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YOLO-Series
Detection | Instance Segmentation | Pose |
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Classification | Obb |
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Head Detection | Fall Detection | Trash Detection |
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YOLO-World | Face Parsing | FastSAM |
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Quick Start
# customized
cargo run -r --example yolo -- --task detect --ver v8 --nc 6 --model xxx.onnx # YOLOv8
# Classify
cargo run -r --example yolo -- --task classify --ver v5 --scale s --width 224 --height 224 --nc 1000 # YOLOv5
cargo run -r --example yolo -- --task classify --ver v8 --scale n --width 224 --height 224 --nc 1000 # YOLOv8
cargo run -r --example yolo -- --task classify --ver v11 --scale n --width 224 --height 224 --nc 1000 # YOLOv11
# Detect
cargo run -r --example yolo -- --task detect --ver v5 --scale n # YOLOv5
cargo run -r --example yolo -- --task detect --ver v6 --scale n # YOLOv6
cargo run -r --example yolo -- --task detect --ver v7 --scale t # YOLOv7
cargo run -r --example yolo -- --task detect --ver v8 --scale n # YOLOv8
cargo run -r --example yolo -- --task detect --ver v9 --scale t # YOLOv9
cargo run -r --example yolo -- --task detect --ver v10 --scale n # YOLOv10
cargo run -r --example yolo -- --task detect --ver v11 --scale n # YOLOv11
cargo run -r --example yolo -- --task detect --ver rtdetr --scale l # RTDETR
cargo run -r --example yolo -- --task detect --ver v8 --nc 1 --model yolov8s-world-v2-shoes.onnx # YOLOv8-world <local file>
# Pose
cargo run -r --example yolo -- --task pose --ver v8 --scale n # YOLOv8-Pose
cargo run -r --example yolo -- --task pose --ver v11 --scale n # YOLOv11-Pose
# Segment
cargo run -r --example yolo -- --task segment --ver v5 --scale n # YOLOv5-Segment
cargo run -r --example yolo -- --task segment --ver v8 --scale n # YOLOv8-Segment
cargo run -r --example yolo -- --task segment --ver v11 --scale n # YOLOv8-Segment
cargo run -r --example yolo -- --task segment --ver v8 --model FastSAM-s-dyn-f16.onnx # FastSAM <local file>
# Obb
cargo run -r --example yolo -- --ver v8 --task obb --scale n --width 1024 --height 1024 --source images/dota.png # YOLOv8-Obb
cargo run -r --example yolo -- --ver v11 --task obb --scale n --width 1024 --height 1024 --source images/dota.png # YOLOv11-Obb
cargo run -r --example yolo -- --help
for more options
YOLOs configs with Options
Use official YOLO Models
let options = Options::default()
.with_yolo_version(YOLOVersion::V5) // YOLOVersion: V5, V6, V7, V8, V9, V10, RTDETR
.with_yolo_task(YOLOTask::Classify) // YOLOTask: Classify, Detect, Pose, Segment, Obb
// .with_nc(80)
// .with_names(&COCO_CLASS_NAMES_80)
.with_model("xxxx.onnx")?;
Cutomized your own YOLO model
// This config is for YOLOv8-Segment
use usls::{AnchorsPosition, BoxType, ClssType, YOLOPreds};
let options = Options::default()
.with_yolo_preds(
YOLOPreds {
bbox: Some(BoxType::Cxcywh),
clss: ClssType::Clss,
coefs: Some(true),
anchors: Some(AnchorsPosition::After),
..Default::default()
}
)
.with_model("xxxx.onnx")?;
Other YOLOv8 Solution Models
Model | Weights | Datasets |
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Face-Landmark Detection | yolov8-face-dyn-f16 | |
Head Detection | yolov8-head-f16 | |
Fall Detection | yolov8-falldown-f16 | |
Trash Detection | yolov8-plastic-bag-f16 | |
FaceParsing | yolov8-face-parsing-dyn | CelebAMask-HQ [Processed YOLO labels][Python Script] |
Export ONNX Models
YOLOv5
YOLOv6
YOLOv7
YOLOv8, YOLOv11
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
yolo export model=yolov8m-obb.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
yolo export model=yolov8m-obb.pt format=onnx simplify