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# Ultralytics YOLO npm Inference
[English](README.md) | [简体中文](README.zh-CN.md)
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Run [Ultralytics](https://www.ultralytics.com) YOLO models directly in the browser,
with no server and no Python. It runs on **WebGPU** (with an automatic CPU/wasm
fallback) and covers detection, segmentation, pose, classification, OBB,
semantic segmentation, and depth estimation, behind a small TypeScript API with a built-in
`annotate()` that draws results straight to a canvas.
```ts
import { YOLO, annotate } from "@ultralytics/yolo";
const model = await YOLO.load("/models/yolo26n.onnx");
const results = await model.predict("bus.jpg");
await annotate(document.querySelector("canvas"), "bus.jpg", results);
```
It is a **library only** (no CLI; that is the native Rust crate). Under the hood
the engine is the `ultralytics-inference` Rust crate compiled to WebAssembly.
Inference runs on [ONNX Runtime Web](https://onnxruntime.ai/docs/tutorials/web/)
via [`ort-web`](https://ort.pyke.io/backends/web), and all pre/postprocessing,
colors, and the pose skeleton come from that shared Rust code, so results and
visuals match the native and Python paths.
## 📦 Install
```bash
npm install @ultralytics/yolo
# or
pnpm add @ultralytics/yolo
yarn add @ultralytics/yolo
bun add @ultralytics/yolo
```
It ships as an ES module with TypeScript types and works in any modern bundler
(Vite, webpack, esbuild, Bun) or directly via a CDN such as
[esm.sh](https://esm.sh/@ultralytics/yolo).
## 🚀 Quick Start
```ts
import { YOLO, annotate } from "@ultralytics/yolo";
// Loads the model and initializes WebGPU + ONNX Runtime Web on first use.
const model = await YOLO.load("/models/yolo26n.onnx");
const results = await model.predict("bus.jpg");
for (const box of results.boxes) {
console.log(box.name, box.conf.toFixed(2), [box.x1, box.y1, box.x2, box.y2]);
}
// Draw boxes, OBB, pose, and labels onto a canvas in one call (no canvas code).
await annotate(document.querySelector("canvas"), "bus.jpg", results);
```
`predict()` accepts a URL/path, a `Blob`/`File`, raw encoded image bytes
(`Uint8Array`/`ArrayBuffer`), `ImageData`, an `HTMLImageElement`,
`HTMLCanvasElement`, `HTMLVideoElement`, or an `ImageBitmap`.
```ts
const results = await model.predict(canvas, { conf: 0.25, iou: 0.7 });
console.log(model.device); // "webgpu" or "cpu"
```
`YOLO.load` also takes a `Blob`/`File`, so you can load a model the user drops or
picks. The backend is detected from the bytes, so the same call handles `.onnx`
and `.tflite`:
```ts
const model = await YOLO.load(fileInput.files[0]); // a dropped/picked .onnx or .tflite
```
### Webcam / Video
Drawable sources (`