unisal | Unified Image and Video Saliency Modeling | Map library

 by   rdroste Python Version: Current License: Apache-2.0

kandi X-RAY | unisal Summary

kandi X-RAY | unisal Summary

unisal is a Python library typically used in Telecommunications, Media, Media, Entertainment, Geo, Map applications. unisal has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However unisal build file is not available. You can download it from GitHub.

Unified Image and Video Saliency Modeling (ECCV 2020)
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            kandi-support Support

              unisal has a low active ecosystem.
              It has 105 star(s) with 33 fork(s). There are 7 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 1 open issues and 10 have been closed. On average issues are closed in 119 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of unisal is current.

            kandi-Quality Quality

              unisal has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              unisal 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.

            kandi-Reuse Reuse

              unisal releases are not available. You will need to build from source code and install.
              unisal 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.
              unisal saves you 1345 person hours of effort in developing the same functionality from scratch.
              It has 3014 lines of code, 196 functions and 10 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed unisal and discovered the below as its top functions. This is intended to give you an instant insight into unisal implemented functionality, and help decide if they suit your requirements.
            • Compute the backbone features
            • Make Gaussian maps
            • Return a list of gaussian maps
            • Log softmax
            • Run fine tuning
            • Exports all scalars
            • Train the model
            • Fit the model
            • Make a dropout connection
            • N_images dictionary
            • Get the data file for a given phase
            • Return the data file for a given vid
            • Load a dataset
            • Load a model from a directory
            • Get a sequence from a given vid
            • Return a dictionary of configuration parameters
            • N_images_dict
            • Measure model size
            • Generate predictions for sources
            • Measure the runtime
            • Forward pass through the layer
            • Loads jpeg files
            • Performs forward computation
            • Generate predictions for each example
            • Create a convolutional convolution layer
            • Get the optimizer
            Get all kandi verified functions for this library.

            unisal Key Features

            No Key Features are available at this moment for unisal.

            unisal Examples and Code Snippets

            No Code Snippets are available at this moment for unisal.

            Community Discussions

            Trending Discussions on unisal

            QUESTION

            Pytorch CPU CUDA device load without gpu
            Asked 2021-Jun-11 at 12:41

            I found this nice code Pytorch mobilenet which I cant get running on a CPU. https://github.com/rdroste/unisal

            I am new to Pytorch so I am not shure what to do.

            In line 174 of the module train.py the device is set:

            ...

            ANSWER

            Answered 2021-Jun-11 at 08:55

            In https://pytorch.org/tutorials/beginner/saving_loading_models.html#save-on-gpu-load-on-cpu you'll see there's a map_location keyword argument to send weights to the proper device:

            Source https://stackoverflow.com/questions/67934005

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

            Vulnerabilities

            No vulnerabilities reported

            Install unisal

            You can download it from GitHub.
            You can use unisal 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/rdroste/unisal.git

          • CLI

            gh repo clone rdroste/unisal

          • sshUrl

            git@github.com:rdroste/unisal.git

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