usls

A Rust library integrated with ONNXRuntime, providing a collection of Computer Vison and Vision-Language models including YOLOv5, YOLOv8, YOLOv9, RTDETR, CLIP, DINOv2, FastSAM, YOLO-World, BLIP, PaddleOCR and others.

Recently Updated

YOLOP-v2	Face-Parsing	Text-Detection

YOLOv8-Obb

Supported Models

Model	Task / Type	Example	CUDA f32	CUDA f16	TensorRT f32	TensorRT f16
YOLOv8-obb	Oriented Object Detection	demo	✅	✅	✅	✅
YOLOv8-detection	Object Detection	demo	✅	✅	✅	✅
YOLOv8-pose	Keypoint Detection	demo	✅	✅	✅	✅
YOLOv8-classification	Classification	demo	✅	✅	✅	✅
YOLOv8-segmentation	Instance Segmentation	demo	✅	✅	✅	✅
YOLOv9	Object Detection	demo	✅	✅	✅	✅
RT-DETR	Object Detection	demo	✅	✅	✅	✅
FastSAM	Instance Segmentation	demo	✅	✅	✅	✅
YOLO-World	Object Detection	demo	✅	✅	✅	✅
DINOv2	Vision-Self-Supervised	demo	✅	✅	✅	✅
CLIP	Vision-Language	demo	✅	✅	✅ visual ❌ textual	✅ visual ❌ textual
BLIP	Vision-Language	demo	✅	✅	✅ visual ❌ textual	✅ visual ❌ textual
DB	Text Detection	demo	✅	✅	✅	✅
SVTR	Text Recognition	demo	✅	✅	✅	✅
RTMO	Keypoint Detection	demo	✅	✅	❌	❌
YOLOPv2	Panoptic driving Perception	demo	✅	✅	✅	✅
YOLOv5-classification	Object Detection	demo	✅	✅	✅	✅
YOLOv5-segmentation	Instance Segmentation	demo	✅	✅	✅	✅

Solution Models

Additionally, this repo also provides some solution models.

Model	Example	Result
Lane Line Segmentation Drivable Area Segmentation Car Detection 车道线-可行驶区域-车辆检测	demo
Face Parsing 人脸解析	demo
Text Detection (PPOCR-det v3, v4) 通用文本检测	demo
Text Recognition (PPOCR-rec v3, v4) 中英文-文本识别	demo
Face-Landmark Detection 人脸 & 关键点检测	demo
Head Detection 人头检测	demo
Fall Detection 摔倒检测	demo
Trash Detection 垃圾检测	demo

Demo

cargo run -r --example yolov8   # yolov9, blip, clip, dinov2, svtr, db, yolo-world...

Installation

check ort guide

For Linux or MacOS users

• Firstly, download from latest release from ONNXRuntime Releases
• Then linking

  export ORT_DYLIB_PATH=/Users/qweasd/Desktop/onnxruntime-osx-arm64-1.17.1/lib/libonnxruntime.1.17.1.dylib
  

Integrate into your own project

Check Here

1. Add usls as a dependency to your project's Cargo.toml

cargo add --git https://github.com/jamjamjon/usls

2. Set Options and build model

let options = Options::default()
    .with_model("../models/yolov8m-seg-dyn-f16.onnx");
let mut model = YOLO::new(&options)?;

• If you want to run your model with TensorRT or CoreML

  let options = Options::default()
      .with_trt(0) // using cuda by default
      // .with_coreml(0) 
  

• If your model has dynamic shapes

  let options = Options::default()
      .with_i00((1, 2, 4).into()) // dynamic batch
      .with_i02((416, 640, 800).into())   // dynamic height
      .with_i03((416, 640, 800).into())   // dynamic width
  

• If you want to set a confidence level for each category

  let options = Options::default()
      .with_confs(&[0.4, 0.15]) // person: 0.4, others: 0.15
  

• Go check Options for more model options.

3. Prepare inputs, and then you're ready to go

• Build DataLoader to load images

let dl = DataLoader::default()
    .with_batch(model.batch.opt as usize)
    .load("./assets/")?;

for (xs, _paths) in dl {
    let _y = model.run(&xs)?;
}

• Or simply read one image

let x = vec![DataLoader::try_read("./assets/bus.jpg")?];
let y = model.run(&x)?;

4. Annotate and save results

let annotator = Annotator::default().with_saveout("YOLOv8");
annotator.annotate(&x, &y);