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YOLOv8 on Jetson

Only test on Jetson-NX 4GB

ENVS:

  • Jetpack 4.6.3
  • CUDA-10.2
  • CUDNN-8.2.1
  • TensorRT-8.2.1
  • DeepStream-6.0.1
  • OpenCV-4.1.1
  • CMake-3.10.2

If you have other environment-related issues, please discuss in issue.

End2End Detection

1. Export Detection End2End ONNX

yolov8s.pt is your trained pytorch model, or the official pre-trained model.

Do not use any model other than pytorch model. Do not use build.py to export engine if you don't know how to install pytorch and other environments on jetson.

!!! Please use the PC to execute the following script !!!

# Export yolov8s.pt to yolov8s.onnx
python3 export-det.py --weights yolov8s.pt --sim

!!! Please use the Jetson to execute the following script !!!

# Using trtexec tools for export engine
/usr/src/tensorrt/bin/trtexec \
--onnx=yolov8s.onnx \
--saveEngine=yolov8s.engine

After executing the above command, you will get an engine named yolov8s.engine .

2. Inference with c++

It is highly recommended to use C++ inference on Jetson. Here is a demo: csrc/jetson/detect .

Build:

Please modify CLASS_NAMES and COLORS in main.cpp for yourself.

And build:

export root=${PWD}
cd src/jetson/detect
mkdir build
cmake ..
make
mv yolov8 ${root}
cd ${root}

Usage:

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

Speedup Segmention

1. Export Segmention Speedup ONNX

yolov8s-seg.pt is your trained pytorch model, or the official pre-trained model.

Do not use any model other than pytorch model. Do not use build.py to export engine if you don't know how to install pytorch and other environments on jetson.

!!! Please use the PC to execute the following script !!!

# Export yolov8s-seg.pt to yolov8s-seg.onnx
python3 export-seg.py --weights yolov8s-seg.pt --sim

!!! Please use the Jetson to execute the following script !!!

# Using trtexec tools for export engine
/usr/src/tensorrt/bin/trtexec \
--onnx=yolov8s-seg.onnx \
--saveEngine=yolov8s-seg.engine

After executing the above command, you will get an engine named yolov8s-seg.engine .

2. Inference with c++

It is highly recommended to use C++ inference on Jetson. Here is a demo: csrc/jetson/segment .

Build:

Please modify CLASS_NAMES and COLORS and postprocess parameters in main.cpp for yourself.

int topk = 100;
int seg_h = 160; // yolov8 model proto height
int seg_w = 160; // yolov8 model proto width
int seg_channels = 32; // yolov8 model proto channels
float score_thres = 0.25f;
float iou_thres = 0.65f;

And build:

export root=${PWD}
cd src/jetson/segment
mkdir build
cmake ..
make
mv yolov8-seg ${root}
cd ${root}

Usage:

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

Normal Posture

1. Export Posture Normal ONNX

yolov8s-pose.pt is your trained pytorch model, or the official pre-trained model.

Do not use any model other than pytorch model. Do not use build.py to export engine if you don't know how to install pytorch and other environments on jetson.

!!! Please use the PC to execute the following script !!!

# Export yolov8s-pose.pt to yolov8s-pose.onnx
yolo export model=yolov8s-pose.pt format=onnx simplify=True

!!! Please use the Jetson to execute the following script !!!

# Using trtexec tools for export engine
/usr/src/tensorrt/bin/trtexec \
--onnx=yolov8s-pose.onnx \
--saveEngine=yolov8s-pose.engine

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

2. Inference with c++

It is highly recommended to use C++ inference on Jetson. Here is a demo: csrc/jetson/pose .

Build:

Please modify KPS_COLORS and SKELETON and LIMB_COLORS and postprocess parameters in main.cpp for yourself.

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

And build:

export root=${PWD}
cd src/jetson/pose
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