yolov12

Introduction

Yolo12 model supports TensorRT-8.

Training code link

Environment

• cuda 11.8
• cudnn 8.9.1.23
• tensorrt 8.6.1.6
• opencv 4.8.0
• ultralytics 8.3.0

Support

• YOLO12-det 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

1. generate .wts from pytorch with .pt, or download .wts from model zoo

# Download ultralytics
wget https://github.com/ultralytics/ultralytics/archive/refs/tags/v8.3.119.zip -O ultralytics-8.3.119.zip
# Unzip ultralytics
unzip ultralytics-8.3.119.zip
cd ultralytics-8.3.119
# Download models
wget https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo12n.pt -O yolo12n.pt # to download other models, replace 'yolo12n.pt' with 'yolo12s.pt', 'yolo12m.pt', 'yolo12l.pt' or 'yolo12x.pt'
# Generate .wts
cp [PATH-TO-TENSORRTX]/yolov12/gen_wts.py .
python gen_wts.py -w yolo12n.pt -o yolo12n.wts -t detect
# A file 'yolo12n.wts' will be generated.

2. build tensorrtx/yolov12 and run

cd [PATH-TO-TENSORRTX]/yolov12
mkdir build
cd build
cmake ..
make

Detection

cp [PATH-TO-ultralytics]/yolo12n.wts .
# Build and serialize TensorRT engine
./yolo12_det -s yolo12n.wts yolo12n.engine [n/s/m/l/x]
# Run inference
./yolo12_det -d yolo12n.engine ../images [c/g]
# results saved in build directory

More Information

See the readme in home page.