I'll release latest code and training pipeline as I return school at request, cause codes are left there, this repo is just a backup commit.
Rotating target detection implemented with yolov3.
Not good enough yet, reach only Hmean 70 on ICDAR15 dataset.
I'll not keep updating here, but PRs are welcomed. Better detector for rotation object detection will be published in my repo as soon as possible(that's why I deprecated ryolo).
- CUDA RNMS
- riou loss
- Inception module
Feel free to contact me if you have any question when use this code, cause maybe I don't know either.(too long the last time I make modification on it, and I don't think yolo is a good choice for arbitrary orientation object detection.)
I'll release a stronger detector later.
Following questions are frequently mentioned. And if you have something unclear, don't doubt and contact me via opening issues.
Q: How can I obtain
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.
Q: How to train the model on my own dataset?
A: This ryolo implemention is based on this repo, training and evaluation pipeline are the same as that one do.
Q: Where is ORN codes?
A: I'll release the whole codebase as I return school, and this repo may help.