Face-Detection-OpenCV | Face detection using OpenCV and Python | Computer Vision library

by informramiz HTML Version: Current License: Apache-2.0

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

kandi X-RAY | Face-Detection-OpenCV Summary

Face-Detection-OpenCV is a HTML library typically used in Artificial Intelligence, Computer Vision, OpenCV applications. Face-Detection-OpenCV has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Face detection using OpenCV and Python

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Face-Detection-OpenCV has a low active ecosystem.

It has 145 star(s) with 118 fork(s). There are 14 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Face-Detection-OpenCV is current.

Quality

Face-Detection-OpenCV has 0 bugs and 0 code smells.

Security

Face-Detection-OpenCV has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Face-Detection-OpenCV code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Face-Detection-OpenCV is licensed under the Apache-2.0 License. This license is Permissive.

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

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Face-Detection-OpenCV releases are not available. You will need to build from source code and install.

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

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Face-Detection-OpenCV Key Features

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Face-Detection-OpenCV Examples and Code Snippets

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Community Discussions

Trending Discussions on Face-Detection-OpenCV

What is the difference between Floating point 16 and 8 bit quantized in Deep Learning Model

QUESTION

What is the difference between Floating point 16 and 8 bit quantized in Deep Learning Model

Asked 2019-Oct-18 at 04:49

Currently, I am reading this website to understand the Face Detection Models. In this article, it mentioned about the Floating Point 16 and 8 bit quantized version model.

I would like to ask:

What is the difference between two of them?
What is the application of different types of DL model? In which case that we need to use ?

Link to website: https://www.learnopencv.com/face-detection-opencv-dlib-and-deep-learning-c-python/

...

ANSWER

Answered 2019-Oct-18 at 04:49

What is the difference between two of them?

As you can see, the model size with 8-bit is smaller than the FP16 one, FP16 and 8-bit here means the precision and type of the model weight values.
What is the application of different types of DL model? In which case that we need to use ?

Normally high precision will make the model size big but may be more accurate, however, in some case when we need to run the model with small latency or on light-weight device, we may want to reduce the model size with some slight lost of accuracy some times.

Source https://stackoverflow.com/questions/58442920

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

Vulnerabilities

No vulnerabilities reported

Install Face-Detection-OpenCV

You can download it from GitHub.

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