/TensorRT-YOLOv4

tensorrt5, yolov4, yolov3,yolov3-tniy,yolov3-tniy-prn

Primary LanguageC++

TensorRT-YOLOv4

demo

image image

Performance

model input_size GPU mode inference Time
yolov4 608x608 gtx 1080Ti float32 23.3 ms
yolov4 416x416 gtx 1080Ti float32 13.0 ms
yolov3 608x608 gtx 1080Ti float32 18.2 ms
yolov3 416x416 gtx 1080Ti float32 10.0 ms
yolov3-tiny 608x608 gtx 1080Ti float32 3.31 ms
yolov3-tiny 416x416 gtx 1080Ti float32 2.01 ms
yolov3-tiny-prn 608x608 gtx 1080Ti float32 3.05 ms
yolov3-tiny-prn 416x416 gtx 1080Ti float32 2.01 ms
  1. Including pre-processing and post-processing time.

Enviroments

  1. gtx 1080Ti
ubuntu 1604
TensorRT 5.0
cuda 9.0
python3 onnx=1.4.1

Models

  1. Add (infer_thresh) and (down_stride) to your .cfg.
[yolo]  ## small anchor
mask = 0,1,2
anchors = ...
down_stride = 8
infer_thresh = 0.5  
...
[yolo] ## mid anchor
mask = 3,4,5
anchors = ...
down_stride = 16
infer_thresh = 0.5
...
[yolo] ## big anchor
mask = 6,7,8
anchors = .....
down_stride = 32
infer_thresh = 0.5
  1. Convert darknet yolo to onnx.
python3 tools/yolo_to_onnx.py --cfg model/yolov4.cfg --weights model/yolov4.weights --out model/yolov4.onnx

Example

git clone https://github.com/CaoWGG/TensorRT-YOLOv4.git
cd TensorRT-YOLOv4
mkdir build
cd build && cmake .. && make
cd ..
## yolov3
./buildEngine -i model/yolov3.onnx -o model/yolov3.engine -b 1 -m 0
./runDet -i model/yolov3.engine -p dog.jpg -v nuscenes_mini.mp4
## yolov4
./buildEngine -i model/yolov4.onnx -o model/yolov4.engine -b 1 -m 0
./runDet -i model/yolov4.engine -p dog.jpg -v nuscenes_mini.mp4

Related projects