YOLOv8-TensorRT/docs/Pose.md

YOLOv8-pose Model with TensorRT

The yolov8-pose model conversion route is : YOLOv8 PyTorch model -> ONNX -> TensorRT Engine

Notice !!! We don't support TensorRT API building !!!

Export Orin ONNX model by ultralytics

You can leave this repo and use the original ultralytics repo for onnx export.

1. ONNX -> TensorRT

You can export your onnx model by ultralytics API.

yolo export model=yolov8s-pose.pt format=onnx opset=11 simplify=True

or run this python script:

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8s-pose.pt")  # load a pretrained model (recommended for training)
success = model.export(format="onnx", opset=11, simplify=True)  # export the model to onnx format
assert success

Then build engine by Trtexec Tools.

You can export TensorRT engine by trtexec tools.

Usage:

/usr/src/tensorrt/bin/trtexec \
--onnx=yolov8s-pose.onnx \
--saveEngine=yolov8s-pose.engine \
--fp16

2. Direct to TensorRT (NOT RECOMMAND!!)

Usage:

yolo export model=yolov8s-pose.pt format=engine device=0

or run python script:

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8s-pose.pt")  # load a pretrained model (recommended for training)
success = model.export(format="engine", device=0)  # export the model to engine format
assert success

After executing the above script, you will get an engine named yolov8s-pose.engine .

Inference

Infer with python script

You can infer images with the engine by infer-pose.py .

Usage:

python3 infer-pose.py \
--engine yolov8s-pose.engine \
--imgs data \
--show \
--out-dir outputs \
--device cuda:0

Description of all arguments

• --engine : The Engine you export.
• --imgs : The images path you want to detect.
• --show : Whether to show detection results.
• --out-dir : Where to save detection results images. It will not work when use --show flag.
• --device : The CUDA deivce you use.

Inference with c++

You can infer with c++ in csrc/pose/normal .

Build:

Please set you own librarys in CMakeLists.txt and modify KPS_COLORS and SKELETON and LIMB_COLORS in main.cpp.

Besides, you can modify the postprocess parameters such as score_thres and iou_thres and topk in main.cpp.

int topk = 100;
float score_thres = 0.25f;
float iou_thres = 0.65f;

And build:

export root=${PWD}
cd src/pose/normal
mkdir build
cmake ..
make
mv yolov8-pose ${root}
cd ${root}

Usage:

# infer image
./yolov8-pose yolov8s-pose.engine data/bus.jpg
# infer images
./yolov8-pose yolov8s-pose.engine data
# infer video
./yolov8-pose yolov8s-pose.engine data/test.mp4 # the video path