YOLOv6-openi/deploy/ONNX/OpenCV

• Object Detection using YOLOv5/YOLOv6/YOLOX and OpenCV DNN (Python/C++)
• • 0. Install Dependancies
  • 1. Usage
  • • 1.1 Python
    • 1.2 CMake C++ Linux YOLOv5
    • 1.3 CMake C++ Linux YOLOv6
    • 1.4 CMake C++ Linux YOLOX
  • 2. Result
  • • Visualization

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