inat_comp_2018 | CNN training code for iNaturalist 2018 image | Machine Learning library

 by   macaodha Python Version: Current License: No License

kandi X-RAY | inat_comp_2018 Summary

kandi X-RAY | inat_comp_2018 Summary

inat_comp_2018 is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. inat_comp_2018 has no bugs, it has no vulnerabilities and it has low support. However inat_comp_2018 build file is not available. You can download it from GitHub.

CNN training code for iNaturalist 2018 image classification competition
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              inat_comp_2018 has a low active ecosystem.
              It has 69 star(s) with 20 fork(s). There are 7 watchers for this library.
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              It had no major release in the last 6 months.
              There are 0 open issues and 3 have been closed. On average issues are closed in 0 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of inat_comp_2018 is current.

            kandi-Quality Quality

              inat_comp_2018 has no bugs reported.

            kandi-Security Security

              inat_comp_2018 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              inat_comp_2018 does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
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              Without a license, all rights are reserved, and you cannot use the library in your applications.

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              inat_comp_2018 releases are not available. You will need to build from source code and install.
              inat_comp_2018 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.

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            inat_comp_2018 Key Features

            No Key Features are available at this moment for inat_comp_2018.

            inat_comp_2018 Examples and Code Snippets

            No Code Snippets are available at this moment for inat_comp_2018.

            Community Discussions

            QUESTION

            How to load and use a pretained PyTorch InceptionV3 model to classify an image
            Asked 2018-Dec-19 at 06:13

            I have the same problem as How can I load and use a PyTorch (.pth.tar) model which does not have an accepted answer or one I can figure out how to follow the advice given.

            I'm new to PyTorch. I am trying to load the pretrained PyTorch model referenced here: https://github.com/macaodha/inat_comp_2018

            I'm pretty sure I am missing some glue.

            ...

            ANSWER

            Answered 2018-Dec-19 at 06:13
            Problem

            Your model isn't actually a model. When it is saved, it contains not only the parameters, but also other information about the model as a form somewhat similar to a dict.

            Therefore, torch.load("iNat_2018_InceptionV3.pth.tar") simply returns dict, which of course does not have an attribute called predict.

            Source https://stackoverflow.com/questions/53844826

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

            Vulnerabilities

            No vulnerabilities reported

            Install inat_comp_2018

            You can download it from GitHub.
            You can use inat_comp_2018 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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            https://github.com/macaodha/inat_comp_2018.git

          • CLI

            gh repo clone macaodha/inat_comp_2018

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            git@github.com:macaodha/inat_comp_2018.git

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