keras-face | face detection , verification and recognition using Keras | Computer Vision library

by chen0040 Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | keras-face Summary

keras-face is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Tensorflow, Keras applications. keras-face has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

face detection, verification and recognition using Keras

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keras-face has a low active ecosystem.

It has 131 star(s) with 62 fork(s). There are 8 watchers for this library.

It had no major release in the last 6 months.

There are 8 open issues and 2 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of keras-face is current.

Quality

keras-face has 0 bugs and 0 code smells.

Security

keras-face has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

keras-face code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

keras-face is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

keras-face releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

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

keras-face saves you 11961 person hours of effort in developing the same functionality from scratch.

It has 24149 lines of code, 59 functions and 15 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Create a face reconstruction
Embed inception block
Inverse inception block
Concatenate tensors along axis
Fit the model to a given database
Creates a network
Create a pair of pairwise pairwise pairs
Create base network
Given an image and a database and a dictionary of parameters find the best matching
Convert an image to a PNG image
Given an image and a database return the name of the matching database
Convert image to embedding
Verify the identity against the given database
Verify the integrity of an image
Detect face detection from an image file
Load the model
Load weights
Convert an image into a binary image
Test the triplet loss
Compute the TripleT loss
Loads the face model
Load weights from faceNet model

Get all kandi verified functions for this library.

keras-face Key Features

No Key Features are available at this moment for keras-face.

keras-face Examples and Code Snippets

No Code Snippets are available at this moment for keras-face.

Community Discussions

Trending Discussions on keras-face

Using keras with tensorflow "You must feed a value for placeholder tensor 'input_1' with dtype float"

QUESTION

Using keras with tensorflow "You must feed a value for placeholder tensor 'input_1' with dtype float"

Asked 2017-May-28 at 00:47

I get the unexpected error "You must feed a value for placeholder tensor 'input_1' with dtype float" when training the discriminator of a GAN

here the error:

...

ANSWER

Answered 2017-Mar-10 at 05:53

It comes from the batchnormalization. You can see here : https://stackoverflow.com/a/42470757/7137636 how to fix this issue.

If you need more info, ask in comments :)

Source https://stackoverflow.com/questions/42697901

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

Vulnerabilities

No vulnerabilities reported

Install keras-face

You can download it from GitHub.
You can use keras-face 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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