Facial-Expression-Recognition.Pytorch | CNN based pytorch implementation on facial expression | Computer Vision library

 by   WuJie1010 Python Version: Current License: MIT

kandi X-RAY | Facial-Expression-Recognition.Pytorch Summary

kandi X-RAY | Facial-Expression-Recognition.Pytorch Summary

Facial-Expression-Recognition.Pytorch is a Python library typically used in Artificial Intelligence, Computer Vision, Pytorch applications. Facial-Expression-Recognition.Pytorch has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However Facial-Expression-Recognition.Pytorch build file is not available. You can download it from GitHub.

A CNN based pytorch implementation on facial expression recognition (FER2013 and CK+), achieving 73.112% (state-of-the-art) in FER2013 and 94.64% in CK+ dataset

            kandi-support Support

              Facial-Expression-Recognition.Pytorch has a medium active ecosystem.
              It has 1493 star(s) with 529 fork(s). There are 33 watchers for this library.
              It had no major release in the last 6 months.
              There are 34 open issues and 100 have been closed. On average issues are closed in 29 days. There are 4 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of Facial-Expression-Recognition.Pytorch is current.

            kandi-Quality Quality

              Facial-Expression-Recognition.Pytorch has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              Facial-Expression-Recognition.Pytorch is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              Facial-Expression-Recognition.Pytorch releases are not available. You will need to build from source code and install.
              Facial-Expression-Recognition.Pytorch has no build file. You will be need to create the build yourself to build the component from source.
              Facial-Expression-Recognition.Pytorch saves you 619 person hours of effort in developing the same functionality from scratch.
              It has 1440 lines of code, 92 functions and 17 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed Facial-Expression-Recognition.Pytorch and discovered the below as its top functions. This is intended to give you an instant insight into Facial-Expression-Recognition.Pytorch implemented functionality, and help decide if they suit your requirements.
            • Convert image to PIL image .
            • Convert a PIL Image into a torch . Image .
            • This function runs the test .
            • Adjust the hue of an image .
            • The private test function .
            • Calculate public test accuracy .
            • Print a progress bar .
            • Train the model .
            • Resize a PIL Image .
            • Crop an image .
            Get all kandi verified functions for this library.

            Facial-Expression-Recognition.Pytorch Key Features

            No Key Features are available at this moment for Facial-Expression-Recognition.Pytorch.

            Facial-Expression-Recognition.Pytorch Examples and Code Snippets

            No Code Snippets are available at this moment for Facial-Expression-Recognition.Pytorch.

            Community Discussions

            Trending Discussions on Facial-Expression-Recognition.Pytorch


            nn.Linear should be mismatch, but it works successfully
            Asked 2019-Apr-02 at 08:26

            I'm confused about the in-feature of nn.linear. For out-feature of the model VGG-19's last nn.MaxPool2d, the tensor size is (512, 7, 7). The model below uses pooling function and resizes the tensor to (512, 49), then uses nn.linear(512, 7) directly. Why can't it work successfully without mismatch problem?




            Answered 2019-Apr-02 at 08:01

            Why is the assumption that this code works? I tested it, and got the following shapes, and the expected size mismatch error.

            Source https://stackoverflow.com/questions/55462619

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


            No vulnerabilities reported

            Install Facial-Expression-Recognition.Pytorch

            You can download it from GitHub.
            You can use Facial-Expression-Recognition.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.


            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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