Readme

Introduction

Yolov13 model supports TensorRT-8.

Detection training code link

Environment

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

Support

• YOLOV13-det support FP32/FP16/INT8 and C++ API

Config

• Choose the YOLOV13 sub-model n/s/l/x from command line arguments.
• Other configs please check include/config.h

Build and Run (Detection)

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

# Download ultralytics
wget https://github.com/iMoonLab/yolov13/releases/tag/yolov13 -O ultralytics-8.3.63.zip
# Unzip ultralytics
unzip ultralytics-8.3.63.zip
cd ultralytics-8.3.63
# Training your ownself models
to download other models, replace 'yolov13n.pt' with 'yolov13s.pt', 'yolov13l.pt', or 'yolov13x.pt'
# Generate .wts
cp [PATH-TO-TENSORRTX]/yolov13/gen_wts.py .
python3 gen_wts.py -w yolov13n.pt -o yolov13n.wts
# A file 'yolov13n.wts' will be generated.

2. build tensorrtx/yolov13 and run

cd [PATH-TO-TENSORRTX]/yolov13
mkdir build
cd build
cmake ..
make

Detection

cp [PATH-TO-ultralytics]/yolov13n.wts .
# Build and serialize TensorRT engine
./yolov13-det -s yolov13n.wts yolov13n-det.engine [n/s/l/x]
# Run inference
./yolov13-det -d yolov13n-det.engine ../images [c/g]
# results saved in build directory

INT8 Quantization

1. Prepare calibration images, you can randomly select 1000s images from your train set. For coco, you can also download the calibration images coco_calib from GoogleDrive or BaiduPan pwd: a9wh
2. unzip it in [PATH-TO-TENSORRTX]/yolov13/build
3. set the macro USE_INT8 in include/config.h and make again
4. serialize the model and test ... build successfully in my 4060 ...

More Information

See the readme in home page.