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Object Detection using YOLOv5/YOLOv6/YOLOX and OpenCV DNN (Python/C++)

0. Install Dependancies

OpenCV >= 4.5.4

Only OpenCV >= 4.5.4 can read onnx model file by dnn module.

1. Usage

Change work directory to /path/to/YOLOv6/deploy/ONNX/OpenCV

1.1 Python

  • YOLOv5 & YOLOv6:
python yolo.py --model /path/to/onnx/yolov5n.onnx --img /path/to/sample.jpg --classesFile /path/to/coco.names
                                     yolov5s.onnx
                                     yolov5m.onnx
                                     yolov6n.onnx
                                     yolov6s.onnx
                                     yolov6t.onnx
  • YOLOX:
python yolox.py --model /path/to/onnx/yolox_nano.onnx --img /path/to/sample.jpg --classesFile /path/to/coco.names
                                      yolox_tiny.onnx
                                      yolox_s.onnx
                                      yolox_m.onnx

1.2 CMake C++ Linux YOLOv5

cd yolov5    // modify CMakeLists.txt
mkdir build
cd build
cmake ..
make
./yolov5 /path/to/onnx/yolov5n.onnx /path/to/sample.jpg /path/to/coco.names
                       yolov5s.onnx
                       yolov5m.onnx

1.3 CMake C++ Linux YOLOv6

cd yolov6    // modify CMakeLists.txt
mkdir build
cd build
cmake ..
make
./yolov6 /path/to/onnx/yolov6n.onnx /path/to/sample.jpg /path/to/coco.names
                       yolov6t.onnx
                       yolov6s.onnx

1.4 CMake C++ Linux YOLOX

cd yolox    // modify CMakeLists.txt
mkdir build
cd build
cmake ..
make
./yolox /path/to/onnx/yolox_nano.onnx /path/to/sample.jpg /path/to/coco.names
                      yolox_tiny.onnx
                      yolox_s.onnx
                      yolox_m.onnx

2. Result

Model Speed CPU b1(ms) Python Speed CPU b1(ms) C++ mAPval 0.5:0.95 params(M) FLOPs(G)
YOLOv5n 116.47 118.89 28.0 1.9 4.5
YOLOv5s 200.53 202.22 37.4 7.2 16.5
YOLOv5m 294.98 291.86 45.4 21.2 49.0
YOLOv6-n 66.88 69.96 35.0 4.3 4.7
YOLOv6-tiny 133.15 137.59 41.3 15.0 36.7
YOLOv6-s 164.44 163.38 43.1 17.2 44.2
YOLOX-Nano 81.06 86.75 25.8@416 0.91 1.08@416
YOLOX-tiny 129.72 144.19 32.8@416 5.06 6.45@416
YOLOX-s 180.86 169.96 40.5 9.0 26.8
YOLOX-m 336.34 357.91 47.2 25.3 73.8

Note:

  • All onnx models are converted from official github(Google Drive).
  • Speed is test by dnn::Net::getPerfProfile, we report the average inference time of 300 runs on the same environment.
  • The mAP/params/FLOPs are from official github.
  • Test environment: MacOS 11.4 with 2.6 GHz 6-core Intel Core i7, 16GB Memory.

Visualization