Hangyu Zhou, Chia-Hsiang Kao, Cheng Perng Phoo, Utkarsh Mall, Bharath Hariharan, Kavita Bala

AllClear is a comprehensive dataset/benchmark for cloud detection and removal.

Notice: We are actively cleaning up the codebase and uploading our dataset for public access. Stay tuned!

Please navigate to the root directory of this project and run the following commands:

# Clone the repository
git clone https://github.com/Zhou-Hangyu/allclear.git

# Obtain the submodules
cd allclear
git submodule update --init --recursive

# Download the test dataset.zip and metadata json file.
. preprocess.sh

This section provides instructions on how to use the benchmark with the UnCRtainTS model as an example.

1. First, set up the environment for UnCRtainTS. Visit the UnCRtainTS GitHub page and follow the instructions there to create their conda environment.

2. After setting up the UnCRtainTS environment, navigate to the root directory of this project and install our package using pip:

   pip install -e .

3. To run the benchmark and see some results, execute the run_benchmark.sh script located in the demos directory:

   # Run the Least Cloudy baseline
   bash demos/run_benchmark_leastcloud.sh 
   
   # Run the pretrained UnCRtainTS
   bash demos/run_uncrtaints_pretrained.sh 
   
   # Run the UnCRtainTS pretrained on our full allclear dataset 
   bash demos/run_uncrtaints_allclear100pc.sh 

This project is licensed under the MIT License.

• The main package folder is allclear. Should only contain reusable code directly related to the use of the dataset and benchmark.
• Every baseline we proposed or reproduced should have one folder in the /baselines folder.
  • They will have a wrapper in allclear/baselines.py with uniform input/output format for easy comparison.
• The demo folder contains minimal code to demonstrate the use of the dataset and benchmark.
• For all other code, please put them in the /experimental_scripts folder for now.