supervising-ui | Web UI for labelling dataset for supervised learning | Data Labeling library

 by   USCDataScience Python Version: Current License: Apache-2.0

kandi X-RAY | supervising-ui Summary

kandi X-RAY | supervising-ui Summary

supervising-ui is a Python library typically used in Artificial Intelligence, Data Labeling applications. supervising-ui has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

This project has a web interface to label training data for machine learning task. As of now it can allow you to easily label images with one or many labels.

            kandi-support Support

              supervising-ui has a low active ecosystem.
              It has 72 star(s) with 23 fork(s). There are 7 watchers for this library.
              It had no major release in the last 6 months.
              There are 1 open issues and 4 have been closed. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of supervising-ui is current.

            kandi-Quality Quality

              supervising-ui has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              supervising-ui 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

              supervising-ui releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              supervising-ui saves you 97 person hours of effort in developing the same functionality from scratch.
              It has 247 lines of code, 17 functions and 3 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed supervising-ui and discovered the below as its top functions. This is intended to give you an instant insight into supervising-ui implemented functionality, and help decide if they suit your requirements.
            • Render the next unlabelled record
            • Render the next record
            • Return the overall status of the experiment
            • Get the count of the given query
            • Return the next unlabelled record
            • Query the database
            • Get a record by url
            • Update a record
            • Update record
            • Download and return a text response
            Get all kandi verified functions for this library.

            supervising-ui Key Features

            No Key Features are available at this moment for supervising-ui.

            supervising-ui Examples and Code Snippets

            No Code Snippets are available at this moment for supervising-ui.

            Community Discussions


            How can I do this split process in Python?
            Asked 2021-Dec-30 at 14:06

            I'm trying to make a data labeling in a table, and I need to do it in such a way that, in each row, the index is repeated, however, that in each column there is another Enum class.

            What I've done so far is make this representation with the same enumerator class.

            A solution using the column separately as a list would also be possible. But what would be the best way to resolve this?



            Answered 2021-Dec-30 at 13:57

            Instead of using Enum you can use a dict mapping. You can avoid loops if you flatten your dataframe:



            Replacing a character with a space and dividing the string into two words in R
            Asked 2020-Nov-18 at 07:32

            I have a dataframe that contains a column that includes strings separeted with semi-colons and it is followed by a space. But unfortunately in some of the strings there is a semi-colon that is not followed by a space.

            In this case, This is what i'd like to do: If there is a space after the semi-colon we do not need a change. However if there are letters before and after the semi-colon, we should change semi-colon with space

            i have this:



            Answered 2020-Nov-16 at 07:24


            Azure ML FileDataset registers, but cannot be accessed for Data Labeling project
            Asked 2020-Oct-28 at 20:31

            Objective: Generate a down-sampled FileDataset using random sampling from a larger FileDataset to be used in a Data Labeling project.

            Details: I have a large FileDataset containing millions of images. Each filename contains details about the 'section' it was taken from. A section may contain thousands of images. I want to randomly select a specific number of sections and all the images associated with those sections. Then register the sample as a new dataset.

            Please note that the code below is not a direct copy and paste as there are elements such as filepaths and variables that have been renamed for confidentiality reasons.



            Answered 2020-Oct-27 at 22:39

            Is the data behind virtual network by any chance?


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


            No vulnerabilities reported

            Install supervising-ui

            You can download it from GitHub.
            You can use supervising-ui 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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