All Projects → yuanxiaosc → Multimodal Short Video Dataset And Baseline Classification Model

yuanxiaosc / Multimodal Short Video Dataset And Baseline Classification Model

500,000 multimodal short video data and baseline models. 50万条多模态短视频数据集和基线模型(TensorFlow2.0)。

Projects that are alternatives of or similar to Multimodal Short Video Dataset And Baseline Classification Model

Tf Dann
Domain-Adversarial Neural Network in Tensorflow
Stars: ✭ 556 (+826.67%)
Mutual labels:  jupyter-notebook, tensorflow-models
Generative Adversarial Networks
Introduction to generative adversarial networks, with code to accompany the O'Reilly tutorial on GANs
Stars: ✭ 505 (+741.67%)
Mutual labels:  jupyter-notebook, tensorflow-models
Tensorflow shiny
A R/Shiny app for interactive RNN tensorflow models
Stars: ✭ 118 (+96.67%)
Mutual labels:  jupyter-notebook, tensorflow-models
Boltzmann Machines
Boltzmann Machines in TensorFlow with examples
Stars: ✭ 768 (+1180%)
Mutual labels:  jupyter-notebook, tensorflow-models
Keras model compression
Model Compression Based on Geoffery Hinton's Logit Regression Method in Keras applied to MNIST 16x compression over 0.95 percent accuracy.An Implementation of "Distilling the Knowledge in a Neural Network - Geoffery Hinton et. al"
Stars: ✭ 59 (-1.67%)
Mutual labels:  jupyter-notebook
Gendis
Contains an implementation (sklearn API) of the algorithm proposed in "GENDIS: GEnetic DIscovery of Shapelets" and code to reproduce all experiments.
Stars: ✭ 59 (-1.67%)
Mutual labels:  jupyter-notebook
Road Lane Instance Segmentation Pytorch
tuSimple dataset road lane instance segmentation with PyTorch, ROS, ENet, SegNet and Discriminative Loss.
Stars: ✭ 59 (-1.67%)
Mutual labels:  jupyter-notebook
Deej A.i.
Create automatic playlists by using Deep Learning to *listen* to the music
Stars: ✭ 57 (-5%)
Mutual labels:  jupyter-notebook
Kdd2020multimodalities
KDD Cup 2020 Challenges for Modern E-Commerce Platform: Multimodalities Recall
Stars: ✭ 60 (+0%)
Mutual labels:  jupyter-notebook
Soccer xg
A Python package for training and analyzing expected goals (xG) models in soccer.
Stars: ✭ 60 (+0%)
Mutual labels:  jupyter-notebook
Deep Learning 101
The tools and syntax you need to code neural networks from day one.
Stars: ✭ 59 (-1.67%)
Mutual labels:  jupyter-notebook
Rastermap
A multi-dimensional embedding algorithm
Stars: ✭ 58 (-3.33%)
Mutual labels:  jupyter-notebook
Stylist
Fast artistic style transfer with convolutional neural networks.
Stars: ✭ 59 (-1.67%)
Mutual labels:  jupyter-notebook
Content based movie recommender
Stars: ✭ 59 (-1.67%)
Mutual labels:  jupyter-notebook
Image Outpainting
🏖 Keras Implementation of Painting outside the box
Stars: ✭ 1,106 (+1743.33%)
Mutual labels:  jupyter-notebook
Ssd keras
Port of Single Shot MultiBox Detector to Keras
Stars: ✭ 1,101 (+1735%)
Mutual labels:  jupyter-notebook
Storytelling With Data
Course materials for Dartmouth Course: Storytelling with Data (PSYC 81.09).
Stars: ✭ 59 (-1.67%)
Mutual labels:  jupyter-notebook
Feedinlib
This repository contains implementations of photovoltaic models to calculate electricity generation from a pv installation based on given solar radiation. Furthermore it contains all necessary pre-calculations.
Stars: ✭ 59 (-1.67%)
Mutual labels:  jupyter-notebook
Home price estimator
Uses Zillow metadata, NLP on realtor description, and VGG16 on home images to predict home sale prices in Portland from 6/16 - 7/17.
Stars: ✭ 59 (-1.67%)
Mutual labels:  jupyter-notebook
Applied Text Mining In Python
Repo for Applied Text Mining in Python (coursera) by University of Michigan
Stars: ✭ 59 (-1.67%)
Mutual labels:  jupyter-notebook

Multimodal Short Video Data Set and Baseline Classification Model

If you have data / access to data / better model, please feel free to issue /pull requests / contact me [email protected]

This resource contains 50+ million(865G) multimodal short video data sets and TensorFlow2.0 multimodal short video classification model, aiming at creating a multimodal classification framework.

Multimodal short video data = short video description text + short video cover image + short video

本资源含有 50+ 万条(865G)多模态短视频数据集和 TensorFlow2.0 多模态短视频分类模型,旨在打造多模态分类框架。

多模态短视频数据 = 短视频描述文本 + 短视频封面图 + 短视频

click to view example data


1. Multimodal dataset information

The current multimodal short video dataset contains 50+ million multimodal data, covering 31 categories, occupying a total of 865G space. Download and unzip the multimodal_data_info.rar file and you will get the download address for all datas. You can download them directly using data_download_tools, but you can also use your own download tool.

目前多模态短视频数据集含有50+万条多模态数据,它们涵盖31个类别,共占用865G空间。下载并解压 multimodal_data_info.rar 文件,你可以获得所有数据的下载地址。你可以直接使用 data_download_tools 下载它们,当然你也可以使用自己的下载工具。

Multimodal data (31 types)

Video category Chinese and English mapping dictionary 视频种类中英文映射字典

video_type_dict = {'360VR': 'VR', '4k': '4K', 'Technology': '科技', 'Sport': '运动', 'Timelapse': '延时',
                   'Aerial': '航拍', 'Animals': '动物', 'Sea': '大海', 'Beach': '海滩', 'space': '太空',
                   'stars': '星空', 'City': '城市', 'Business': '商业', 'Underwater': '水下摄影',
                   'Wedding': '婚礼', 'Archival': '档案', 'Backgrounds': '背景', 'Alpha Channel': '透明通道',
                   'Intro': '开场', 'Celebration': '庆典', 'Clouds': '云彩', 'Corporate': '企业',
                   'Explosion': '爆炸', 'Film': '电影镜头', 'Green Screen': '绿幕', 'Military': '军事',
                   'Nature': '自然', 'News': '新闻', 'R3d': 'R3d', 'Romantic': '浪漫', 'Abstract': '抽象'}

In addition to 360VR type video data, each of the other types has approximately 20,000 pieces of data. You can check the contents of all multimodal files at any time using the download_file_info.ipynb tool in data_download_tools. As follows:

除了360VR类型的视频数据,其它每个类型有大约20000条数据。你可以使用data_download_tools中的download_file_info.ipynb工具随时检查所有多模态文件的内容,如下所示:

Check the disk space occupied by the data. 检查数据占用的磁盘空间。

Check a type of video cover image and corresponding video description information. 检查某个类型的视频封面图以及对应的视频描述信息。

multimodal data statistics

The multimodal_data_info.json file contains statistics on 562,342 multimodal data, ['mp4_id', 'video_label', 'mp4_time', 'mp4_download_url', 'mp4_background_image_url', 'mp4_txt_brief'] content.

The content of multimodal_data_info.json is as follows:

{"mp4_id": "80328682", "mp4_download_url": "https://p5-v1.xpccdn.com/080328682_main_xl.mp4",
 "mp4_time": "0:16", "mp4_background_image_url": "https://p5-i1.xpccdn.com/080328682_iconl.jpeg",
 "mp4_txt_brief": " Woman in swimsuit and cover up walking at the beach", "video_label": "Beach"}

{"mp4_id": "63660083", "mp4_download_url": "https://p5-v1.xpccdn.com/063660083_main_xl.mp4",
"mp4_time": "0:29", "mp4_background_image_url": "https://p5-i1.xpccdn.com/063660083_iconl.jpeg",
 "mp4_txt_brief": " 4K Happy female friends chatting & drinking on city rooftop in the summer", "video_label": "City"}

You can use the data_analysis.ipynb tool in aggregate_download_data_to_a_json_file to count the data of a multimodal file. The statistics are as follows.

你可以使用aggregate_download_data_to_a_json_file中的data_analysis.ipynb工具统计多模态文件的数据,统计结果如下所示。


2. Baseline Classification Model

查看我的博客 短视频分类技术 获取更多短视频分类信息。

Model structure picture 模型结构图

Model structure test 模型结构测试

Click on baseline_model to learn more

Require

  • python 3+, e.g. python==3.6
  • tensorflow version 2, e.g. tensorflow==2.0.0-beta1
  • tensorflow-datasets

Train Model

python train_multimodal_baseline_model.py

4. Build your own model

Click on data_interface_for_model to learn more

Data can be easily provided to your model using the data_interface_for_model data interface. Data_interface_for_model contains three types of data interfaces: tensor required by TensorFlow, numpy required by Pytorch, and native Python type.

可以使用data_interface_for_model 数据接口方便的为你的模型提供数据。data_interface_for_model包含三种类型的数据接口,分别是:TensorFlow需要的tensor、Pytorch需要的numpy和原生的Python类型。


5. Copyright Statement

Currently all multimodal video data comes from the Internet, and the data is copyrighted by the original author. If this data (from https://xinpianchang.com) is used for profit, please contact [email protected] to purchase data copyright.

目前所有多模态视频数据来自互联网,该数据版权归原作者所有。如果将该数据(来自 https://xinpianchang.com )用于牟利,请联系 [email protected] 购买数据版权。

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].