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Introduction

Yolov12 model supports TensorRT-8.

Detection training code link Segment training codelink Classify training codelink

Environment

  • cuda 11.6
  • cudnn 8.9.1.23
  • tensorrt 8.6.1.6
  • opencv 4.8.0
  • ultralytics 8.3.63

Support

  • YOLO12-det support FP32/FP16 and C++ API
  • YOLO12-seg support FP32/FP16 and C++ API
  • YOLO12-cls support FP32/FP16 and C++ API

Config

  • Choose the YOLO12 sub-model n/s/m/l/x from command line arguments.
  • Other configs please check src/config.h

Build and Run (Detection)

  1. generate .wts from pytorch with .pt, or download .wts from model zoo
shell
# You are supposed to train your own models instead of using the pre-trained models
# to download other models, replace 'yolov12n.pt' with 'yolov12s.pt', 'yolov12m.pt', 'yolov12l.pt' or 'yolov12x.pt'
# Generate .wts
cp [PATH-TO-TENSORRTX]/yolov12/gen_wts.py .
python gen_wts.py -w yolov12n.pt -o yolov12n.wts -t detect
# A file 'yolov12n.wts' will be generated.
  1. build tensorrtx/yolov12 and run
shell
cd [PATH-TO-TENSORRTX]/yolov12
mkdir build
cd build
cmake ..
make

Build and Run (Segment)

  1. generate .wts from pytorch with .pt, or download .wts from model zoo
shell
# You are supposed to train your own models instead of using the pre-trained models
to download other models, replace 'yolov12n-seg.pt' with 'yolov12s-seg.pt', 'yolov12m-seg.pt', 'yolov12l-seg.pt' or 'yolov12x-seg.pt'
# Generate .wts
cp [PATH-TO-TENSORRTX]/yolov12/gen_wts.py .
python gen_wts.py -w yolov12n.pt -o yolov12n.wts -t seg
# A file 'yolov12n.wts' will be generated.
  1. build tensorrtx/yolov12 and run
shell
cd [PATH-TO-TENSORRTX]/yolov12
mkdir build
cd build
cmake ..
make

Build and Run (Classify)

  1. generate .wts from pytorch with .pt, or download .wts from model zoo
shell
# Download ultralytics

# You are supposed to train your own models instead of using the pre-trained models
to download other models, replace 'yolov12n-cls.pt' with 'yolov12s-cls.pt', 'yolov12m-cls.pt', 'yolov12l-cls.pt' or 'yolov12x-cls.pt'
# Generate .wts
cp [PATH-TO-TENSORRTX]/yolov12/gen_wts.py .
python gen_wts.py -w yolov12n-cls.pt -t cls -o yolov12n-cls.wts
# A file 'yolov12n-cls.wts' will be generated.
  1. build tensorrtx/yolov12 and run
shell
cd [PATH-TO-TENSORRTX]/yolov12
mkdir build
cd build
cmake ..
make

Detection

shell
cp [PATH-TO-ultralytics]/yolov12n.wts .
# Build and serialize TensorRT engine
./yolov12_det -s yolov12n.wts yolov12n.engine [n/s/m/l/x]
# Run inference
./yolov12_det -d yolov12n.engine ../images [c/g]
# results saved in build directory

Segment

shell
cp [PATH-TO-ultralytics]/yolov2n-seg.wts .
# Build and serialize TensorRT engine
./yolov12-seg -s yolov12n-seg.wts yolov12n-seg.engine [n/s/m/l/x]
# Run inference
./yolov12-seg -d yolov12n-seg.engine ../images
# results saved in build directory

Classify

shell
cp [PATH-TO-ultralytics]/yolov2n-cls.wts .
# Build and serialize TensorRT engine
./yolov12-cls -s yolov12n-cls.wts yolov12n-cls.engine [n/s/m/l/x]
# Run inference
./yolov12-cls -d yolov12n-cls.engine ../images
# results saved in build directory
## More Information
See the readme in [home page.](https://github.com/wang-xinyu/tensorrtx)