yolov3-spp

yolov3-spp

yolov4 is here.

The Pytorch implementation is ultralytics/yolov3. It provides two trained weights of yolov3-spp, yolov3-spp.pt and yolov3-spp-ultralytics.pt(originally named ultralytics68.pt).

Following tricks are used in this yolov3-spp:

• Yololayer plugin is different from the plugin used in this repo's yolov3. In this version, three yololayer are implemented in one plugin to improve speed, codes derived from lewes6369/TensorRT-Yolov3
• Batchnorm layer, implemented by scale layer.

Excute:

1. generate yolov3-spp_ultralytics68.wts from pytorch implementation with yolov3-spp.cfg and yolov3-spp-ultralytics.pt, or download .wts from model zoo

git clone https://github.com/wang-xinyu/tensorrtx.git
git clone https://github.com/ultralytics/yolov3.git
// download its weights 'yolov3-spp-ultralytics.pt'
cd yolov3
cp ../tensorrtx/yolov3-spp/gen_wts.py .
python gen_wts.py yolov3-spp-ultralytics.pt
// a file 'yolov3-spp_ultralytics68.wts' will be generated.
// the master branch of yolov3 should work, if not, you can checkout 4ac60018f6e6c1e24b496485f126a660d9c793d8

2. put yolov3-spp_ultralytics68.wts into yolov3-spp, build and run

mv yolov3-spp_ultralytics68.wts ../tensorrtx/yolov3-spp/
cd ../tensorrtx/yolov3-spp
mkdir build
cd build
cmake ..
make
sudo ./yolov3-spp -s             // serialize model to plan file i.e. 'yolov3-spp.engine'
sudo ./yolov3-spp -d  ../samples // deserialize plan file and run inference, the images in samples will be processed.

3. check the images generated, as follows. _zidane.jpg and _bus.jpg

Config

• Input shape defined in yololayer.h
• Number of classes defined in yololayer.h
• FP16/FP32 can be selected by the macro in yolov3-spp.cpp
• GPU id can be selected by the macro in yolov3-spp.cpp
• NMS thresh in yolov3-spp.cpp
• BBox confidence thresh in yolov3-spp.cpp

More Information

See the readme in home page.