Skip to content
master
Switch branches/tags
Code
This branch is up to date with master.
Contribute

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 

Temporal Modeling Approaches for Large-scale Youtube-8M Video Understanding


By Fu Li, Chuang Gan, Xiao Liu, Yunlong Bian, Xiang Long, Yandong Li, Zhichao Li, Jie Zhou, Shilei Wen (Baidu IDL & Tsinghua University)

Table of Contents

  1. Introduction
  2. Usage
  3. Results
  4. Citation

Introduction

This repository contains the data providers and model configurations of three temporal modeling approaches (fast-forward sequence model, two stream sequence model and temporal residual neural networks) described in the paper "Temporal Modeling Approaches for Large-scale Youtube-8M Video Understanding" (xxx). These model configurations are those used in the Google Cloud & YouTube-8M Video Understanding Challenge (https://www.kaggle.com/c/youtube8m/leaderboard).

Usage

Dependencies of PaddlePaddle 0.9.0 (https://github.com/PaddlePaddle/Paddle) and Python 2.7.

Model Training:

cfg=your_config_file
paddle_trainer \
    --config=$cfg \
    --save_dir=./models \
    --trainer_count=4 \
    --log_period=20 \
    --num_passes=100 \
    --use_gpu=true \
    --test_period=0 \
    --show_parameter_stats_period=100

Model Testing:

cfg=your_config_file
paddle_trainer \
    --config=$cfg \
    --use_gpu=true \
    --gpu_id=0 \
    --trainer_count=1 \
    --job=test \
    --init_model_path=pass-00000 \
    --predict_output_dir=output \
    --log_period=20 

Results

Model GAP@20
Temporal CNN 0.80889
Two-stream LSTM 0.82172
Two-stream GRU 0.82366
Fast-forward LSTM 0.81885
Fast-forward GRU 0.81970
Fast-forward LSTM (depth7) 0.82750

Citation

About

PaddlePaddle models for Youtube-8M Video Understanding Challenge

Resources

License

Releases

No releases published

Packages

No packages published