percentage of the input image width.

**`rightEye.y`**

The y position of the right eye as a floating point number between 0 and 1, measured in percentage of the input image height.

**`yaw`**

The yaw of the visual gaze, measured in radians.

**`pitch`**

The pitch of the visual gaze, measured in radians.

### `CVImage`

A class representing an image that can be used for computer vision tasks. It provides various methods to manipulate and convert the image.

#### **Constructor**

The `CVImage(image)` class constructor initializes a new instance of the class. It accepts one image of one of the following types:

* `ImageBitmap`: An optional `ImageBitmap` representation of the image.
* `HTMLImageElement`: An optional `HTMLImageElement` representation of the image.
* `tf.Tensor`: An optional `tf.Tensor` representation of the image.
* `tf.Tensor4D`: An optional 4D `tf.Tensor` representation of the image.

#### **Methods**

**`bitmap()`**

Returns a promise that resolves to an `ImageBitmap` representation of the image. If the image is already a bitmap, it returns the cached bitmap.

**`tensor()`**

Returns a `tf.Tensor` representation of the image. If the image is already a tensor, it returns the cached tensor.

**`tensor4D()`**

Returns a promise that resolves to a 4D `tf.Tensor` representation of the image. If the image is already a 4D tensor, it returns the cached 4D tensor.

**`array()`**

Returns a promise that resolves to a JavaScript array representation of the image. If the image is already a tensor, it converts the tensor to an array.

**`dims()`**

Returns an array containing the dimensions of the image. If the image is a bitmap, it returns `[width, height]`. If the image is a tensor, it returns the shape of the tensor. If the image is an HTML image element, it returns `[width, height]`.

**`dispose()`**

Disposes of the tensor representations of the image to free up memory.

**`static fromArray(array: tf.TensorLike)`**

Creates a new `CVImage` instance from a given tensor-like array.