https://github.com/henghuiding/mose-api
[ICCV 2023] MOSE: A New Dataset for Video Object Segmentation in Complex Scenes
https://github.com/henghuiding/mose-api
benchmark complex-environment dataset iccv2023 video-object-segmentation video-segmentation
Last synced: 6 months ago
JSON representation
[ICCV 2023] MOSE: A New Dataset for Video Object Segmentation in Complex Scenes
- Host: GitHub
- URL: https://github.com/henghuiding/mose-api
- Owner: henghuiding
- Created: 2023-02-05T16:30:27.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2023-11-23T13:26:23.000Z (almost 2 years ago)
- Last Synced: 2025-03-30T11:09:45.466Z (6 months ago)
- Topics: benchmark, complex-environment, dataset, iccv2023, video-object-segmentation, video-segmentation
- Language: Python
- Homepage: https://henghuiding.github.io/MOSE/
- Size: 25.4 KB
- Stars: 355
- Watchers: 5
- Forks: 6
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# MOSE: A New Dataset for Video Object Segmentation in Complex Scenes
**[🏠[Homepage]](https://henghuiding.github.io/MOSE/)** **[📄[Arxiv]](https://arxiv.org/abs/2302.01872)**
This repository contains information and tools for the [MOSE](https://henghuiding.github.io/MOSE/) dataset.
## Download
***[🔥02.09.2023: Dataset has been released!]***
⬇️ Get the dataset from:
- ☁️ [***OneDrive*** ](https://entuedu-my.sharepoint.com/:f:/g/personal/liuc0058_e_ntu_edu_sg/EjXSfDF7QEZApAVpFJ5rfdABkHCf0k2Va6VDfUy7rpabNw?e=9BVkrz) ***(Recommended)***
- ☁️ [Google Drive](https://drive.google.com/drive/folders/1vChKHzbboP1k6wd6t95guxxURW3nIXBe?usp=sharing)
- ☁️ [Baidu Pan](https://pan.baidu.com/s/116p3tQsUqObem8G8FOJ7cA) (Access Code: MOSE)
📦 Or use [gdown](https://github.com/wkentaro/gdown):
```bash
# train.tar.gz
gdown 'https://drive.google.com/uc?id=ID_removed_to_avoid_overaccesses_get_it_by_yourself'
# valid.tar.gz
gdown 'https://drive.google.com/uc?id=ID_removed_to_avoid_overaccesses_get_it_by_yourself'
# test set will be released when competition starts.
```
Please also check the SHA256 sum of the files to ensure the data intergrity:
```
3f805e66ecb576fdd37a1ab2b06b08a428edd71994920443f70d09537918270b train.tar.gz
884baecf7d7e85cd35486e45d6c474dc34352a227ac75c49f6d5e4afb61b331c valid.tar.gz
```
## Evaluation
***[🔥02.16.2023: Our [CodaLab competition](https://codalab.lisn.upsaclay.fr/competitions/10703) is on live now!]***
Please submit your results on
- 💯 [**CodaLab**](https://codalab.lisn.upsaclay.fr/competitions/10703).
## File Structure
The dataset follows a similar structure as [DAVIS](https://davischallenge.org/) and [Youtube-VOS](https://youtube-vos.org/). The dataset consists of two parts: `JPEGImages` which holds the frame images, and `Annotations` which contains the corresponding segmentation masks. The frame images are numbered using five-digit numbers. Annotations are saved in color-pattlate mode PNGs like DAVIS.
Please note that while annotations for all frames in the training set are provided, annotations for the validation set will only include the first frame.
```
│
├── Annotations
│ │
│ ├──
│ │ ├── 00000.png
│ │ ├── 00001.png
│ │ └── ...
│ │
│ ├──
│ │ ├── 00000.png
│ │ ├── 00001.png
│ │ └── ...
│ │
│ ├──
│
└── JPEGImages
│
├──
│ ├── 00000.jpg
│ ├── 00001.jpg
│ └── ...
│
├──
│ ├── 00000.jpg
│ ├── 00001.jpg
│ └── ...
│
└──
```
## BibTeX
Please consider to cite MOSE if it helps your research.
```latex
@inproceedings{MOSE,
title={{MOSE}: A New Dataset for Video Object Segmentation in Complex Scenes},
author={Ding, Henghui and Liu, Chang and He, Shuting and Jiang, Xudong and Torr, Philip HS and Bai, Song},
booktitle={ICCV},
year={2023}
}
```
## License
MOSE is licensed under a [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) License. The data of MOSE is released for non-commercial research purpose only.