a85675c16f
Note that this PR is a modified version of the withdrawn PR https://github.com/open-mmlab/mmsegmentation/pull/1748 ## Motivation In the last years, panoptic segmentation has become more into the focus in reseach. Weber et al. [[Link]](http://www.cvlibs.net/publications/Weber2021NEURIPSDATA.pdf) have published a quite nice dataset, which is in the same style like Cityscapes, but for KITTI sequences. Since Cityscapes and KITTI-STEP share the same classes and also a comparable domain (dashcam view), interesting investigations, e.g. about relations in the domain e.t.c. can be done. Note that KITTI-STEP provices panoptic segmentation annotations which are out of scope for mmsegmentation. ## Modification Mostly, I added the new dataset and dataset preparation file. To simplify the first usage of the new dataset, I also added configs for the dataset, segformer and deeplabv3plus. ## BC-breaking (Optional) No BC-breaking ## Use cases (Optional) Researchers want to test their new methods, e.g. for interpretable AI in the context of semantic segmentation. They want to show, that their method is reproducible on comparable datasets. Thus, they can compare Cityscapes and KITTI-STEP. --------- Co-authored-by: CSH <40987381+csatsurnh@users.noreply.github.com> Co-authored-by: csatsurnh <cshan1995@126.com> Co-authored-by: 谢昕辰 <xiexinch@outlook.com>
Projects
Implementing new models and features into OpenMMLab's algorithm libraries could be troublesome due to the rigorous requirements on code quality, which could hinder the fast iteration of SOTA models and might discourage our members from sharing their latest outcomes here.
And that's why we have this Projects/ folder now, where some experimental features, frameworks and models are placed, only needed to satisfy the minimum requirement on the code quality, and can be used as standalone libraries. Users are welcome to use them if they use MMSegmentation from source.
Everyone is welcome to post their implementation of any great ideas in this folder! If you wish to start your own project, please go through the example project for the best practice.
Note: The core maintainers of MMSegmentation only ensure the results are reproducible and the code quality meets its claim at the time each project was submitted, but they may not be responsible for future maintenance. The original authors take responsibility for maintaining their own projects.