AllClear
is a comprehensive dataset/benchmark for cloud detection and removal.
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.
-
First, set up the environment for
UnCRtainTS
. Visit the UnCRtainTS GitHub page and follow the instructions there to create their conda environment. -
After setting up the
UnCRtainTS
environment, navigate to the root directory of this project and install our package using pip:pip install -e .
-
To run the benchmark and see some results, execute the
run_benchmark.sh
script located in thedemos
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.
- They will have a wrapper in
- 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.