Team "80 TFlops" solution to Carvana Image Masking Challenge on Kaggle. 4th place with TensorFlow-backed Keras.
Solution overview at Yandex (2017-10-07): YouTube (Russian), pdf (English).
Symlink your dataset folder (optional, you can pass an absolute path with --dataset-dir):
ln -s /path/to/dataset input
All of the data is expected to be in that folder, either as a file in the root or as a subdirectory.
Install all the required packages:
pip install -r requirements.txt
You should have Keras master installed:
pip install git+git://github.com/fchollet/keras.git --upgrade --no-deps
python train.py\
--gpu=1\
--seed=80\
--dataset_dir='input'\
--models_dir='models'\
--network='resnet50'\
--preprocessing_function='caffe'\
--weights='weights/resnet-on-test-combined-8960.000010-0-0.0044540-99.7300751.h5'\
--fold=1\
--n_folds=5\
--folds_source='folds.csv'\
--val_mask_dir='gif_train_masks'\
--train_mask_dir='gif_train_masks'
python train.py\
--gpu=1\
--seed=80\
--dataset_dir='input'\
--models_dir='models'\
--network='mobilenet'\
--preprocessing_function='tf'\
--weights='weights/mobilenet-on-test-6400.000010-5-0.0054162-99.7713900.h5'\
--fold=1\
--n_folds=5\
--folds_source='folds.csv'\
--val_mask_dir='gif_train_masks'\
--train_mask_dir='gif_train_masks'
python generate_encoded_submission.py\
--pred_mask_dir='predicted_masks/subfolder'\
--submissions_dir='submissions'\
--pred_threads=32
python predict_multithreaded.py \
--gpu=0,1,2\
--weights=weights/resnet-refine-fold_3-10240.000010-5-0.0033064-99.7107349.h5\
--test_data_dir=input/train\
--pred_mask_dir=predicted_masks/val_from_fold_3\
--fold=3\
--folds_source=folds.csv\
--dataset_dir=input\
--predict_on_val=True
or, alternatively
sh predict_train.sh
id,fold
424658d9f222,2
The columns should respectively contain a car id and a fold number. Important to notice:
- We had 5 of them to do 5-fold cross-validation.
- Since the cars were of a different size and colors, it was critical to keep each of 16 pictures of a single car in the same fold.
- Folds were created by sorting by the size of masks (i.e. total number of 1s vs 0s in the mask, a simple sum over the mask matrix).