A complete solution for your training data problem with fast labeling tools, human workforce, data management, a powerful API and automation features.
User-Friendly Interface
Labelbox features a clean, intuitive interface that makes it easy for users to navigate and manage their projects, even for those who are new to data labeling.
Collaboration Tools
The platform includes robust collaboration tools, allowing multiple team members to work together efficiently on the same project and oversee progress in real-time.
API Integration
Labelbox provides a powerful API that enables seamless integration with other tools and systems, which can help automate workflows and enhance productivity.
Comprehensive Annotations
The platform supports a wide range of annotation types including bounding boxes, polygons, and more. This flexibility allows users to create detailed and precise annotations for diverse use cases.
Scalability
Labelbox is designed to scale with your needs, making it suitable for small projects as well as large enterprises requiring high-volume data labeling.
Quality Assurance Features
Labelbox includes features for quality control and assurance, such as review workflows and consensus scoring, to ensure the accuracy and reliability of labeled data.
Data Security
With strong security protocols in place, Labelbox ensures that sensitive data is protected, meeting compliance standards for various industries.
Service goes down often. Very slow team. Slow support.
Labelbox is considered a good tool for data labeling, particularly in the context of machine learning and artificial intelligence projects.
We have collected here some useful links to help you find out if Labelbox is good.
Check the traffic stats of Labelbox on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of Labelbox on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of Labelbox's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of Labelbox on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about Labelbox on Reddit. This can help you find out how popualr the product is and what people think about it.
Cursor's security agents primarily operate in the first dimension, catching vulnerabilities in code. That's valuable and necessary work. But as you'll see in the walkthrough below, the other two dimensions matter just as much, especially at enterprise scale. And the organizations getting the best results, like Labelbox, which cleared a multi-year vulnerability backlog by running Cursor and Snyk together, are the... - Source: dev.to / 4 months ago
Use tools like Weights & Biases, Labelbox, or Maximโs data engine to version your datasets, track changes, and continuously add new edge cases and user feedback. - Source: dev.to / 12 months ago
Labelbox | Remote | Frontend / WebGL, Backend, Engineering Managers | https://labelbox.com Labelbox is building the training data platform to power breakthroughs in machine learning. We provide an end to end solutions for the full AI lifecycle from creating catalogs of unstructured data all the way to building the tools for humans to label the data to teach machines. Why choose us? - Source: Hacker News / almost 4 years ago
Hey, I have currently developed a U-Net model for segmentation and I am trying to use the model assisted labeling feature on LabelBox to annotate some masks, so I can save time on relabeling. I am just wondering if anyone is familiar with this feature or can give me a step by step guideline on how to go about doing this. I went through the examples on their GitHub but Iโm honestly still very confused. Any help... Source: almost 4 years ago
By now, I hope you see where I'm going with this. What is MDR doing? They're creating the labelled data used to train severance chips. They get a raw download of human brains in encoded format, and go about manually labelling the different pieces based on their most basic elements. Then, based on this manually labelled data, an algorithm can be trained to create a severance chip. MDR is basically Labelbox for... Source: about 4 years ago
LabelBox - they provide free versions for research. Source: about 4 years ago
Doing some progress, labelbox.com allows me to do the Video annotation, and access all data through python SDK/API... Working on converting myself to CSV GCP format :-). Source: over 4 years ago
Labelbox is a training data platform that optimizes the training data iteration loop to make it more efficient. With Labelbox, you can annotate data, diagnose model errors and better understand performance, and prioritize your data. It also helps fully remote teams work more seamlessly when working with training data, facilitating faster progress and collaboration. - Source: dev.to / over 4 years ago
Labelbox if you have budget: https://labelbox.com/. Source: almost 5 years ago
Ok, so I tried comparing 4 of the better data annotation tools like dLabel.org, CVAT.com, SuperAnnotate.com and Labelbox.com . I tried them all as thoroughly as I could and I probably missed some things so apologies in advance for that! Let me know what I missed in the comment. Btw, I'm Amir and I've worked most of my data-labeling career at dLabel.org. Source: about 5 years ago
Labelbox has emerged as a significant player in the landscape of data labeling and image annotation tools, garnering both praise and criticism from the AI and data science community. Its prominent position amongst competitors such as AWS SageMaker Ground Truth, Scale AI, and Supervisely is indicative of its robust feature set and offerings designed to streamline the AI lifecycle. However, opinions remain mixed, with feedback highlighting both its strengths and areas for improvement.
On the positive side, Labelbox is lauded for providing an end-to-end solution that facilitates the entire AI lifecycle, from cataloging unstructured data to enabling human labeling for machine learning development. This comprehensive approach appeals particularly to organizations seeking to optimize their training data processes and enhance collaboration within remote teams. The platform's integration capabilities, exemplified by access through a Python SDK/API, allow users to seamlessly incorporate it into existing workflows, thereby improving efficiency and collaboration on training data projects.
Moreover, the integration of advanced features, such as model-assisted labeling, has positioned Labelbox as a forward-thinking solution for teams aiming to leverage AI capabilities in the data annotation process. This feature, although appreciated, has also been a source of confusion for some users, indicating a potential need for clearer guidance and more user-friendly documentation to support adoption and usage.
Despite these strengths, some limitations have been identified. Notably, the platform's video annotation capabilities are somewhat restricted, as it currently only supports .mp4 files and lacks a playback option for segmentation mask annotation, requiring annotators to step through each frame individually. This can be cumbersome for projects necessitating complex video annotations and may deter users who require more comprehensive video handling capabilities.
In the realm of pricing and accessibility, Labelbox offers free versions for research purposes, making it an attractive option for academic institutions and small teams. However, potential users need to consider budget implications for scaling use cases, as indicated by community discussions highlighting its suitability for organizations able to invest in premium features.
Overall, Labelbox continues to strengthen its reputation as a versatile, if somewhat specialized, tool in the data annotation and AI fields. Its capabilities are well-suited to teams seeking a comprehensive, integrated platform for data handling and annotation. Yet, as with any complex product, careful consideration is needed to assess its fit concerning specific project requirements, especially for tasks involving sophisticated video annotations. As AI and machine learning continue to evolve, so too will the demands placed on platforms like Labelbox, requiring ongoing innovation and enhancement to meet diverse user needs effectively.
Do you know an article comparing Labelbox to other products?
Suggest a link to a post with product alternatives.
Is Labelbox good? This is an informative page that will help you find out. Moreover, you can review and discuss Labelbox here. The primary details have not been verified within the last quarter, and they might be outdated. If you think we are missing something, please use the means on this page to comment or suggest changes. All reviews and comments are highly encouranged and appreciated as they help everyone in the community to make an informed choice. Please always be kind and objective when evaluating a product and sharing your opinion.
