focal-loss | Focal loss for Dense Object Detection | Computer Vision library

 by   unsky Python Version: Current License: No License

kandi X-RAY | focal-loss Summary

kandi X-RAY | focal-loss Summary

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

Focal loss for Dense Object Detection
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            kandi-support Support

              focal-loss has a low active ecosystem.
              It has 474 star(s) with 129 fork(s). There are 24 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 23 open issues and 3 have been closed. On average issues are closed in 94 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of focal-loss is current.

            kandi-Quality Quality

              focal-loss has 0 bugs and 0 code smells.

            kandi-Security Security

              focal-loss has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              focal-loss code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              focal-loss does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              OutlinedDot
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              focal-loss releases are not available. You will need to build from source code and install.
              focal-loss has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              focal-loss saves you 3158 person hours of effort in developing the same functionality from scratch.
              It has 6795 lines of code, 409 functions and 79 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed focal-loss and discovered the below as its top functions. This is intended to give you an instant insight into focal-loss implemented functionality, and help decide if they suit your requirements.
            • Get the symbol
            • Creates a convolutional convolution layer
            • Get convolution layer
            • Compute the RPN score
            • Run kitti evaluation
            • Parse a RECORD file
            • Compute the vocab
            • Train RNN model
            • Fit the model
            • Train RPN model
            • Get batch data
            • Visualize a 2D image
            • Get a batch of data
            • Locate CUDA
            • Binds all executors
            • Visualize dpsroi offsets
            • Forward computation
            • Create a logger
            • Get a list of outputs
            • Get the gradients of the gradients
            • Save checkpoint
            • Get segmentation traination data
            • Set one or more states
            • Combine model checkpoints
            • Get segmentation test data
            • Parse command line arguments
            Get all kandi verified functions for this library.

            focal-loss Key Features

            No Key Features are available at this moment for focal-loss.

            focal-loss Examples and Code Snippets

            Calculate the loss of the loss function .
            pythondot img1Lines of Code : 26dot img1License : Permissive (MIT License)
            copy iconCopy
            def _neg_loss(pred, gt):
              ''' Modified focal loss. Exactly the same as CornerNet.
                  Runs faster and costs a little bit more memory
                Arguments:
                  pred (batch x c x h x w)
                  gt_regr (batch x c x h x w)
              '''
              pos_inds = gt.eq(1).float(  
            Compute negative loss .
            pythondot img2Lines of Code : 23dot img2License : Permissive (MIT License)
            copy iconCopy
            def _slow_neg_loss(pred, gt):
              '''focal loss from CornerNet'''
              pos_inds = gt.eq(1)
              neg_inds = gt.lt(1)
            
              neg_weights = torch.pow(1 - gt[neg_inds], 4)
            
              loss = 0
              pos_pred = pred[pos_inds]
              neg_pred = pred[neg_inds]
            
              pos_loss = torch.log(po  

            Community Discussions

            Trending Discussions on focal-loss

            QUESTION

            Docker: Cannot install specific pip packages
            Asked 2021-Oct-12 at 10:16

            I want to install specific packages in a docker container. For most of them, the pip installation works fine. However, it seems like some of the versions of the packages cannot be found.

            Here's the requirements.txt file from which I'm installing the packages. It was obtained from pip freeze done in PyCharm from Windows.

            Currently, I am trying to install them in Docker working on Ubuntu 20.04.

            ...

            ANSWER

            Answered 2021-Oct-12 at 09:25

            Try to use some other version of ipython.

            Replace the python version in your requirements.txt with:

            ipython==7.24.1

            However, it doesn't have anything to do with docker configuration i.e. --network=host

            Source https://stackoverflow.com/questions/69537812

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

            Vulnerabilities

            No vulnerabilities reported

            Install focal-loss

            You can download it from GitHub.
            You can use focal-loss 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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            CLONE
          • HTTPS

            https://github.com/unsky/focal-loss.git

          • CLI

            gh repo clone unsky/focal-loss

          • sshUrl

            git@github.com:unsky/focal-loss.git

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