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 |
 |
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