Update README.md

README.md

@@ -1,3 +1,91 @@
 ---
 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
 ---
+# Walking Tours Dataset
+
+## Overview
+
+We introduce the Walking Tours dataset (WTours), a unique collection of long-range egocentric videos captured in an urban setting from various cities in Europe and Asia. It consists of 10 high-resolution videos, each showcasing a person walking through different urban environments, ranging from city centers to parks to residential areas under different lighting conditions. Additionally, a video from a Wildlife safari is included to diversify the dataset.
+
+## Cities Covered
+
+The dataset encompasses walks through the following cities:
+
+- Amsterdam
+- Bangkok
+- Chiang Mai
+- Istanbul
+- Kuala Lumpur
+- Singapore
+- Stockholm
+- Venice
+- Zurich
+
+![](path/to/Example_Gif_1.gif) ![](path/to/Example_Gif_1.gif) ![](path/to/Example_Gif_1.gif)
+
+## 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
+
+
+## Usage
+
+The complete list of WTour videos are available in ```WTour.txt``` with 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} 
+}
+```
+---