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Tianlong Ai e394e2aa28
CodeCamp #1555[Feature] Support Mapillary Vistas Dataset (#2484) 
## 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>
2023-01-20 14:25:51 +08:00
..
example_project
[Projects] Add 'Projects/' folder, and the first example project (#2412)
2022-12-30 22:52:07 +08:00
HieraSeg
CodeCamp #151[Feature] Support HieraSeg on cityscapes (#2444)
2023-01-12 17:12:40 +08:00
isnet
[Doc] Add checklist of ISNet (#2460)
2023-01-09 13:09:23 +08:00
mapillary_dataset
CodeCamp #1555[Feature] Support Mapillary Vistas Dataset (#2484)
2023-01-20 14:25:51 +08:00
README.md
[Projects] Add 'Projects/' folder, and the first example project (#2412)
2022-12-30 22:52:07 +08:00

README.md

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.