TensorRT-YOLOv4
demo
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 |
- Including pre-processing and post-processing time.
Enviroments
- gtx 1080Ti
ubuntu 1604
TensorRT 5.0
cuda 9.0
python3 onnx=1.4.1
Models
- 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
- 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