FaceDetection_CNN | Multi-view Face Detection Using Deep | Machine Learning library

by guoyilin Python Version: Current License: No License

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kandi X-RAY | FaceDetection_CNN Summary

FaceDetection_CNN is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. FaceDetection_CNN has no bugs, it has no vulnerabilities and it has low support. However FaceDetection_CNN build file is not available. You can download it from GitHub.

Implement Yahoo Paper: Multi-view Face Detection Using Deep Convolutional Neural Networks 1. Image Preprocess aflw dataset[1]. Use iou>=0.5 as positive, iou0.3 as negative. You should download the aflw dataset by yourself. 2. Fine-tune Alex-Net using AFLW dataset. The model is in Baidu Yun: or Google Drive: 3. Convert fully connected layers into convolutional layers by reshaping layer parameters, see [2], you can use the convert_full_conv() function in test.py for converting. 4. Get heat map for each scale of image. 5. Process heat map by using non-maximal suppression to accurately localize the faces..

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FaceDetection_CNN has a low active ecosystem.

It has 317 star(s) with 219 fork(s). There are 34 watchers for this library.

It had no major release in the last 6 months.

There are 10 open issues and 7 have been closed. On average issues are closed in 15 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of FaceDetection_CNN is current.

Quality

FaceDetection_CNN has 0 bugs and 0 code smells.

Security

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

FaceDetection_CNN code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

FaceDetection_CNN 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.

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

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

FaceDetection_CNN saves you 121 person hours of effort in developing the same functionality from scratch.

It has 306 lines of code, 7 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 FaceDetection_CNN and discovered the below as its top functions. This is intended to give you an instant insight into FaceDetection_CNN implemented functionality, and help decide if they suit your requirements.

Find the intersection between two images .

Get all kandi verified functions for this library.

FaceDetection_CNN Key Features

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FaceDetection_CNN Examples and Code Snippets

No Code Snippets are available at this moment for FaceDetection_CNN.

Community Discussions

Trending Discussions on FaceDetection_CNN

Caffe training uses face crop but deploy uses full image

Caffe accuracy increases too fast

QUESTION

Caffe training uses face crop but deploy uses full image

Asked 2017-May-18 at 18:21

I'm implementing this project and it is working fine. Now I wonder how is it possible that the training phase uses only a face crop of the image, but actual use can accept a full image with multiple people.

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ANSWER

Answered 2017-May-18 at 18:19

The model is trained to find a face within an image. Training with face crops allows the training to converge faster, as it does not go through the trial-and-error to recognize -- and then learn to ignore -- other structures in the input images. The full capacity of the model topology can go toward facial features.

When you get to scoring ("actual use", a.k.a. inference), the model has no training for or against all the other stuff in each photo. It's trained to find faces, and will do that well.

Does that explain it well enough?

Source https://stackoverflow.com/questions/44048357

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.

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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 FaceDetection_CNN

You can download it from GitHub.
You can use FaceDetection_CNN 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.

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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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