Arbitrary oriented object detection implemented with yolov3 (attached with some tricks).
Rotaion object detection implemented with yolov3.
Hello, the no-program ryolov3 is available now. Although not so many tricks are attached like this repo, it still achieves good results, and is friendly for beginners to learn, have a good luck.
The latest code has been uploaded, unfortunately, due to my negligence, I incorrectly modified some parts of the code and did not save the historical version last year, which made it hard to reproduce the previous high performance. It is tentatively that there are some problems in the loss calculation part.
But I found from the experimental results left last year that yolov3 is suitable for rotation detection. After using several tricks (attention, ORN, Mish, and etc.), it have achieved good performance. More previous experiment results can be found here.
The detection results from rotated yolov3 left over last year:
Following questions are frequently mentioned. And if you have something unclear, don't doubt and contact me via opening issues.
icdar_608_care.txtsets the initial anchors generated via kmeans, you need to run
kmeans.pyrefer to my implemention here. You can also check
utils/parse_config.pyfor more details.
A: This ryolo implemention is based on this repo, training and evaluation pipeline are the same as that one do.
A: I'll release the whole codebase as I return school, and this repo may help.
There is no need or time to maintain the codebase to reproduce the previous performance. If you are interested in this work, you are welcome to fix the bugs in this codebase, and the trained models are available here with extracted code
5noq. I'll reimplement the rotation yolov4 or yolov5 if time permitting in the future.