FaceScrub | Python script for downloading the FaceScrub face dataset

by lightalchemist Python Version: Current License: No License

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kandi X-RAY | FaceScrub Summary

FaceScrub is a Python library typically used in Telecommunications, Media, Advertising, Marketing applications. FaceScrub has no bugs, it has no vulnerabilities and it has low support. However FaceScrub build file is not available. You can download it from GitHub.

This project is released under a Creative Commons Attribution-NonCommercial 4.0 International Public License. To view a copy of this license, visit python_download_facescrub.py downloads the FaceScrub dataset described in. H.-W. Ng, S. Winkler. A data-driven approach to cleaning large face datasets. Proc. IEEE International Conference on Image Processing (ICIP), Paris, France, Oct. 27-30, 2014. If you are using Python 2, use the script python2_download_facescrub.py. If you are using Python 3, use python3_download_facescrub.py. In particular, the Python 3 version has been updated by ottocho to support multi-threading. This code was tested on Ubuntu 14.04 and Mac OS X El Capitan.

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FaceScrub has a low active ecosystem.

It has 22 star(s) with 19 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 2 have been closed. On average issues are closed in 0 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of FaceScrub is current.

Quality

FaceScrub has 0 bugs and 0 code smells.

Security

FaceScrub has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

FaceScrub code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

FaceScrub 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

FaceScrub releases are not available. You will need to build from source code and install.

FaceScrub has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

FaceScrub saves you 171 person hours of effort in developing the same functionality from scratch.

It has 423 lines of code, 23 functions and 2 files.

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

Top functions reviewed by kandi - BETA

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

Download an image
Return the URL for a given URL
Generate headers
Generate a hash from raw bytes
Save an image
Ensure directory exists
Create a logger
Parse a line from a line
Setup a Requests session

Get all kandi verified functions for this library.

FaceScrub Key Features

No Key Features are available at this moment for FaceScrub.

FaceScrub Examples and Code Snippets

No Code Snippets are available at this moment for FaceScrub.

Community Discussions

Trending Discussions on FaceScrub

Caffe accuracy increases too fast

QUESTION

Caffe accuracy increases too fast

Asked 2017-Mar-20 at 11:28

I'm doing a AlexNet fine tuning for face detection following this: link

The only difference with the link is that I am using another dataset (facescrub and some images from imagenet as negative examples).

I noticed the accuracy increasing too fast, in 50 iterations it goes from 0.308 to 0.967 and when it is about 0.999 I stop the training and use the model using the same python script as the above link.

I use for testing an image from the dataset and the result is nowhere near good, test image result. As you can see the box in the faces is too big (and the dataset images are tightly cropped), not to mention the box not containing a face.

My solver and train_val files are exactly the same, only difference is batch sizes and max iter size.

...

ANSWER

Answered 2017-Mar-20 at 11:28

The reason was that my dataset has way more face examples than non-face examples. I tried the same setup with the same number of positive and negative examples and now the accuracy increases slower.

Source https://stackoverflow.com/questions/42898937

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

Vulnerabilities

No vulnerabilities reported

Install FaceScrub

First, obtain the FaceScrub files containing links to the images from http://vintage.winklerbros.net/facescrub.html
Next, set MY_USER_AGENT_STRING in the script. You can obtain it by visiting a site such as https://www.whatismybrowser.com/detect/what-is-my-user-agent
Finally, run download_facescrub.py to download the dataset.
Note: actors_users_normal_bbox.txt is obtained from http://vintage.winklerbros.net/facescrub.html. The above code will save full size images to the directory actors/images and faces (if required) to actors/faces. The naming convention for full size images is <name>_<image_id>.<ext> and <name>_<image_id>_<face_id>.<ext> for face images. Note that <ext> is the extension of image format for the image. It need not be "jpeg". All error messages in the log are of the form "Line <number>: <error message>: <url>", in case users are interested in them.

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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