Face_Pytorch | face recognition algorithms in pytorch framework, including arcface, cosface, sphereface and so on | Computer Vision library

by wujiyang Python Version: Current License: Apache-2.0

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

kandi X-RAY | Face_Pytorch Summary

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

The implementation of popular face recognition algorithms in pytorch framework, including arcface, cosface and sphereface and so on. All codes are evaluated on Pytorch 0.4.0 with Python 3.6, Ubuntu 16.04.10, CUDA 9.1 and CUDNN 7.1. Partially evaluated on Pytorch 1.0.

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Face_Pytorch has a low active ecosystem.

It has 762 star(s) with 153 fork(s). There are 23 watchers for this library.

It had no major release in the last 6 months.

There are 53 open issues and 11 have been closed. On average issues are closed in 146 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Face_Pytorch is current.

Quality

Face_Pytorch has 0 bugs and 0 code smells.

Security

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

Face_Pytorch code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Face_Pytorch 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.

Reuse

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

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

Face_Pytorch saves you 1258 person hours of effort in developing the same functionality from scratch.

It has 2828 lines of code, 150 functions and 39 files.

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

Top functions reviewed by kandi - BETA

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

Train the model
Extract features from torch txt
Calculates the coefficient of 10 fold
Plot loss curves
Calculate the threshold for the given scores
Calculate accuracy
Extract a feature
Write a matplotlib matrix to a file
Load a BAM model
Extract features from torch Tensor
Calculate the accuracy of 10 folds
Return a list of all the blocks
Load an MXNet record
Read the names from a file
Create a single layer
Plot the loss curves
Load a mobile face network
Returns a DataLoader for a dataset
Load a MAT file
Read a matrix from file

Get all kandi verified functions for this library.

Face_Pytorch Key Features

No Key Features are available at this moment for Face_Pytorch.

Face_Pytorch Examples and Code Snippets

No Code Snippets are available at this moment for Face_Pytorch.

Community Discussions

Trending Discussions on Face_Pytorch

Why loaded Pytorch model's loss highly increased?

QUESTION

Why loaded Pytorch model's loss highly increased?

Asked 2020-Oct-27 at 17:03

I'm trying to train Arcface with reference to.

As far as I know, Arcface requires more than 200 training epochs on CASIA-webface with a large batch size.

Within 100 epochs of training, I stopped the training for a while because I was needed to use GPU for other tasks. And the checkpoints of the model(Resnet) and margin are saved. Before it was stopped, its loss recorded a value between 0.3~1.0, and training accuracy was mount to 80~95%.

When I resume the Arcface training by loading the checkpoint files using load_sate, it seems normal when the first batch is processed. But suddenly the loss increased sharply and the accuracy became very low.

Its loss suddenly became increased. How did this happen? I had no other way so anyway continued the training, but I don't think the loss is decreasing well even though it is a trained model over 100 epochs...

When I searched for similar issues, they told the problem was that the optimizer was not saved (Because the reference github page didn't save the optimizer, so did I. Is it true?

My losses after loading

...

ANSWER

Answered 2020-Oct-27 at 17:03

if you see this line! you are Decaying the learning rate of each parameter group by gamma. This has altered your learning rate as you had reached 100th epoch. and moreover you had not saved your optimizer state while saving your model.
This made your code to start with the starting lr i.e 0.1 after resuming your training. And this spiked your loss again.

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Source https://stackoverflow.com/questions/64554881

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

Vulnerabilities

No vulnerabilities reported

Install Face_Pytorch

You can download it from GitHub.
You can use Face_Pytorch 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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