FocalLoss | Caffe implementation of FAIR paper | Machine Learning library

by chuanqi305 C++ Version: Current License: MIT

X-Ray Key Features Code Snippets(1)Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | FocalLoss Summary

FocalLoss is a C++ library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. FocalLoss has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Caffe implementation of FAIR paper "Focal Loss for Dense Object Detection" for SSD.

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Quality

Security

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Support

FocalLoss has a low active ecosystem.

It has 173 star(s) with 58 fork(s). There are 20 watchers for this library.

It had no major release in the last 6 months.

There are 9 open issues and 2 have been closed. On average issues are closed in 116 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of FocalLoss is current.

Quality

FocalLoss has no bugs reported.

Security

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

License

FocalLoss 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

FocalLoss 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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FocalLoss Key Features

No Key Features are available at this moment for FocalLoss.

FocalLoss Examples and Code Snippets

Initialize FocalLoss .

python

Lines of Code : 3

License : Permissive (MIT License)

Copy

def __init__(self):
    super(FocalLoss, self).__init__()
    self.neg_loss = _neg_loss

Community Discussions

Trending Discussions on FocalLoss

How to use custom loss function for keras

Custom loss function (focal loss) input size error in Keras

Fintuning custom keras model

Implementation of Focal loss for multi label classification

QUESTION

How to use custom loss function for keras

Asked 2020-Aug-06 at 07:42

I have recently came across the Focal loss function and heard it's mainly used in imbalanced dataset. So i just gave it a try on Cifar10 dataset by using this simple Focal loss function i found online(For Keras).

I am continuously facing an error which i have mentioned at the end. I have tried several methods to resolve it but no luck. Please see to it, i really appreciate your help. Thank you!

Focal Loss

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ANSWER

Answered 2020-Aug-06 at 07:42

the problem is related to your target type, they are int8 but you need to cast the to float32. I do it inside the loss, where I removed also the flatten part which is a mistake

Source https://stackoverflow.com/questions/63278000

QUESTION

Custom loss function (focal loss) input size error in Keras

Asked 2020-May-14 at 15:05

I am using a neutral network to do multi-class classification. There're 3 imbalanced classes so I'd like to use the focal loss to handle the in-balance. So I use custom loss function to fit in Keras sequential model. I tried multiple versions of code for focal loss function I found online, but they return the same error message, basically saying the input size is the bath size while expected 1. Could anyone have a look at the issue and let me know if you can fix it? I really appreciate it!!!

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ANSWER

Answered 2019-Nov-06 at 12:31

Keras loss functions take a batch of predictions and training data, and use them to produce a tensor of loss. One way this can be implemented is simply by defining a function with two tensor inputs which returns a number, like so

Source https://stackoverflow.com/questions/58728659

QUESTION

Fintuning custom keras model

Asked 2019-Aug-29 at 04:13

I have a keras model which is trained on 5 classes,The final layers of the model look like so

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ANSWER

Answered 2019-Aug-29 at 04:13

Here,

Source https://stackoverflow.com/questions/57702247

QUESTION

Implementation of Focal loss for multi label classification

Asked 2019-Aug-24 at 10:45

trying to write focal loss for multi-label classification

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ANSWER

Answered 2019-Aug-24 at 10:45

You shouldn't inherit from torch.nn.Module as it's designed for modules with learnable parameters (e.g. neural networks).

Just create normal functor or function and you should be fine.

BTW. If you inherit from it, you should call super().__init__() somewhere in your __init__().

Source https://stackoverflow.com/questions/57635169

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

Vulnerabilities

No vulnerabilities reported

Install FocalLoss

You can download it from GitHub.

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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