First place solution for Crop Detection from Satellite Imagery competition organized by CV4A workshop at ICLR 2020.
A summarized description of the approach can be found here.
Firstly, you need to have
- Ubuntu 18.04
- 20 GB RAM
- 11 GB GPU RAM
Secondly, you need to install the challenge data and sample submission file by the following the instructions here.
Thirdly, you need to install the dependencies by running:
pip3 install -r requirements.txt
python3 prepare_data.py --data_path ...
This step generates patches around each crop field in the data and saves all of them in a numpy matrix along side their ground truth labels.
python3 main.py --data_path ...
This step trains an ensemble of 10 instances of the same DL model on different train/valid splits then generate a submission file with results on test set.
All augmentations are used except for Mixup augmentation. In order to use it, run
python3 main.py --data_path ... --mixup_augmentation True
However it uses a lot of RAM (~50 GB) so I wouldn't recommend using it.