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yolo26/README.md

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Introduction

Yolo26 model supports TensorRT-8.

Training code link

Environment

  • cuda 12.4
  • cudnn 9.1.0.70
  • tensorrt 8.6.3
  • opencv 4.8.0
  • ultralytics 8.4.0

Support

  • [✅] Yolo26n-det, Yolo26s-det, Yolo26m-det, Yolo26l-det, Yolo26sx-det, support FP32/FP16 and C++ API
  • [✅] Yolo26n-obb, Yolo26s-obb, Yolo26m-obb, Yolo26l-obb, Yolo26sx-obb, support FP32/FP16 and C++ API
  • [✅] Yolo26n-cls, Yolo26s-cls, Yolo26m-cls, Yolo26l-cls, Yolo26sx-cls, support FP32/FP16 and C++ API

COMING FEATURES

  • [⏳] Windows OS Support
  • [⏳] Support Batched Inputs
  • [⏳] Support Quantization
  • [⏳] Yolo26-cls models
  • [⏳] Yolo26-pose models
  • [⏳] Yolo26-seg models

Config

  • Choose the YOLO26 sub-model n/s/m/l/x from command line arguments.
  • Other configs please check include/config.h

Build and Run

  1. generate .wts from pytorch with .pt, or download .wts from model zoo
shell
# Download ultralytics
wget https://github.com/ultralytics/ultralytics/archive/refs/tags/v8.4.4.zip -O ultralytics-8.4.4.zip
# Unzip ultralytics
unzip ultralytics-8.4.4.zip
cd ultralytics-8.4.4
# Download models For Detection
wget https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n.pt -O yolo26n.pt # to download other models, replace 'yolo26n.pt' with 'yolo26s.pt', 'yolo26m.pt', 'yolo26l.pt' or 'yolo26x.pt'
# Generate .wts
cp [PATH-TO-MAIN-FOLDER]/gen_wts.py .
python gen_wts.py -w yolo26n.pt -o yolo26n.wts -t detect
# A file 'yolo26n.wts' will be generated.

# Download models for Obb
wget https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n-obb.pt -O yolo26n-obb.pt # to download other models, replace 'yolo26n-obb.pt' with 'yolo26s-obb.pt', 'yolo26m-obb.pt', 'yolo26l-obb.pt' or 'yolo26x-obb.pt'
# Generate .wts
cp [PATH-TO-MAIN-FOLDER]/gen_wts.py .
python gen_wts.py -w yolo26n-obb.pt -o yolo26n-obb.wts -t obb
# A file 'yolo26n-obb.wts' will be generated.

# Download models for Cls
wget https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n-cls.pt -O yolo26n-cls.pt # to download other models, replace 'yolo26n-cls.pt' with 'yolo26s-cls.pt', 'yolo26m-cls.pt', 'yolo26l-cls.pt' or 'yolo26x-cls.pt'
# Generate .wts
cp [PATH-TO-MAIN-FOLDER]/gen_wts.py .
python gen_wts.py -w yolo26n-cls.pt -o yolo26n-cls.wts -t cls
# A file 'yolo26n-cls.wts' will be generated.
  1. build and run
shell
cd [PATH-TO-MAIN-FOLDER]
mkdir build
cd build
cmake ..
make

Detection

shell
cp [PATH-TO-ultralytics]/yolo26n.wts .
# Build and serialize TensorRT engine
./yolo26_det -s yolo26n.wts yolo26n.engine [n/s/m/l/x]
# Run inference
./yolo26_det -d yolo26n.engine ../images
# results saved in build directory

Obb

shell
cp [PATH-TO-ultralytics]/yolo26n-obb.wts .
# Build and serialize TensorRT engine
./yolo26_obb -s yolo26n-obb.wts yolo26n-obb.engine [n/s/m/l/x]
# Run inference
./yolo26_obb -d yolo26n-obb.engine ../images
# results saved in build directory

Cls

shell
Generate classification text file in build folder or download it
# wget https://github.com/joannzhang00/ImageNet-dataset-classes-labels/blob/main/imagenet_classes.txt

cp [PATH-TO-ultralytics]/yolo26n-cls.wts .
# Build and serialize TensorRT engine
./yolo26_cls -s yolo26n-cls.wts yolo26n-cls.engine [n/s/m/l/x]
# Run inference
./yolo26_cls -d yolo26n-cls.engine ../images
# results saved in build directory

More Information

See the readme in home page.