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Label Denoising with Large Ensembles of Heterogeneous Neural Networks

Samsung AI Center Moscow solution of the 2nd YouTube-8M Video Understanding Challenge.

Reproduce results

First, prepare the data (convert it to numpy, split into several folds):

python youtube8m/utils/bin/convert_to_numpy.py path_to_train_and_val_tfrecords_video_level /home/video_level_numpy_tmp
python youtube8m/utils/bin/convert_to_numpy.py path_to_test_tfrecords_video_level /home/video_level_numpy_test_tmp --is-test
python youtube8m/utils/bin/split_into_folds.py /home/video_level_numpy_tmp /home/video_level_numpy
python youtube8m/utils/bin/combine_test_blobs.py /home/video_level_numpy_test_tmp /home/video_level_numpy_test
python youtube8m/utils/bin/big_dataset_convert_and_split.py path_to_train_and_val_tfrecords_frame_level /home/video_level_numpy /home/frame_level_numpy
python youtube8m/utils/bin/big_dataset_convert_test.py path_to_test_tfrecords_frame_level /home/frame_level_numpy_test

Then, train all L1 models.

Use config files from configs/ folder. Do not forget to use correct src_data section (see details in a config file).

For each config file, call:

python youtube8m/video_level_nn_models/bin/nntool.py --config path_to_config_file --out /home/model1_output_path fit_predict

Prepare features for L2 model

For each trained model from L1:

  1. Sort predictions by video_ids
python youtube8m/utils/bin/sort_all_predictions.py path_to_model

If you used frame-level features for training a model, add flag --frame-level to the command.

  1. Average test predictions from different folds
python youtube8m/utils/bin/average_test_predictions.py path_to_model

If you used frame-level features for training a model, add flag --frame-level to the command.

  1. Add path_to_model to youtube8m/utils/bin/ready_models.

  2. Merge predictions from different models

python youtube8m/utils/bin/merge_predictions.py youtube8m/utils/bin/ready_models output_path_with_merged_chunks
  1. Extract features for LGBM:
python youtube8m/utils/bin/prepare_features_for_lgbm.py path_with_merged_chunks output_path_with_prepared_features

Train LGBM model :tree:

python youtube8m/lgbm_models/bin/lgbm_train.py path_with_prepared_features/folds path_with_prepared_features/test output_path

Build soft labels index

python youtube8m/utils/bin/build_soft_labels_index.py lgbm_output_path lgbm_path_with_prepared_features output_soft_labels_index_path

Then, train tf models separately: EnsembleModelA, EnsembleModelB, …, EnsembleModelF and use OOF predictions for training EnsembleModel. Note, you need to use correct soft_labels_path in NumpyY8MAggFeaturesReader.

After this procedure you will be able to run eval.py and inference.py and get final predictions and a final metagraph.