mmsegmentation/requirements/docs.txt

docutils==0.16.0
myst-parser
-e git+https://github.com/gaotongxiao/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme
sphinx==4.0.2
sphinx_copybutton
sphinx_markdown_tables
urllib3<2.0.0
[Docs] Improve docs style (#879) * Improve docs style * update lists * update the size of image * modify duplicate mmdet3d 2021-09-16 23:23:50 +08:00			`docutils==0.16.0`
			`myst-parser`
			`-e git+https://github.com/gaotongxiao/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme`
Fix sphinx version (#827) 2021-09-02 09:45:53 +08:00			`sphinx==4.0.2`
[Docs] Improve docs style (#879) * Improve docs style * update lists * update the size of image * modify duplicate mmdet3d 2021-09-16 23:23:50 +08:00			`sphinx_copybutton`
Add Colab Tutorial (#7) * add badge * Created using Colaboratory * add read docs * Fixed readthedocs * fixed colab ref * add readthedocs.txt * add link * fixed modelzoo link * add missing reference * fixed docs * remove relative path in docs * add colab in README.md * update docker image * add newline * fixed br 2020-07-10 16:55:47 +08:00			`sphinx_markdown_tables`
Created KITTI dataset for segmentation in autonomous driving scenario (#2730) Note that this PR is a modified version of the withdrawn PR https://github.com/open-mmlab/mmsegmentation/pull/1748 ## Motivation In the last years, panoptic segmentation has become more into the focus in reseach.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> 2023-05-09 12:08:31 +02:00			`urllib3<2.0.0`