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Scikit-learn VS Segments.ai

Compare Scikit-learn VS Segments.ai and see what are their differences

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Scikit-learn logo Scikit-learn

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

Segments.ai logo Segments.ai

Multi-sensor labeling platform for robotics and autonomous driving
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • Segments.ai Homepage
    Homepage //
    2024-04-12

Segments.ai is a fast and accurate data labeling platform for multi-sensor data annotation. You can obtain segmentation labels, vector labels, and more via the intuitive labeling interfaces for images, videos, and 3D point clouds.

Build your clever annotation workflow exactly how you want, with the flexibility you need to get the job done quickly and efficiently. Segments.ai is a self-serve platform with dedicated support from our core team of engineers when you need it.

Onboard your workforce or use one of our workforce partners. Our management tools make it easy to label and review large datasets together.

Get started with a free trial today at https://segments.ai/join

Scikit-learn

Website
scikit-learn.org
Pricing URL
-
$ Details
Platforms
-
Release Date
-
Edit details

Segments.ai

Website
segments.ai
Pricing URL
Official Segments.ai Pricing
$ Details
freemium €800.0 / Monthly (Includes 3,600 hours/yr of labeling usage)
Platforms
AWS Azure Python TensorFlow Hugging Face 🤗
Release Date
2020 January
Edit details

Scikit-learn features and specs

  • Ease of Use
    Scikit-learn provides a high-level interface for common machine learning algorithms, making it easy for beginners and professionals to implement complex models with minimal coding.
  • Extensive Documentation and Community Support
    The library has comprehensive documentation and a large, active community. This makes it easy to find tutorials, examples, and solutions to common problems.
  • Integration with Other Libraries
    Scikit-learn integrates well with other scientific computing libraries such as NumPy, SciPy, and pandas, allowing for seamless data manipulation and analysis.
  • Variety of Algorithms
    It offers a wide array of machine learning algorithms for tasks such as classification, regression, clustering, and dimensionality reduction.
  • Performance
    Designed with performance in mind, many of the algorithms are optimized and some even support multicore processing.

Possible disadvantages of Scikit-learn

  • Limited Deep Learning Support
    Scikit-learn is primarily focused on traditional machine learning algorithms and does not offer support for deep learning models, unlike libraries like TensorFlow or PyTorch.
  • Not Ideal for Large-Scale Data
    While Scikit-learn performs well for moderate-sized datasets, it may not be the best choice for extremely large datasets or big data applications.
  • Lack of Online Learning Algorithms
    The library has limited support for online learning algorithms, which are useful for scenarios where data arrives in a stream and model needs to be updated incrementally.
  • Less Flexibility in Customization
    It can be less flexible compared to lower-level libraries when highly customized or specific implementations are needed.
  • Dependency Overhead
    Scikit-learn relies on several other Python libraries like NumPy and SciPy, which might require users to manage multiple dependencies.

Segments.ai features and specs

  • Image Segmentation
    Semantic Segmentation / Instance Segmentation / Panoptic Segmentation
  • Image Vector Labeling
    Bounding Boxes / Polygons / Polylines / Keypoints
  • Point Cloud Segmentation
    Semantic Segmentation / Instance Segmentation / Panoptic Segmentation
  • Point Cloud Vector Labeling
    Cuboids / Polygons / Polylines / Keypoints
  • ML-powered labeling tools
    SuperPixel 2.0 / Autosegment
  • Multi-sensor fusion
    2D and 3D overlay / 3D to 2D projections
  • Powerful Python SDK
  • Unlimited sized Point Clouds
    Unlimited

Analysis of Scikit-learn

Overall verdict

  • Yes, Scikit-learn is generally regarded as a good library for machine learning, especially for beginners and intermediate users who need reliable tools with efficient implementation of numerous algorithms.

Why this product is good

  • Scikit-learn is considered a good machine learning library because it provides a wide range of state-of-the-art algorithms for supervised and unsupervised learning. It is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. The library is well-documented, easy to use, and has a consistent API that simplifies the integration of different algorithms. Furthermore, there's a strong community and continuous development, which means it is well-maintained and updated regularly with new features and improvements.

Recommended for

  • Beginners learning machine learning concepts and application.
  • Data scientists and engineers looking for a robust and efficient toolkit to build and deploy machine learning models.
  • Researchers who need an easy-to-use library that facilitates the experimentation of various algorithms.
  • Developers who require a seamless, Python-based machine learning library that integrates well with other data analysis tools and environments.

Analysis of Segments.ai

Overall verdict

  • Overall, Segments.ai is considered a good choice for those involved in machine learning and data annotation, particularly in the realm of computer vision. It is especially well-regarded for its user-friendly interface and robust feature set.

Why this product is good

  • Segments.ai is a platform that offers tools for training and managing machine learning models, particularly for computer vision tasks. It provides an interface for data annotation, dataset management, and model management with a focus on collaboration. The platform is known for its intuitive design, scalability, and integrations with various data sources and ML frameworks. The ability to handle large datasets efficiently and integrate seamlessly into existing workflows makes it a valuable tool for both individual practitioners and teams.

Recommended for

  • Data scientists working on computer vision projects
  • Teams requiring collaborative data annotation tools
  • Organizations needing scalable dataset and model management solutions
  • Researchers looking for an efficient tool to manage and annotate large datasets

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

  • Review - Python Machine Learning Review | Learn python for machine learning. Learn Scikit-learn.

Segments.ai videos

+ Add

3D point cloud labeling platform for autonomous vehicles and robotics | Segments ai

Category Popularity

0-100% (relative to Scikit-learn and Segments.ai)

Scikit-learn

Segments.ai

Data Science And Machine Learning
88 88%
Data Science And Machine Learning
12% 12
Data Labeling
0 0%
Data Labeling
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Image Annotation
0 0%
Image Annotation
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 Scikit-learn and Segments.ai

Scikit-learn Reviews

15 data science tools to consider using in 2021
Scikit-learn is an open source machine learning library for Python that's built on the SciPy and NumPy scientific computing libraries, plus Matplotlib for plotting data. It supports both supervised and unsupervised machine learning and includes numerous algorithms and models, called estimators in scikit-learn parlance. Additionally, it provides functionality for model...
Source: searchbusinessanalytics.techtarget.com

Segments.ai Reviews

We have no reviews of Segments.ai yet.
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Social recommendations and mentions

Based on our record, Scikit-learn seems to be more popular. It has been mentiond 31 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.

Scikit-learn mentions (31)

  • Must-Know 2025 Developer’s Roadmap and Key Programming Trends
    Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 4 months ago
  • 🚀 Launching a High-Performance DistilBERT-Based Sentiment Analysis Model for Steam Reviews 🎮🤖
    Scikit-learn (optional): Useful for additional training or evaluation tasks. - Source: dev.to / 6 months ago
  • Essential Deep Learning Checklist: Best Practices Unveiled
    How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / 12 months ago
  • How to Build a Logistic Regression Model: A Spam-filter Tutorial
    Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / over 1 year ago
  • Link Prediction With node2vec in Physics Collaboration Network
    Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / almost 2 years ago
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Segments.ai mentions (0)

We have not tracked any mentions of Segments.ai yet. Tracking of Segments.ai recommendations started around Mar 2021.

What are some alternatives?

When comparing Scikit-learn and Segments.ai, 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.

Labelbox - Build computer vision products for the real world

OpenCV - OpenCV is the world's biggest computer vision library

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

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

Universal Data Tool - Machine learning, data labeling tool, computer vision, annotate-images, classification, dataset

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