- pytorch 0.1.12, installed via pip or from source
- torchvision, installed from source
- torchsample, commit 00a41b05
pip install tensorboard_logger, tqdm- tensorflow >= 1.0 to use tensorboard (cpu version is enough)
- other necessary dependencies...
The folder ./data_utils contains codes to generate index files (all the idx files used for our models have been generated in ./data_utils) for train/val/test. Note that the images should be put following our folder structure:
# data_root
# ├── additional
# │ ├── Type_1
# │ ├── Type_2
# │ └── Type_3
# ├── test
# │ └── test
# └── train
# ├── Type_1
# ├── Type_2
# └── Type_3
We resize all the original images to 256x256, please use data_utils/resize_convert_all_multi.py and change the paths in it to generate the resized data.
We assume the resized_data root is ./data/resized_data/
Use ./scripts/train_10_fold.py and change the arch to resnet101 and inception_v3 to train two kinds of models, respectively.
In this step, we will get model checkpoints in 20 different sub-folders in ./ckpt.
-
10 crop test for each single model: use
./scripts/gen_test_10_fold_cmds.pyand change thearchtoresnet101andinception_v3, respectively. We will get 20 different submission files in./submission, each of which corresponds to a single model. -
ensemble scores of all models (average): put all the paths of the submission files in a file, for example:
ls submission/resnet101_model_epoch_15_fold_* > resnet101_epoch15_10_fold.txt
ls submission/inception_v3_model_epoch_15_fold_* > inception_v3_epoch15_10_fold.txt
cat resnet101_epoch15_10_fold.txt inception_v3_epoch15_10_fold.txt > inception_v3_resnet101_epoch15_10_folds.txt
python ensemble_scores.py inception_v3_resnet101_epoch15_10_folds.txt
The final submission file is like ./submission/ensemble_submission_inception_v3_resnet101_epoch15_10_folds__{date_time}.txt
(This scores 0.48662 on public Leaderboard, and 0.85024 on the private Leaderboard.)
-
10 crop test for each single model: use
./scripts/gen_test_10_fold_cmds.pyand change thearchtoresnet101andinception_v3, respectively. We will get 20 different submission files in./submission, each of which corresponds to a single model. (Same as version 1.) -
40 crop test for each single model: use
./scripts/gen_test_10_fold_cmds_40_crop.pyand change thearchtoresnet101andinception_v3, respectively. We will get 20 different submission files in./submission, each of which corresponds to a single model. -
ensemble scores of all models (average): put all the paths of the submission files in a file, for example:
# 10 crop
ls submission/resnet101_model_epoch_15_fold_* > resnet101_epoch15_10_fold.txt
ls submission/inception_v3_model_epoch_15_fold_* > inception_v3_epoch15_10_fold.txt
cat resnet101_epoch15_10_fold.txt inception_v3_epoch15_10_fold.txt > inception_v3_resnet101_epoch15_10_folds.txt
python ensemble_scores.py inception_v3_resnet101_epoch15_10_folds.txt
# 40 crop
ls submission/resnet101_model_epoch_15_fold_*_40* > 40crop_resnet101_epoch15_10_fold.txt
ls submission/inception_v3_model_epoch_15_fold_*_40* > 40crop_inception_v3_epoch15_10_fold.txt
cat 40crop_resnet101_epoch15_10_fold.txt 40crop_inception_v3_epoch15_10_fold.txt > 40crop_inception_v3_resnet101_epoch15_10_folds.txt
python ensemble_scores.py 40crop_inception_v3_resnet101_epoch15_10_folds.txt
# merge 10 and 40 crop (**NB**: 10crop is used twice)
ls submission/ensemble_submission_inception_v3_resnet101_epoch15_10_folds* submission/ensemble_submission_inception_v3_resnet101_epoch15_10_folds* submission/ensemble_submission_40crop_inception_v3_resnet101_epoch15_10_folds* > 10_40_crop_res101_v3_epoch15.txt
python ensemble_scores.py 10_40_crop_res101_v3_epoch15.txtThe final submission file is like ./submission/ensemble_submission_10_40_crop_res101_v3_epoch15__{date_time}.txt
(This scores 0.48592 on the public Leaderboard, and 0.83833 on the private Leaderboard.)
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