Nanodet: YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. YOLOX is a high-performing object detector, an improvement to the existing YOLO series. YOLO series are in constant exploration of techniques to improve the object detection techniques for optimal speed and accuracy trade-off for real-time applications.
1# detect on camera input
2python demo.py
3# detect on an image
4python demo.py --input /path/to/image -v
1# A typical and default installation path of OpenCV is /usr/local
2cmake -B build -D OPENCV_INSTALLATION_PATH=/path/to/opencv/installation .
3cmake --build build
4
5# detect on camera input
6./build/opencv_zoo_object_detection_yolox
7# detect on an image
8./build/opencv_zoo_object_detection_yolox -m=/path/to/model -i=/path/to/image -v
9# get help messages
10./build/opencv_zoo_object_detection_yolox -h
The model is evaluated on
COCO 2017 val. Results are showed below:
All files in this directory are licensed under
Apache 2.0 License.
