usls/examples/yolov8/README.md

Features

• Support Classification, Segmentation, Detection, Pose(Keypoints)-Detection tasks.
• Support FP16 & FP32 ONNX models.
• Support CoreML, CUDA and TensorRT execution provider to accelerate computation.
• Support dynamic input shapes(batch, width, height).
• Support dynamic confidence(DynConf) for each class in Detection task.

Quick Start

cargo run -r --example yolov8

Or you can manully

1. Export YOLOv8 ONNX Models

pip install -U ultralytics

# export onnx model with dynamic shapes
yolo export model=yolov8m.pt format=onnx simplify dynamic
yolo export model=yolov8m-cls.pt format=onnx simplify dynamic
yolo export model=yolov8m-pose.pt format=onnx simplify dynamic
yolo export model=yolov8m-seg.pt format=onnx simplify dynamic

# export onnx model with fixed shapes
yolo export model=yolov8m.pt format=onnx simplify
yolo export model=yolov8m-cls.pt format=onnx simplify
yolo export model=yolov8m-pose.pt format=onnx simplify
yolo export model=yolov8m-seg.pt format=onnx simplify

2. Specify the ONNX model path in main.rs

let options = Options::default()
    .with_model("ONNX_PATH")   // <= modify this
    .with_confs(&[0.4, 0.15]) // person: 0.4, others: 0.15
    .with_saveout("YOLOv8");
let mut model = YOLO::new(&options)?;

3. Then, run

cargo run -r --example yolov8

Result

Task	Annotated image
Instance Segmentation
Classification
Detection
Pose