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A simple baseline for the 2022 IEEE GRSS Data Fusion Contest (DFC2022)


A simple baseline for the 2022 IEEE GRSS Data Fusion Contest (DFC2022)

This repository uses TorchGeo, PyTorch Lightning, and Segmentation Models PyTorch to train a U-Net to perform semantic segmentation on the DFC2022 dataset. Masks for the holdout set are then predicted and zipped to be submitted. Note that the the baseline is only trained on the small labeled train set containing imagery from the Nice and Nantes Saint-Nazaire regions.

Install packages

pip install -r requirements.txt

Dataset

The dataset can be downloaded at the DFC2022 IEEE DataPort page and unzipped into a root folder. In our case this is data/.

Train

python train.py --config_file conf/dfc2022.yaml

Predict

python predict.py --log_dir checkpoints/version_0/ --predict_on val --output_directory outputs --device cuda
cd outputs && zip -r submission.zip ./

Submit

Upload submission.zip to the evaluation server here. This baseline results in a mIoU of 0.1278 on the heldout validation set and as of 1/12/22 is 3rd place on the leaderboard.

Checkpoints

Checkpoints can be downloaded from the following link

GitHub

View Github


Is it a security concern to allow a client to generate a CSRF token at login time, usable by the client upon the next request?

clang-tidy fails with OpenCV code that compiles fine