youtube-8m | CVPR 2017 Workshop ) Hierarchical Deep Recurrent | Machine Learning library

by Tsingularity Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | youtube-8m Summary

youtube-8m is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Neural Network applications. youtube-8m has no bugs, it has no vulnerabilities and it has low support. However youtube-8m build file is not available. You can download it from GitHub.

(CVPR 2017 Workshop) Hierarchical Deep Recurrent Architecture for Video Understanding

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Security

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Support

youtube-8m has a low active ecosystem.

It has 6 star(s) with 2 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. On average issues are closed in 1009 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of youtube-8m is current.

Quality

youtube-8m has no bugs reported.

Security

youtube-8m has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

youtube-8m does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

youtube-8m releases are not available. You will need to build from source code and install.

youtube-8m has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed youtube-8m and discovered the below as its top functions. This is intended to give you an instant insight into youtube-8m implemented functionality, and help decide if they suit your requirements.

Run the model
Builds the graph of the given model
Find a class by name
Build the model
Get input tensors
Evaluate the model
Runs evaluation loop
Adds a summary
Adds a single global step summary
Retrieves the list of feature names and sizes
Creates a model
Sample random frames
Pool the input frames
Returns a random sequence of random samples
Calculate the average precision
Calculate the average precision of predictions
Builds inputs and outputs
Builds prediction graph
Run inference
Format examples
Retrieves a list of feature names and sizes
Convert a dict to a CSV row
Prepare a tf reader
Return the value at the given index
Reads a TFRecord reader
Return a reader object
Start the parameter server
Find a class by its name

Get all kandi verified functions for this library.

youtube-8m Key Features

No Key Features are available at this moment for youtube-8m.

youtube-8m Examples and Code Snippets

No Code Snippets are available at this moment for youtube-8m.

Community Discussions

Trending Discussions on youtube-8m

What algorithm is used for audio feature extraction in google's audioset?

How to download youtube-8m dataset using curl

Read Frame-level features from youtube-8m tf-records

QUESTION

What algorithm is used for audio feature extraction in google's audioset?

Asked 2018-Aug-13 at 08:52

I am getting started with Google's Audioset. While the dataset is extensive, I find the information with regards to the audio feature extraction very vague. The website mentions

128-dimensional audio features extracted at 1Hz. The audio features were extracted using a VGG-inspired acoustic model described in Hershey et. al., trained on a preliminary version of YouTube-8M. The features were PCA-ed and quantized to be compatible with the audio features provided with YouTube-8M. They are stored as TensorFlow Record files.

Within the paper, the authors discuss using mel spectrograms on 960 ms chunks to get a 96x64 representation. It is then unclear to me how they get to the 1x128 format representation used in the Audioset. Does anyone know more about this??

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ANSWER

Answered 2018-Aug-13 at 08:52

They use the 96*64 data as input for a modified VGG network.The last layer of VGG is FC-128, so its output will be 1*128, and that is the reason.

The architecture of VGG can be found here: https://github.com/tensorflow/models/blob/master/research/audioset/vggish_slim.py

Source https://stackoverflow.com/questions/44080007

QUESTION

How to download youtube-8m dataset using curl

Asked 2017-Nov-02 at 19:18

The Youtube-8m download webpage provides the following curl instructions:

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ANSWER

Answered 2017-Nov-02 at 15:50

That script is intended to run in a *nix (Unix or linux or ...) environment.

Do you have the bash for windows installed? If so, that is the quick solution, just run the script/cmds in that environment (and make sure that which python returns the correct /path/to/preferred/version_of/python).

To explain/expand on what that code does, *nix allows setting env vars specific to the command being run at the end of the line. An alternate way to "say" the same thing as the code you have included in *nix is

Source https://stackoverflow.com/questions/47078364

QUESTION

Read Frame-level features from youtube-8m tf-records

Asked 2017-Sep-14 at 12:23

As denoted here youtube-8m tf-records are saved with the format comes at the end of my question.I write a code to extract features. but there is a problem. the code can read all elements in features successfully but it is not able to read feature_lists. in fact, the example does not include features_list at all and I get an error while I try to access it. How can I read the feauures_list. I attach Data format, My code and the output :

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ANSWER

Answered 2017-Sep-14 at 12:23

Instead of

Source https://stackoverflow.com/questions/45811941

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install youtube-8m

You can download it from GitHub.
You can use youtube-8m like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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