---
title: GTA5
canonical_url: "https://www.modelscope.cn/datasets/OmniData/GTA5"
md_url: "https://www.modelscope.cn/datasets/OmniData/GTA5.md"
repository: OmniData/GTA5
last_updated: 2024-07-17
license: "[Unknown]"
storage_size: "57 GB"
domain:
  - publishDate
  - taskTypes
  - displayName
  - paperUrl
  - publishUrl
  - mediaTypes
  - publisher
tasks:
  - 2016
  - "Domain adaptation"
  - "GTA5(Grand Theft Auto 5)"
  - "https://arxiv.org/pdf/1608.02192v1.pdf"
  - "https://github.com/sarrrrry/PyTorchDL_GTA5"
  - Image
  - "Intel Labs"
downloads: 1046
stars: 0
---

# GTA5

> GTA5 - OmniData 在 ModelScope 开源的数据集。displayName: GTA5(Grand Theft Auto 5) labelTypes: [] license: Unknown mediaTypes: Image paperUrl: https://arxiv.org/pdf/1608.02192v1.pdf publishDate: "2016" publishUrl: https://github.com/sarrrrry/PyTorchDLGTA5 publisher:…

OmniData/GTA5 是 ModelScope 魔搭社区上的2016、Domain adaptation、GTA5(Grand Theft Auto 5)数据集，涉及 publishDate、taskTypes、displayName 领域，存储大小 57 GB，采用 [Unknown] 许可。

- **Repository**: OmniData/GTA5
- **License**: [Unknown]
- **Tasks**: 2016, Domain adaptation, GTA5(Grand Theft Auto 5), https://arxiv.org/pdf/1608.02192v1.pdf, https://github.com/sarrrrry/PyTorchDL_GTA5, Image, Intel Labs
- **Domain**: publishDate, taskTypes, displayName, paperUrl, publishUrl, mediaTypes, publisher
- **Storage size**: 57 GB
- **Downloads**: 1046
- **Stars**: 0
- **Last updated**: 2024-07-17

Source: https://www.modelscope.cn/datasets/OmniData/GTA5

---

displayName: GTA5(Grand Theft Auto 5)
labelTypes: []
license:
- Unknown
mediaTypes:
- Image
paperUrl: https://arxiv.org/pdf/1608.02192v1.pdf
publishDate: "2016"
publishUrl: https://github.com/sarrrrry/PyTorchDL_GTA5
publisher:
- Technical University of Darmstadt
- Intel Labs
tags:
- Image pixel
taskTypes:
- Semantic Segmentation
- Unsupervised Domain Adaptation
- Image To Image Translation
- Synthetic To Real Translation
- One Shot Unsupervised Domain Adaptation
- Domain adaptation

---
# 数据集介绍
  ## 简介
  我们从gta5中提取了24,966帧。每帧具有1914年 × 1052像素的分辨率。然后使用第3.2节中描述的接口对帧进行语义标记。标签过程在49小时内完成。此时，提取的图像的像素区域的98.3% 被用相应的语义类标记。类被定义为与户外场景的其他语义分割数据集兼容 [8,11，39,50]。我们数据集中类的分布如图4所示。
  ## 引文
  ```
@inproceedings{richter2016playing,
  title={Playing for data: Ground truth from computer games},
  author={Richter, Stephan R and Vineet, Vibhav and Roth, Stefan and Koltun, Vladlen},
  booktitle={European conference on computer vision},
  pages={102--118},
  year={2016},
  organization={Springer}
}
```
  
## Download dataset
:modelscope-code[]{type="git"}