Skip to content

Latest commit

 

History

History
95 lines (68 loc) · 2.96 KB

readme.md

File metadata and controls

95 lines (68 loc) · 2.96 KB
Raw

TRTForYolov3

Desc

tensorRT for Yolov3

Test Enviroments

Ubuntu  16.04
TensorRT 5.0.2.6/4.0.1.6
CUDA 9.2

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.

Run Sample

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

#for yolov3-608
./install/runYolov3 --caffemodel=./yolov3_608.caffemodel --prototxt=./yolov3_608.prototxt --input=./test.jpg --W=608 --H=608 --class=80

#for fp16
./install/runYolov3 --caffemodel=./yolov3_608.caffemodel --prototxt=./yolov3_608.prototxt --input=./test.jpg --W=608 --H=608 --class=80 --mode=fp16

#for int8 with calibration datasets
./install/runYolov3 --caffemodel=./yolov3_608.caffemodel --prototxt=./yolov3_608.prototxt --input=./test.jpg --W=608 --H=608 --class=80 --mode=int8 --calib=./calib_sample.txt

#for yolov3-416 (need to modify include/YoloConfigs for YoloKernel)
./install/runYolov3 --caffemodel=./yolov3_416.caffemodel --prototxt=./yolov3_416.prototxt --input=./test.jpg --W=416 --H=416 --class=80

Performance

Model GPU Mode Inference Time
Yolov3-416 GTX 1060 Caffe 54.593ms
Yolov3-416 GTX 1060 float32 23.817ms
Yolov3-416 GTX 1060 int8 11.921ms
Yolov3-608 GTX 1060 Caffe 88.489ms
Yolov3-608 GTX 1060 float32 43.965ms
Yolov3-608 GTX 1060 int8 21.638ms
Yolov3-608 GTX 1080 Ti float32 19.353ms
Yolov3-608 GTX 1080 Ti int8 9.727ms
Yolov3-416 GTX 1080 Ti float32 9.677ms
Yolov3-416 GTX 1080 Ti int8 6.129ms

Eval Result

run above models with appending --evallist=labels.txt

int8 calibration data made from 200 pics selected in val2014 (see scripts dir)

Model GPU Mode dataset MAP(0.50) MAP(0.75)
Yolov3-416 GTX 1060 Caffe(fp32) COCO val2014 50.33 33.00
Yolov3-416 GTX 1060 float32 COCO val2014 50.27 32.98
Yolov3-416 GTX 1060 int8 COCO val2014 44.15 30.24
Yolov3-608 GTX 1060 Caffe(fp32) COCO val2014 52.89 35.31
Yolov3-608 GTX 1060 float32 COCO val2014 52.84 35.26
Yolov3-608 GTX 1060 int8 COCO val2014 48.55 35.53

Notice:

  • caffe implementation is little different in yolo layer and nms, and it should be the similar result compared to tensorRT fp32.

Details About Wrapper

see link TensorRTWrapper