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trt-detect

Performance

model input_size GPU mode inference Time
mobilenetv2 512x512 gtx 1070 float32 3.798ms
mobilenetv2 512x512 gtx 1070 int8 1.75ms
mobilenetv2 512x512 jetson TX2 float16 22ms
dla34 512x512 gtx 1070 float32 24ms
dla34 512x512 gtx 1070 int8 19.6ms
dla34 512x512 jetson TX2 fp32 209ms
dla34 512x512 jetson TX2 fp16 186ms
dla34v0 512x512 gtx 1070 float32 12.6ms
dla34v0 512x512 gtx 1070 int8 6.76ms
dla34v0 512x512 jetson TX2 fp32 114ms
dla34v0 512x512 jetson TX2 fp16 80ms
resdcn101 512x512 gtx 1070 float32 20.9ms
resdcn18 512x512 gtx 1070 float32 5.81ms
resdcn18 512x512 gtx 1070 int8 3.63ms
resdcn18 512x512 jetson TX2 fp32 54ms
resdcn18 512x512 jetson TX2 fp16 41ms
  1. support Deform Conv v2.
  2. no nms.
  3. support fp32 fp16 int8 mode.

Eval Result

model GPU mode APtrt/APpaper APtrt50 APtrt75 APtrtS APtrtM APtrtL
ctdet_coco_dla_2x gtx 1070 float32 0.365/0.374 0.543 0.390 0.164 0.398 0.536
ctdet_coco_dlav0_1x gtx 1070 float32 0.324/-- 0.511 0.343 0.140 0.350 0.476
ctdet_coco_dlav0_1x gtx 1070 int8 0.295/-- 0.468 0.311 0.123 0.318 0.446
ctdet_coco_resdcn101 gtx 1070 float32 0.332/0.346 0.516 0.349 0.115 0.367 0.531
ctdet_coco_resdcn18 gtx 1070 float32 0.277/0.281 0.448 0.286 0.083 0.290 0.454
ctdet_coco_resdcn18 gtx 1070 int8 0.242/0.281 0.401 0.250 0.061 0.255 0.409

notes

  • cocoval2017 test AP with no augmentation.
  • input_szie = 512x512
  • thresh = 0.01
  • maxpool kernel_size = 3
  • calib_img_list.txt : random sample 700 images from COCO2017/val2017

Enviroments

  1. dGPU
pytorch 1.3
ubuntu 1804
TensorRT 7.1
cuda 10.2
  1. jetson TX2
jetpack 4.4

Models

  1. Convert CenterNet model to onnx. See here for details.
  2. Use netron to observe whether the output of the converted onnx model is (hm, reg, wh)

Example

cd TensorRT-CenterNet
mkdir build
cd build && cmake .. && make
cd ..

##ctdet | config include/ctdetConfig.h 
## float32
./buildEngine -i model/ctdet_coco_dla_2x.onnx -o model/ctdet_coco_dla_2x.engine 
./runDet -e model/ctdet_coco_dla_2x.engine -i test.jpg -c test.h264

##cthelmet   | config include/ctdetConfig.h
## flaot32
./buildEngine -i model/ctdet_helmet.onnx -o model/ctdet_helmet.engine -m 0
./runDet -e model/ctdet_helmet.engine -i test.jpg -c test.h264
## int8
./buildEngine -i model/ctdet_helmet.onnx -o model/ctdet_helmet.engine -m 2 -c calib_img_list.txt
./runDet -e model/ctdet_helmet.engine -i test.jpg -c test.h264

## run eval_coco.py | conifg your cocodaset and ctdet_coco engine 
python3 eval_coco.py model/ctdet_coco_dla_2x.engine

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