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TRTForYolov3

996.icu

Desc

tensorRT for Yolov3

Test Enviroments

Ubuntu  16.04  or Jetson Nano
TensorRT 5.1/5.0.2.6/4.0.1.6
CUDA 9.2 or CUDA 9.0 or CUDA 10.0

Models

Download the caffe model converted by official model:

  • Baidu Cloud here pwd: gbue
  • Google Drive here

If run model trained by yourself, comment the "upsample_param" blocks, and modify the prototxt the last layer as:

layer {
    #the bottoms are the yolo input layers
    bottom: "layer82-conv"
    bottom: "layer94-conv"
    bottom: "layer106-conv"
    top: "yolo-det"
    name: "yolo-det"
    type: "Yolo"
}

It also needs to change the yolo configs in "YoloConfigs.h" if different kernels.

build source code

git submodule update --init --recursive mkdir build cd build && cmake .. && make && make install cd ..

what I do

1.Added multithreading

2.Added tag name

3.Added video inference

for yolov3-608

video

./install/runYolov3 --caffemodel=./yolov3_608.caffemodel --prototxt=./yolov3_608.prototxt --display=1 --inputstream=video --videofile=sample_720p.mp4 --classname=coco.names

cam

./install/runYolov3 --caffemodel=./yolov3_608.caffemodel --prototxt=./yolov3_608.prototxt --display=1 --inputstream=cam --cam=0 --classname=coco.names

int8

./install/runYolov3 --caffemodel=./yolov3_608.caffemodel --prototxt=./yolov3_608.prototxt --display=1 --inputstream=cam --cam=0 --classname=coco.names --mode=int8 --calib=cal.list

example

图片alt

Performance

Model TYPE GPU Mode Inference Time FPS
Yolov3-608 tensorRT GTX 1060(laptop)(win10) float32 58ms 15
Yolov3-608 tensorRT GTX 1060(laptop)(win10) int8 33ms 18
Yolov3-608 tensorRT P40 float32 20ms 40(display)
Yolov3-608 darknet P40 float32 -- 30(display)
Yolov3-608 tensorRT P40 int8 13ms 50(display)
Yolov3-416 tensorRT P40 float32 12ms 60(display)
Yolov3-416 darknet P40 float32 -- 50(display)
Yolov3-416 tensorRT P40 int8 8ms 70(display)
Yolov3-416 tensorRT jeston nano fp16 343ms 2.8
Yolov3-tiny-416 darknet jeston nano fp32 -- 17(display)
Yolov3-tiny-416 tensorRT jeston nano fp16 37ms 25(display)

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