---
license: cc-by-4.0
task_categories:
- image-classification
- image-to-video
language:
- en
tags:
- self-supervised learning
- representation learning
pretty_name: Walking_Tours
size_categories:
- n<1K
---

<p align="center"style="font-size:32px;">
  <strong>Walking Tours Dataset</strong>
</p>
<p align="center">
<img src="gifs/Wt_img.jpg" alt="Alt Text" width="80%" />
</p>



## Overview

The Walking Tours dataset is a unique collection of long-duration egocentric videos captured in urban environments from cities in Europe and Asia. It consists of 10 high-resolution videos, each showcasing a person walking through a different environment, ranging from city centers to parks to residential areas, under different lighting conditions. A video from a Wildlife safari is also included to diversify the dataset with natural environments. The dataset is completely unlabeled and uncurated, making it suitable for self-supervised pretraining.

## Cities Covered

The dataset encompasses walks through the following cities:

- Amsterdam
- Bangkok
- Chiang Mai
- Istanbul
- Kuala Lumpur
- Singapore
- Stockholm
- Venice
- Zurich



## Video Specifications

- **Resolution:** 4K (3840 × 2160 pixels)
- **Frame Rate:** 60 frames-per-second
- **License:** Creative Commons License (CC-BY)

## Duration

The videos vary in duration, offering a diverse range of content:

- Minimum Duration: 59 minutes (Wildlife safari)
- Maximum Duration: 2 hours 55 minutes (Bangkok)
- Average Duration: 1 hour 38 minutes


## Download the Dataset

The complete list of WTour videos are available in ```WTour.txt```, comprising the YouTube link and the corresponding city.  

To download the dataset, we first install **pytube**
```
pip install pytube
```

then, we run 
```
python download_WTours.py --output_folder <path_to_folder> 
```

In order to comply with [GDPR](https://gdpr.eu/what-is-gdpr/), we also try to blur out all faces and license plates appearing in the video using [Deface](https://github.com/ORB-HD/deface)  

To do this for all videos in WTour dataset:
```
python3 -m pip install deface
```
Then run Deface on all videos using the bash script:
```
chmod a+x gdpr_blur_faces.sh  
./gdpr_blur_faces.sh
```


## Citation

If you find this work useful and use it on your own research, please cite our paper:

```
@inproceedings{venkataramanan2023imagenet,  
  title={Is ImageNet worth 1 video? Learning strong image encoders from 1 long unlabelled video},  
  author={Venkataramanan, Shashanka and Rizve, Mamshad Nayeem and Carreira, Jo{\~a}o and Asano, Yuki M and Avrithis, Yannis},  
  booktitle={International Conference on Learning Representations},  
  year={2024}  
}
```
---