ppu-yolo-onnx-inference

Easily run YOLOv11 object detection models in a TypeScript Bun environment. No Python, PyTorch, or heavy dependencies needed. Supports multiple independent instances of YOLOv11 models, each with its own inference session.

See: Demo repo

YOLO in javascript runtime should as easy as:

import { YoloDetectionInference } from "ppu-yolo-onnx-inference";

const model = new YoloDetectionInference({
  model: {
    onnx: myOnnxModel,
    classNames: ["person", "car", "bicycle"],
  },
  thresholds: {
    confidence: 0.5,
  },
});

await model.init();
const detections = await model.detect(imageBuffer);
await model.destroy();

Why use this library?

• ✅ Lightweight & Fast: Inference runs with onnxruntime-web or onnxruntime-node in a JS/TS environment. No Python or PyTorch required.
• ✅ Multi-instance Ready: You can load and run multiple YOLO models (even different sizes) independently and concurrently.
• ✅ Flexible Deployment: Ideal for server-side Bun inference or potential browser/WebAssembly support in the future.
• ✅ Easy Integration: Minimal configuration, with out-of-the-box support for ONNX models.
• ✅ Bun Optimized: Designed for Bun’s performance, though can be extended for Node.js with community help.

Installation

Install using your preferred package manager:

npm install ppu-yolo-onnx-inference
yarn add ppu-yolo-onnx-inference
bun add ppu-yolo-onnx-inference

Note

This project is developed and tested primarily with Bun.
Support for Node.js, Deno, or browser environments is not guaranteed.

If you choose to use it outside of Bun and encounter any issues, feel free to report them.
I'm open to fixing bugs for other runtimes with community help.

Getting the onnx and class names

See yolo-convert-onnx.py to get the onnx file and class name list.

Configuration

All options are grouped under the YoloDetectionOptions interface:

export interface YoloDetectionOptions {
  /** File paths to the required Onnx YOLO model and class name list. */
  model: ModelOptions;

  /** Controls threshold for object detection. */
  thresholds?: ModelThresholds;

  /** Controls model input output tensor metadata. */
  modelMetadata?: ModelMetadata;

  /** Controls logging and image dump behavior for debugging. */
  debug?: DebuggingOptions;
}

ModelOptions

Property	Type	Description
onnx	ArrayBuffer	The YOLOv11 ONNX model file.
classNames	string[]	Array of class names corresponding to model output indices.

ModelThresholds

Property	Type	Description
confidence?	number	Minimum confidence for a detection (default 0.75).
iou?	number	IOU threshold for NMS filtering (default 0.5).
classConfidence?	number	Per-class confidence threshold (default 0.2).

ModelMetadata

Property	Type	Description
inputShape	[number, number]	Input image shape (e.g., [640, 640]). Defaults to model metadata.
inputTensorName	string	Output tensor name (default from model metadata).
outputTensorName	string	Input tensor name (default from model metadata).

DebuggingOptions

Property	Type	Default	Description
verbose	boolean	false	Turn on detailed console logs of each processing step.
debug	boolean	false	Write intermediate image frames to disk.
debugFolder	string	"out"	Directory (relative to CWD) to save debug image outputs.

Result example

[
  {
    box: {
      x: 275,
      y: 6,
      width: 24,
      height: 38,
    },
    className: "person",
    classId: 0,
    confidence: 0.9873744249343872,
  },
  {
    box: {
      x: 5,
      y: 2,
      width: 24,
      height: 38,
    },
    className: "car",
    classId: 1,
    confidence: 0.9779072999954224,
  },
  {
    box: {
      x: 247,
      y: 6,
      width: 24,
      height: 39,
    },
    className: "bicycle",
    classId: 2,
    confidence: 0.9770053625106812,
  },
  {
    box: {
      x: 32,
      y: 3,
      width: 23,
      height: 38,
    },
    className: "car",
    classId: 1,
    confidence: 0.9710473418235779,
  },
];

Contributing

Contributions are welcome! If you would like to contribute, please follow these steps:

1. Fork the Repository: Create your own fork of the project.
2. Create a Feature Branch: Use a descriptive branch name for your changes.
3. Implement Changes: Make your modifications, add tests, and ensure everything passes.
4. Submit a Pull Request: Open a pull request to discuss your changes and get feedback.

Running Tests

This project uses Bun for testing. To run the tests locally, execute:

bun test

Ensure that all tests pass before submitting your pull request.

Scripts

Recommended development environment is in linux-based environment.
Library template: https://github.com/aquapi/lib-template

All script sources and usage.

Build

Emit .js and .d.ts files to lib.

Publish

Move package.json, README.md to lib and publish the package.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Support

If you encounter any issues or have suggestions, please open an issue in the repository.

Happy coding!