Welcome to AI Blitz X! 🚀

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Introduction

High-altitude Unmanned Aerial Vehicle is revolutionary technology! The images from high-altitude UAVs can help detect fires, humans, and wildlife like mountain terrains and thick forests. The images also help in improving understanding of hard-to-reach geographical areas. 

One of the biggest challenges in using high-altitude UAV images in the presence of clouds! One cannot always wait for clear skies to collect data and these clouds act as a major obstacle in getting clear images. 

Your final task for Blitz X is to remove clouds from high-altitude UAV images. Check out the starter kit to make your first submission!

💪 Getting Started

So in this challenge, the task is to remove the clouds from a high-altitude UAV Imagery.

Use our Getting Started Notebook available here. 

💾 Dataset

In this dataset, for a given data file, such as train.zip. There will be two groups of videos, clear and cloud. The number after the video name will represent the video id.   Sample data format  - 

train
├── clear_0.mp4
├── cloud_0.mp4
├── clear_1.mp4
├── cloud_1.mp4
└── ...

Sample Videos - 

Note - 

• Make sure the videos are in .mp4 format
• Make sure that there is a total of 24 frames in the video.  

📁 Files

Following files are available in the resources section:

• train.zip - [ 2000 samples ]  Used for Training. Include labels.
• partial_train.zip - [ 500 samples ] Subset of train.zip. Use for getting started! 
• test.zip  - [ 500 samples ] Used for evaluation. Does not include labels. 

🚀  Submission

• Creating a submission directory
• Create a clear folder inside the submission directory.  
• Inside a submission directory, put the .ipynb notebook from which you trained the model and made inference and save it as original_notebook.ipynb.
• Overall, this is what your submission directory should look like

     submission
     ├── clear
     │   ├── clear_0.mp4
     │   ├── clear_1.mp4
     │   ├── ...
     │   └── clear_499.mp4
     └── original_notebook.ipynb

• Zip the submission directory!

Make your first submission here 🚀 !!

🖊 Evaluation Criteria

During the evaluation, Mean Squared Error will be used to test the efficiency of the model.

🔗 Links

• 💪 Challenge Page: https://www.aicrowd.com/challenges/clouds-removal
• 🗣️ Discussion Forum: https://www.aicrowd.com/challenges/clouds-removal/discussion
• 🏆 Leaderboard: https://www.aicrowd.com/challenges/clouds-removal/leaderboards

📱 Contact

• Shubhamai