This is the Preferred Contrail's solution for Google Research - Identify Contrails to Reduce Global Warming.
Please prepare the competition dataset under input/.
The directory can also be changed by --in_base_dir argument of train.py.
Train 2.5D resnest269e:
python train.py --config_path config/resnest-2-5d.yaml --save_model --out_base_dir result/resnest
By this, model.pt is saved under result/resnest/-1/.
You can use different config files under config/ to train different models.
Make submission by previously trained resnest269e:
python make_submission.py --model_dirs result/resnest/-1
By this, you can output submission.csv.
You can also ensemble multiple models' predictions by giving space-separated directories to --model_dirs.
Make psuedo labels by previously trained resnest269e:
python make_pl.py --model_dirs result/resnest/-1
By this script, you can generate pseudo label under pseudo-label/ (which can be changed by --out_dir option) for frames 2-7.
You can also ensemble multiple models' predictions by giving space-separated directories to --model_dirs.
We uploaded the pseudo labels that we created by several models as a Kaggle dataset (link).
By specifying pseudo_dir (defaults to None) to psudo-label in config files, you can use the pseudo labels to pretrain models by train.py.
After that, you can finetune the model on the original data by setting pretrained_model_path to the pretrained model's directory.
The best single model achieved 0.706/0.71770/0.71629 (validation/private/public), which could still win 3rd place. I uploaded this model as a Kaggle dataset (link).
By ensembling 18 models with different backbones and settings (the best configurations are under config/), I achieved 0.72233 of private LB.
As a final submission, we achieved a score of 0.72305 by ensembling team members' models with the ratio of knshnb:yiemon:charmq = 0.80:0.15:0.05.
Models were trained mainly on 2x or 4x NVIDIA A100 (80GB).
- For an overview of our key ideas and detailed explanation, please also refer to 3rd Place Solution: 2.5D U-Net Kaggle discussion.
- yiemon's repository
- charmq's repository