NEW: I just released a pretrained model (Gated NetVLAD) as of 11th December 2017! you can download the pretrained model here: https://www.rocq.inria.fr/cluster-willow/amiech/pretrainedmodel.zip the model is: gatednetvladLF-256k-1024-80-0002-300iter-norelu-basic-gatedmoe
This is code of the kaggle Youtube-8M Large-Scale Video Understanding challenge winners (https://www.kaggle.com/c/youtube8m). For more details about our models, please read our arXiv paper: https://arxiv.org/abs/1706.06905 .
This repo contains some code to reproduce a winning submission for the kaggle challenge. If you are just looking for an efficient Tensorflow implementation of NetVLAD, NetRVLAD, NetFV, Soft-DBoW and their gated version, please directly consult our Tensorflow toolbox (LOUPE): https://github.com/antoine77340/LOUPE.
The code is built on top of the Google Youtube-8M starter code (https://github.com/google/youtube-8m). Please look at their README to see the needed dependencies to run the code (mainly Tensorflow 1.0).
You will additionally only need to have the pandas python library installed.
Hardware requirement: Each model have been run on a single NVIDIA TITAN X 12 GB GPU. Be aware that some of the models do not fit with a GPU with less than 9GB of memory. Please do not modify the training batch size of these models as it might affect the final results.
Our best submitted model (GAP: 0.84967% on the private leaderboard) is a weighted ensemble of 25 models. However for the sake of simplicity, we present a much more simple ensemble of 7 models that is enough to reach the first place with a significant margin (GAP ~ 84.7%). The 25 models trained are only some very similar variant (of hyper-parameter) of these seven main models.
Please note that because of the time constraint, we did not have time to try to run the code from scratch. It might be possible, but rather unlikely, that something is not working properly. If so please create an issue on github.
Each of the following command lines train a single model. They are scheduled to stop training at the good time.
Our models were trained on all the training set and almost all the validation set. We only discarded 21k videos to build a smaller validation set. This validation set (used in the arXiv paper) is composed of all the tensorflow record file of the form: 'validatea*.tfrecord'. We will however, train the models on all both training and validation set as it was allowed in the kaggle competition. It should not make any huge difference.
Each model takes several days to train, so each command line are separated in order to be run in parallel if possible. Please replace 'path_to_features' with the folder path which contains all the tensorflow record frame level feature.
path_to_features='path_to_features'Training Gated NetVLAD (256 Clusters):
python train.py --train_data_pattern="$path_to_features/*a*??.tfrecord" --model=NetVLADModelLF --train_dir=gatednetvladLF-256k-1024-80-0002-300iter-norelu-basic-gatedmoe --frame_features=True --feature_names="rgb,audio" --feature_sizes="1024,128" --batch_size=80 --base_learning_rate=0.0002 --netvlad_cluster_size=256 --netvlad_hidden_size=1024 --moe_l2=1e-6 --iterations=300 --learning_rate_decay=0.8 --netvlad_relu=False --gating=True --moe_prob_gating=True --max_step=700000Note: The best single model is this one but with the flag --max_step=300000. We somehow need it to train longer for better effect on the ensemble. G Training Gated NetFV (128 Clusters):
python train.py --train_data_pattern="$path_to_features/*a*??.tfrecord" --model=NetFVModelLF --train_dir=gatednetfvLF-128k-1024-80-0002-300iter-norelu-basic-gatedmoe --frame_features=True --feature_names="rgb,audio" --feature_sizes="1024,128" --batch_size=80 --base_learning_rate=0.0002 --fv_cluster_size=128 --fv_hidden_size=1024 --moe_l2=1e-6 --iterations=300 --learning_rate_decay=0.8 --fv_relu=False --gating=True --moe_prob_gating=True --fv_couple_weights=False --max_step=600000Training Gated Soft-DBoW (4096 Clusters):
python train.py --train_data_pattern="$path_to_features/*a*??.tfrecord" --model=GatedDbofModelLF --train_dir=gateddboflf-4096-1024-80-0002-300iter --frame_features=True --feature_names="rgb,audio" --feature_sizes="1024,128" --batch_size=80 --base_learning_rate=0.0002 --dbof_cluster_size=4096 --dbof_hidden_size=1024 --moe_l2=1e-6 --iterations=300 --dbof_relu=False --max_step=1000000Training Soft-DBoW (8000 Clusters):
python train.py --train_data_pattern="$path_to_features/*a*??.tfrecord" --model=SoftDbofModelLF --train_dir=softdboflf-8000-1024-80-0002-300iter --frame_features=True --feature_names="rgb,audio" --feature_sizes="1024,128" --batch_size=80 --base_learning_rate=0.0002 --dbof_cluster_size=8000 --dbof_hidden_size=1024 --iterations=300 --dbof_relu=False --max_step=800000Training Gated NetRVLAD (256 Clusters):
python train.py --train_data_pattern="$path_to_features/*a*??.tfrecord" --model=NetVLADModelLF --train_dir=gatedlightvladLF-256k-1024-80-0002-300iter-norelu-basic-gatedmoe --frame_features=True --feature_names="rgb,audio" --feature_sizes="1024,128" --batch_size=80 --base_learning_rate=0.0002 --netvlad_cluster_size=256 --netvlad_hidden_size=1024 --moe_l2=1e-6 --iterations=300 --learning_rate_decay=0.8 --netvlad_relu=False --gating=True --moe_prob_gating=True --lightvlad=True --max_step=600000Training GRU (2 layers):
python train.py --train_data_pattern="$path_to_features/*a*??.tfrecord" --model=GruModel --train_dir=GRU-0002-1200 --frame_features=True --feature_names="rgb,audio" --feature_sizes="1024,128" --batch_size=128 --base_learning_rate=0.0002 --gru_cells=1200 --learning_rate_decay=0.9 --moe_l2=1e-6 --max_step=300000Training LSTM (2 layers):
python train.py --train_data_pattern="$path_to_features/*a*??.tfrecord" --model=LstmModel --train_dir=lstm-0002-val-150-random --frame_features=True --feature_names="rgb,audio" --feature_sizes="1024,128" --batch_size=128 --base_learning_rate=0.0002 --iterations=150 --lstm_random_sequence=True --max_step=400000After training, we will write the predictions into 7 different files and then ensemble them. Run each one of this command to run inference for each model.
python inference.py --output_file=test-lstm-0002-val-150-random.csv --input_data_pattern="$path_to_features/test*.tfrecord" --model=LstmModel --train_dir=lstm-0002-val-150-random --frame_features=True --feature_names="rgb,audio" --feature_sizes="1024,128" --batch_size=1024 --base_learning_rate=0.0002 --iterations=150 --lstm_random_sequence=True --run_once=True --top_k=50
python inference.py --output_file=test-gatedlightvladLF-256k-1024-80-0002-300iter-norelu-basic-gatedmoe.csv --input_data_pattern="$path_to_features/test*.tfrecord" --model=NetVLADModelLF --train_dir=gatedlightvladLF-256k-1024-80-0002-300iter-norelu-basic-gatedmoe --frame_features=True --feature_names="rgb,audio" --feature_sizes="1024,128" --batch_size=1024 --base_learning_rate=0.0002 --netvlad_cluster_size=256 --netvlad_hidden_size=1024 --moe_l2=1e-6 --iterations=300 --learning_rate_decay=0.8 --netvlad_relu=False --gating=True --moe_prob_gating=True --lightvlad=True --run_once=True --top_k=50
python inference.py --output_file=test-gateddboflf-4096-1024-80-0002-300iter-gatedmoe.csv --input_data_pattern="$path_to_features/test*.tfrecord" --model=GatedDbofModelLF --train_dir=gateddboflf-4096-1024-80-0002-300iter-gatedmoe --frame_features=True --feature_names="rgb,audio" --feature_sizes="1024,128" --batch_size=512 --base_learning_rate=0.0002 --dbof_cluster_size=4096 --dbof_hidden_size=1024 --moe_l2=1e-6 --iterations=300 --dbof_relu=False --moe_prob_gating=True --run_once=True --top_k=50
python inference.py --output_file=test-gatednetfvLF-128k-1024-80-0002-300iter-norelu-basic-gatedmoe.csv --input_data_pattern="$path_to_features/test*.tfrecord" --model=NetFVModelLF --train_dir=gatednetfvLF-128k-1024-80-0002-300iter-norelu-basic-gatedmoe --frame_features=True --feature_names="rgb,audio" --feature_sizes="1024,128" --batch_size=1024 --base_learning_rate=0.0002 --fv_cluster_size=128 --fv_hidden_size=1024 --moe_l2=1e-6 --iterations=300 --learning_rate_decay=0.8 --fv_relu=False --gating=True --moe_prob_gating=True --fv_couple_weights=False --top_k=50
python inference.py --output_file=test-GRU-0002-1200.csv --input_data_pattern="$path_to_features/test*.tfrecord" --model=GruModel --train_dir=GRU-0002-1200 --frame_features=True --feature_names="rgb,audio" --feature_sizes="1024,128" --batch_size=1024 --base_learning_rate=0.0002 --gru_cells=1200 --learning_rate_decay=0.9 --moe_l2=1e-6 --run_once=True --top_k=50
python inference.py --output_file=test-gatednetvladLF-256k-1024-80-0002-300iter-norelu-basic-gatedmoe.csv --input_data_pattern="$path_to_features/test*.tfrecord" --model=NetVLADModelLF --train_dir=gatednetvladLF-256k-1024-80-0002-300iter-norelu-basic-gatedmoe --frame_features=True --feature_names="rgb,audio" --feature_sizes="1024,128" --batch_size=1024 --base_learning_rate=0.0002 --netvlad_cluster_size=256 --netvlad_hidden_size=1024 --moe_l2=1e-6 --iterations=300 --learning_rate_decay=0.8 --netvlad_relu=False --gating=True --moe_prob_gating=True --run_once=True --top_k=50
python inference.py --output_file=test-softdboflf-8000-1024-80-0002-300iter.csv --input_data_pattern="$path_to_features/test*.tfrecord" --model=SoftDbofModelLF --train_dir=softdboflf-8000-1024-80-0002-300iter --frame_features=True --feature_names="rgb,audio" --feature_sizes="1024,128" --batch_size=256 --base_learning_rate=0.0002 --dbof_cluster_size=8000 --dbof_hidden_size=1024 --iterations=300 --dbof_relu=False --run_once=True --top_k=50After inference done for all models just run:
python file_averaging.pyIt will just take you the time to make a coffee and the submission file will be written in WILLOW_submission.csv before you finish to drink it :D.
Antoine Miech