## Motivation
Add a model deployment example.
## Modification
Add an inference script and update the README.
## BC-breaking (Optional)
None
## Use cases (Optional)
In README.
Thanks for your contribution and we appreciate it a lot. The following
instructions would make your pull request more healthy and more easily
get feedback. If you do not understand some items, don't worry, just
make the pull request and seek help from maintainers.
## Motivation
Support CAT-Seg open-vocabulary semantic segmentation (CVPR2023).
## Modification
Support CAT-Seg open-vocabulary semantic segmentation (CVPR2023).
- [x] Support CAT-Seg model training.
- [x] CLIP model based `backbone` (R101 & Swin-B), aggregation layers
based `neck`, and `decoder` head.
- [x] Provide customized coco-stuff164k_384x384 training configs.
- [x] Language model supports for `open vocabulary` (OV) tasks.
- [x] Support CLIP-based pretrained language model (LM) inference.
- [x] Add commonly used prompts templates.
- [x] Add README tutorials.
- [x] Add zero-shot testing scripts.
**Working on the following tasks.**
- [x] Add unit test.
## BC-breaking (Optional)
Does the modification introduce changes that break the
backward-compatibility of the downstream repos?
If so, please describe how it breaks the compatibility and how the
downstream projects should modify their code to keep compatibility with
this PR.
## Use cases (Optional)
If this PR introduces a new feature, it is better to list some use cases
here, and update the documentation.
## Checklist
1. Pre-commit or other linting tools are used to fix the potential lint
issues.
2. The modification is covered by complete unit tests. If not, please
add more unit test to ensure the correctness.
3. If the modification has potential influence on downstream projects,
this PR should be tested with downstream projects, like MMDet or
MMDet3D.
4. The documentation has been modified accordingly, like docstring or
example tutorials.
---------
Co-authored-by: xiexinch <xiexinch@outlook.com>
## Motivation
The original version of Visual Attention Network (VAN) can be found from
https://github.com/Visual-Attention-Network/VAN-Segmentation
添加Visual Attention Network (VAN)的支持。
## Modification
added a floder mmsegmentation/projects/van/
added 13 configs totally and aligned performance basically.
只增加了一个文件夹，共增加13个配置文件，基本对齐性能（没有全部跑）。
## Use cases (Optional)
Before running, you may need to download the pretrain model from
https://cloud.tsinghua.edu.cn/d/0100f0cea37d41ba8d08/
and then move them to the folder mmsegmentation/pretrained/, i.e.
"mmsegmentation/pretrained/van_b2.pth".
After that, run the following command:
cd mmsegmentation
bash tools/dist_train.sh
projects/van/configs/van/van-b2_pre1k_upernet_4xb2-160k_ade20k-512x512.py
4
---------
Co-authored-by: xiexinch <xiexinch@outlook.com>
## Motivation
Make projects contribution more clear
## Modification
1. Add description on project/README
2. Modify comments to reference in example_project/README
3. Add faq for projects
## BC-breaking (Optional)
No
## Motivation
Supplementary PR #2444
Fix tiny bug and add loss_by_feat() to compute loss to train.
The inference process have verified to be accurate.
## Modification
- modify `sep_aspp_contrast_head.py` , add `loss_by_feat()` function to
train(training still has bug, will fix in future😫)
- fix testing commands path error `bash tools/dist_test.sh
projects/HieraSeg_project/` to `bash tools/dist_test.sh
projects/HieraSeg/` at README.md
## Support `Mapillary Vistas Dataset`
## Motivation
Support **`Mapillary Vistas Dataset`**
Dataset Paper link : https://ieeexplore.ieee.org/document/9878466/
Download and more information view
https://www.mapillary.com/dataset/vistas
```
@InProceedings{Neuhold_2017_ICCV,
author = {Neuhold, Gerhard and Ollmann, Tobias and Rota Bulo, Samuel and Kontschieder, Peter},
title = {The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}
```
## Modification
Add `Mapillary_dataset` in `mmsegmentation/projects`
Add `configs/_base_/mapillary_v1_2.py` and
`configs/_base_/mapillary_v2_0.py`
Add `configs/deeplabv3plus_r18-d8_4xb2-80k_mapillay-512x1024.py` to test
training and testing on Mapillary datasets
Add `docs/en/user_guides/2_dataset_prepare.md` , add Mapillary Vistas
Dataset Preparing and Structure.
Add `tools/dataset_converters/mapillary.py` to convert RGB labels to
Mask labels.
Co-authored-by: 谢昕辰 <xiexinch@outlook.com>
Yang-ChangHui
谢昕辰
Xu CAO
CastleDream
masaaki
legendchilli
tianbin li
Sizheng Guo
Tianlong Ai
tang576225574
CSH
Junhwa Song
Miao Zheng
unrealMJ