label-studio | Label Studio is a multi-type data labeling | Data Labeling library

 by   heartexlabs Python Version: 1.12.1 License: Apache-2.0

kandi X-RAY | label-studio Summary

kandi X-RAY | label-studio Summary

label-studio is a Python library typically used in Telecommunications, Media, Media, Entertainment, Artificial Intelligence, Data Labeling applications. label-studio has build file available, it has a Permissive License and it has medium support. However label-studio has 111 bugs and it has 9 vulnerabilities. You can install using 'pip install label-studio' or download it from GitHub, PyPI.

Label Studio is an open source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats. It can be used to prepare raw data or improve existing training data to get more accurate ML models. Have a custom dataset? You can customize Label Studio to fit your needs. Read an introductory blog post to learn more.

            kandi-support Support

              label-studio has a medium active ecosystem.
              It has 13344 star(s) with 1576 fork(s). There are 157 watchers for this library.
              There were 7 major release(s) in the last 6 months.
              There are 437 open issues and 1141 have been closed. On average issues are closed in 69 days. There are 137 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of label-studio is 1.12.1

            kandi-Quality Quality

              label-studio has 111 bugs (1 blocker, 0 critical, 18 major, 92 minor) and 122 code smells.

            kandi-Security Security

              label-studio has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              label-studio code analysis shows 9 unresolved vulnerabilities (7 blocker, 2 critical, 0 major, 0 minor).
              There are 16 security hotspots that need review.

            kandi-License License

              label-studio 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

              label-studio releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              label-studio saves you 7265 person hours of effort in developing the same functionality from scratch.
              It has 15021 lines of code, 669 functions and 194 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

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            label-studio Key Features

            No Key Features are available at this moment for label-studio.

            label-studio Examples and Code Snippets

            Start using Docker Compose
            Pythondot img1Lines of Code : 144dot img1License : Permissive (Apache-2.0)
            copy iconCopy
            # Specify the path to the license file. 
            # Alternatively, it can be a URL like LICENSE=
            # Specify the FQDN name with port if differs from 80
            Text classification with Scikit-Learn-Create a model script
            Pythondot img2Lines of Code : 121dot img2License : Permissive (Apache-2.0)
            copy iconCopy
            import pickle
            import os
            import numpy as np
            from sklearn.linear_model import LogisticRegression
            from sklearn.feature_extraction.text import TfidfVectorizer
            from sklearn.pipeline import make_pipeline
            from label_studio_ml.model import LabelStudioMLBas  
            Transfer learning for images with PyTorch-Create a model script
            Pythondot img3Lines of Code : 119dot img3License : Permissive (Apache-2.0)
            copy iconCopy
            import torch
            import torch.nn as nn
            import torch.optim as optim
            import time
            from import Dataset, DataLoader
            from torchvision import datasets, models, transforms
            device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')  
            Web scraping python <span>2</span>, <span>2</span>
            Pythondot img4Lines of Code : 4dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            Property_beds = response.css('span.b6a29bc0[aria-label=Beds]::text').extract()
            response.css('span.b6a29bc0[aria-label=Beds], span.b6a29bc0[aria-label=Studio]').getall()

            Community Discussions


            How to uninstall label studio?
            Asked 2021-Dec-07 at 23:54

            I accidentally installed label studio in a wrong directory using the follow command:

            docker run -it -p 8080:8080 -v `pwd`/mydata:/label-studio/data heartexlabs/label-studio:latest

            Is there any way to uninstall or remove it?



            Answered 2021-Dec-07 at 23:54

            Did pwd have anything else except for what the image installed? If not, simply delete the container and the contents that was created on your main filesystem.

            If you did have something in the pwd and the contents mixed, this is a bit trickier. You can create an empty directory, then run the image in the empty directory. After finishing you can see what directories and files got created and compare with the pwd one by one.



            Install Label Studio in Google Cloud and make it available with the public IP
            Asked 2021-Mar-15 at 15:12

            I have a VM instance in Google Cloud with Ubuntu 20.04 LTS, I set it to allow HTTP traffic.

            I need to setup Label Studio ( in this VM so anyone can access it by just typing the VM public IP.

            I already tried building it with docker:



            Answered 2021-Mar-15 at 15:12

            Try this:
            sudo docker run --rm -d -p 80:8080 -v `pwd`/my_project:/label-studio/my_project --name label-studio heartexlabs/label-studio:latest label-studio start my_project --init
            I was able to access it from External IP with this.



            Use a vanilla JavaScript package in React app
            Asked 2020-Jul-09 at 00:08

            I am aiming to use a Vanilla JavaScript package in a more sophisticated React app to build additional logic around the JavaScript package.

            The JavaScript library is LabelStudio and docs can be found here:

            However, when I try to import the LabelStudio I get an error saying Module not found: Can't resolve 'label-studio' , as described here

            Since my understanding of frontend code is limited, I am not sure whether this is something the developers did not expected users to do and just wanted them to use the entire library and customized instead of using the library as a component. My idea was to use the library as in the vanilla javascript example here:



            Answered 2020-Jul-09 at 00:08

            I don't have a solution making the npm module label-studio to work. I tried importing the dist file instead, but it errors


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


            No vulnerabilities reported

            Install label-studio

            Official Label Studio docker image is here and it can be downloaded with docker pull. Run Label Studio in a Docker container and access it at http://localhost:8080. You can find all the generated assets, including SQLite3 database storage label_studio.sqlite3 and uploaded files, in the ./mydata directory.
            You can run the latest Label Studio version locally without installing the package with pip.
            If you see any errors during installation, try to rerun the installation.
            Connect your favorite machine learning model using the Label Studio Machine Learning SDK. Follow these steps:.
            Start your own machine learning backend server. See more detailed instructions.
            Connect Label Studio to the server on the model page found in project settings.
            Pre-label your data using model predictions.
            Do online learning and retrain your model while new annotations are being created.
            Do active learning by labeling only the most complex examples in your data.


            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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          • PyPI

            pip install label-studio

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          • CLI

            gh repo clone heartexlabs/label-studio

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