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NumPy VS Segments.ai

Compare NumPy VS Segments.ai and see what are their differences

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NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

Segments.ai logo Segments.ai

Multi-sensor labeling platform for robotics and autonomous driving
  • NumPy Landing page
    Landing page //
    2023-05-13
  • 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

NumPy

Website
numpy.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

NumPy features and specs

  • Performance
    NumPy operations are executed with highly optimized C and Fortran libraries, making them significantly faster than standard Python arithmetic operations, especially for large datasets.
  • Versatility
    NumPy supports a vast range of mathematical, logical, shape manipulation, sorting, selecting, I/O, and basic linear algebra operations, making it a versatile tool for scientific and numeric computing.
  • Ease of Use
    NumPy provides an intuitive, easy-to-understand syntax that extends Python's ability to handle arrays and matrices, lowering the barrier to performing complex scientific computations.
  • Community Support
    With a large and active community, NumPy offers extensive documentation, tutorials, and support for troubleshooting issues, as well as continuous updates and enhancements.
  • Integrations
    NumPy integrates seamlessly with other libraries in Python's scientific stack like SciPy, Matplotlib, and Pandas, facilitating a streamlined workflow for data science and analysis tasks.

Possible disadvantages of NumPy

  • Memory Consumption
    NumPy arrays can consume large amounts of memory, especially when working with very large datasets, which can become a limitation on systems with limited memory capacity.
  • Learning Curve
    For users new to scientific computing or coming from different programming backgrounds, understanding the intricacies of NumPy's operations and efficient usage can take time and effort.
  • Limited GPU Support
    NumPy primarily runs on the CPU and doesn't natively support GPU acceleration, which can be a disadvantage for extremely compute-intensive tasks that could benefit from parallel processing.
  • Dependency on Python
    Since NumPy is a Python library, it depends on the Python runtime environment. This can be a limitation in environments where Python is not the primary language or isn't supported.
  • Indexing Complexity
    Although NumPy's slicing and indexing capabilities are powerful, they can sometimes be complex or unintuitive, especially for multi-dimensional arrays, leading to potential errors and confusion.

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 NumPy

Overall verdict

  • Yes, NumPy is considered good. It is a foundational library in the Python ecosystem for numerical computing and is used globally by researchers, engineers, and data scientists.

Why this product is good

  • NumPy is widely regarded as a good library because it offers fast, flexible, and efficient array handling that is integral to scientific computing in Python. It provides tools for integrating C/C++ and Fortran code, useful linear algebra, random number capabilities, and a vast collection of mathematical functions. Its array broadcasting capabilities and versatility make complex mathematical computations straightforward.

Recommended for

  • Scientists and researchers working with large-scale scientific computations.
  • Data scientists engaged in data analysis and manipulation.
  • Engineers and developers needing performance-optimized mathematical computations.
  • Educators and students in STEM fields.

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

NumPy videos

+ Add

Learn NUMPY in 5 minutes - BEST Python Library!

More videos:

  • Review - Python for Data Analysis by Wes McKinney: Review | Learn python, numpy, pandas and jupyter notebooks
  • Review - Effective Computation in Physics: Review | Learn python, numpy, regular expressions, install python

Segments.ai videos

+ Add

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

Category Popularity

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

NumPy

Segments.ai

Data Science And Machine Learning
87 87%
Data Science And Machine Learning
13% 13
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 NumPy and Segments.ai

NumPy Reviews

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Source: kinsta.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Source: neptune.ai
Top Python Libraries For Image Processing In 2021
Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.
Source: www.analyticsvidhya.com
4 open source alternatives to MATLAB
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...
Source: opensource.com

Segments.ai Reviews

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

Based on our record, NumPy seems to be more popular. It has been mentiond 119 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.

NumPy mentions (119)

  • Building an AI-powered Financial Data Analyzer with NodeJS, Python, SvelteKit, and TailwindCSS - Part 0
    The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis. - Source: dev.to / 5 months ago
  • F1 FollowLine + HSV filter + PID Controller
    This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays. - Source: dev.to / 9 months ago
  • Intro to Ray on GKE
    The Python Library components of Ray could be considered analogous to solutions like numpy, scipy, and pandas (which is most analogous to the Ray Data library specifically). As a framework and distributed computing solution, Ray could be used in place of a tool like Apache Spark or Python Dask. It’s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as... - Source: dev.to / 9 months ago
  • Streamlit 101: The fundamentals of a Python data app
    It's compatible with a wide range of data libraries, including Pandas, NumPy, and Altair. Streamlit integrates with all the latest tools in generative AI, such as any LLM, vector database, or various AI frameworks like LangChain, LlamaIndex, or Weights & Biases. Streamlit’s chat elements make it especially easy to interact with AI so you can build chatbots that “talk to your data.”. - Source: dev.to / 10 months ago
  • A simple way to extract all detected objects from image and save them as separate images using YOLOv8.2 and OpenCV
    The OpenCV image is a regular NumPy array. You can see it shape:. - Source: dev.to / 10 months ago
View more

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 NumPy 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...

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

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

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