Software Alternatives, Accelerators & Startups

OpenCV VS Labelbox

Compare OpenCV VS Labelbox and see what are their differences

OpenCV logo OpenCV

OpenCV is the world's biggest computer vision library

Labelbox logo Labelbox

Build computer vision products for the real world
  • OpenCV Landing page
    Landing page //
    2023-07-29
  • Labelbox Landing page
    Landing page //
    2023-08-20

A complete solution for your training data problem with fast labeling tools, human workforce, data management, a powerful API and automation features.

OpenCV features and specs

  • Comprehensive Library
    OpenCV offers a wide range of tools for various aspects of computer vision, including image processing, machine learning, and video analysis.
  • Cross-Platform Compatibility
    OpenCV is designed to run on multiple platforms, including Windows, Linux, macOS, Android, and iOS, which makes it versatile for development across different environments.
  • Open Source
    Being open-source, OpenCV is freely available for use and allows developers to inspect, modify, and enhance the code according to their needs.
  • Large Community Support
    A large community of developers and researchers actively contributes to OpenCV, providing extensive support, tutorials, forums, and continuously updated documentation.
  • Real-Time Performance
    OpenCV is highly optimized for real-time applications, making it suitable for performance-critical tasks in various industries such as robotics and interactive installations.
  • Extensive Integration
    OpenCV can easily be integrated with other libraries and frameworks such as TensorFlow, PyTorch, and OpenCL, enhancing its capabilities in deep learning and GPU acceleration.
  • Rich Collection of examples
    OpenCV provides a large number of example codes and sample applications, which can significantly reduce the learning curve for beginners.

Possible disadvantages of OpenCV

  • Steep Learning Curve
    Due to the vast array of functionalities and the complexity of some of its advanced features, beginners may find it challenging to learn and use effectively.
  • Documentation Gaps
    While the documentation is extensive, it can sometimes be incomplete or outdated, requiring users to rely on community forums or external sources for solutions.
  • Resource Intensive
    Some functions and algorithms in OpenCV can be quite resource-intensive, requiring significant processing power and memory, which can be a limitation for low-end devices.
  • Limited High-Level Abstractions
    OpenCV provides a wealth of low-level functions, but it may lack higher-level abstractions and frameworks, necessitating more hands-on coding and algorithm development.
  • Dependency Management
    Setting up and managing dependencies can be cumbersome, especially when integrating OpenCV with other libraries or on certain operating systems.
  • Backward Compatibility Issues
    With frequent updates and new versions, backward compatibility can sometimes be problematic, potentially breaking existing code when updating.

Labelbox features and specs

  • 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.

Possible disadvantages of Labelbox

  • Cost
    Labelbox can be expensive, especially for small teams or startups. The cost might be prohibitive for those with limited budgets.
  • Learning Curve
    Despite its user-friendly interface, some advanced features have a learning curve, requiring time and training to leverage the platform's full potential.
  • Dependency on Internet Connection
    Since Labelbox is a cloud-based platform, a stable internet connection is required. Any internet issues can disrupt workflow and access.
  • Limited Offline Capabilities
    The platform's reliance on being cloud-based means it offers limited offline capabilities, restricting users who might need to work without internet access.
  • Feature Limitations on Basic Plans
    Some advanced features and integrations are only available in higher-tier plans, which can be restrictive for users on basic subscription plans.
  • Integration Complexity
    While powerful, API integrations can be complex and may require technical expertise to set up and maintain effectively.

OpenCV videos

AI Courses by OpenCV.org

More videos:

  • Review - Practical Python and OpenCV

Labelbox videos

Review App : Labelbox

More videos:

  • Review - Machine Learning Support Engineer at Labelbox
  • Review - Bounding box annotation with Labelbox

Category Popularity

0-100% (relative to OpenCV and Labelbox)
Data Science And Machine Learning
Data Labeling
0 0%
100% 100
Data Science Tools
100 100%
0% 0
Image Annotation
0 0%
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare OpenCV and Labelbox

OpenCV Reviews

7 Best Computer Vision Development Libraries in 2024
From the widespread adoption of OpenCV with its extensive algorithmic support to TensorFlow's role in machine learning-driven applications, these libraries play a vital role in real-world applications such as object detection, facial recognition, and image segmentation.
10 Python Libraries for Computer Vision
OpenCV is the go-to library for computer vision tasks. It boasts a vast collection of algorithms and functions that facilitate tasks such as image and video processing, feature extraction, object detection, and more. Its simple interface, extensive documentation, and compatibility with various platforms make it a preferred choice for both beginners and experts in the field.
Source: clouddevs.com
Top 8 Alternatives to OpenCV for Computer Vision and Image Processing
OpenCV is an open-source computer vision and machine learning software library that was first released in 2000. It was initially developed by Intel, and now it is maintained by the OpenCV Foundation. OpenCV provides a set of tools and software development kits (SDKs) that help developers create computer vision applications. It is written in C++, but it supports several...
Source: www.uubyte.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
These are some of the most basic operations that can be performed with the OpenCV on an image. Apart from this, OpenCV can perform operations such as Image Segmentation, Face Detection, Object Detection, 3-D reconstruction, feature extraction as well.
Source: neptune.ai
5 Ultimate Python Libraries for Image Processing
Pillow is an image processing library for Python derived from the PIL or the Python Imaging Library. Although it is not as powerful and fast as openCV it can be used for simple image manipulation works like cropping, resizing, rotating and greyscaling the image. Another benefit is that it can be used without NumPy and Matplotlib.

Labelbox Reviews

  1. Sharon
    · manager at Mcormicki ·
    Unreliable

    Service goes down often. Very slow team. Slow support.

    🏁 Competitors: Diffgram
    👎 Cons:    Slow|Bad support

Top Video Annotation Tools Compared 2022
However, Labelbox only accepts .mp4 files into their platform, and only their most basic annotation modes have the full scope of video annotation options. When annotating videos with segmentation masks, annotators must step through each frame to view their work – there is no playback option.
Source: innotescus.io

Social recommendations and mentions

Based on our record, OpenCV should be more popular than Labelbox. It has been mentiond 59 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

OpenCV mentions (59)

  • Top Programming Languages for AI Development in 2025
    Ideal For: Computer vision, NLP, deep learning, and machine learning. - Source: dev.to / 7 days ago
  • Why 2024 Was the Best Year for Visual AI (So Far)
    Almost everyone has heard of libraries like OpenCV, Pytorch, and Torchvision. But there have been incredible leaps and bounds in other libraries to help support new tasks that have helped push research even further. It would be impossible to thank each and every project and the thousands of contributors who have helped make the entire community better. MedSAM2 has been helping bring the awesomeness of SAM2 to the... - Source: dev.to / 4 months ago
  • 20 Open Source Tools I Recommend to Build, Share, and Run AI Projects
    OpenCV is an open-source computer vision and machine learning software library that allows users to perform various ML tasks, from processing images and videos to identifying objects, faces, or handwriting. Besides object detection, this platform can also be used for complex computer vision tasks like Geometry-based monocular or stereo computer vision. - Source: dev.to / 6 months ago
  • F1 FollowLine + HSV filter + PID Controller
    This library is used for image and video processing, offering functions for tasks like object detection, filtering, and transformations in computer vision. - Source: dev.to / 7 months ago
  • Built in Days, Acquired for $20K: The NuloApp Story
    First of all, OpenCV, an open-source computer vision library, was used as the main editing tool. This is how NuloApp is able to get the correct aspect ratio for smartphone content, and do other cool things like centering the video on the speaker so that they aren't out of frame when the aspect ratio is changed. - Source: dev.to / 7 months ago
View more

Labelbox mentions (8)

  • Ask HN: Who is hiring? (October 2022)
    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 / over 2 years ago
  • Model Assisted Labeling using Label box
    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 I’m honestly still very confused. Any help... Source: almost 3 years ago
  • What MDR is doing: a Machine Learning perspective
    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: almost 3 years ago
  • [D] Any recommendations for image annotation software .
    LabelBox - they provide free versions for research. Source: almost 3 years ago
  • Video box annotation tool for Google Cloud Video Intelligence autoML CSV format ?
    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: about 3 years ago
View more

What are some alternatives?

When comparing OpenCV and Labelbox, you can also consider the following products

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

Supervisely - Supervisely helps people with and without machine learning expertise to create state-of-the-art...

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Playment - Playment is a fully-managed solution offering training data for AI, transcription, data collection and enrichment services at scale.

NumPy - NumPy is the fundamental package for scientific computing with Python

CloudFactory - Human-powered Data Processing for AI and Automation