https://github.com/developmentseed/chabud2023
Change detection for Burned area Delineation (ChaBuD) ECML/PKDD 2023 challenge
https://github.com/developmentseed/chabud2023
change-detection ecml-pkdd ecmlpkdd lightning ml-competition remote-sensing sentinel-2 torchdata
Last synced: 3 months ago
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Change detection for Burned area Delineation (ChaBuD) ECML/PKDD 2023 challenge
- Host: GitHub
- URL: https://github.com/developmentseed/chabud2023
- Owner: developmentseed
- License: other
- Created: 2023-05-09T21:00:16.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-06-14T06:01:49.000Z (over 2 years ago)
- Last Synced: 2024-04-10T03:11:33.120Z (over 1 year ago)
- Topics: change-detection, ecml-pkdd, ecmlpkdd, lightning, ml-competition, remote-sensing, sentinel-2, torchdata
- Language: Python
- Homepage:
- Size: 198 KB
- Stars: 5
- Watchers: 5
- Forks: 1
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# ChaBuD2023
A Machine Learning data pipeline for the
[Change detection for Burned area Delineation (ChaBuD)](https://huggingface.co/spaces/competitions/ChaBuD-ECML-PKDD2023)
challenge at the [ECML/PKDD 2023](https://2023.ecmlpkdd.org/submissions/discovery-challenge/challenges)
conference.
[](https://wandb.ai/devseed/chabud2023)
# Getting started
### Quickstart
Launch into a [JupyterLab](https://jupyterlab.readthedocs.io) environment on
| [Binder](https://mybinder.readthedocs.io/en/latest) | [SageMaker Studio Lab](https://studiolab.sagemaker.aws) | [Planetary Computer](https://planetarycomputer.microsoft.com) |
|:--:|:--:|:--:|
| [](https://mybinder.org/v2/gh/developmentseed/chabud2023/main) | [](https://studiolab.sagemaker.aws/import/github/developmentseed/chabud2023/blob/main/train_chabud.ipynb) | [](https://pccompute.westeurope.cloudapp.azure.com/compute/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fdevelopmentseed%2Fchabud2023&urlpath=lab%2Ftree%2Fchabud2023%2Ftrain_chabud.ipynb&branch=main) |
## Installation
### Basic
To help out with development, start by cloning this [repo-url](/../../)
git clone
Then I recommend [using mamba](https://mamba.readthedocs.io/en/latest/installation.html)
to install the dependencies.
A virtual environment will also be created with Python and
[JupyterLab](https://github.com/jupyterlab/jupyterlab) installed.
cd chabud2023
mamba env create --file environment.yml
Activate the virtual environment first.
mamba activate chabud
Finally, double-check that the libraries have been installed.
mamba list
### Advanced
This is for those who want full reproducibility of the virtual environment.
Create a virtual environment with just Python and conda-lock installed first.
mamba create --name chabud python=3.11 conda-lock=2.0.0
mamba activate chabud
Generate a unified [`conda-lock.yml`](https://github.com/conda/conda-lock) file
based on the dependency specification in `environment.yml`. Use only when
creating a new `conda-lock.yml` file or refreshing an existing one.
conda-lock lock --mamba --file environment.yml
Installing/Updating a virtual environment from a lockile. Use this to sync your
dependencies to the exact versions in the `conda-lock.yml` file.
conda-lock install --mamba --name chabud conda-lock.yml
See also https://conda.github.io/conda-lock/output/#unified-lockfile for more
usage details.
## Usage
### Running jupyter lab
mamba activate chabud
python -m ipykernel install --user --name chabud # to install virtual env properly
jupyter kernelspec list --json # see if kernel is installed
jupyter lab &
### Running the model
The neural network model can be ran via
[LightningCLI v2](https://pytorch-lightning.medium.com/introducing-lightningcli-v2-supercharge-your-training-c070d43c7dd6).
To check out the different options available, and look at the hyperparameter
configurations, run:
python trainer.py --help
python trainer.py test --print_config
To quickly test the model on one batch in the validation set:
python trainer.py validate --trainer.fast_dev_run=True
To train the model for a hundred epochs and log metrics to
[WandB](https://wandb.ai/devseed/chabud2023):
python trainer.py fit --trainer.max_epochs=100 \
--trainer.logger=WandbLogger \
--trainer.logger.entity=devseed \
--trainer.logger.project=chabud2023
To generate the CSV file of predicted masks on the validation set for
[submission](https://huggingface.co/datasets/chabud-team/chabud-ecml-pkdd2023/blob/main/create_sample_submission.py)
to https://huggingface.co/spaces/competitions/ChaBuD-ECML-PKDD2023.
python trainer.py test --model.submission_filepath=submission.csv
More options can be found using `python trainer.py fit --help`, or at the
[LightningCLI docs](https://lightning.ai/docs/pytorch/2.0.2/cli/lightning_cli.html).