Segments.ai
Labelbox
Universal Data Tool
Supervisely
CrowdFlower
Amazon Mechanical Turk
V7
Playment
NumPy
Pandas
OpenCV
Scikit-learn
Dataiku
Exploratory
htm.java
WEKA
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
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Based on our record, NumPy seems to be more popular. It has been mentiond 107 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.
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication. - Source: dev.to / 2 months ago
Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:. - Source: dev.to / 3 months ago
Numpy: A library for scientific computing in Python. - Source: dev.to / 5 months ago
Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy. - Source: dev.to / 7 months ago
A majority of software in the modern world is built upon various third party packages. These packages help offload work that would otherwise be rather tedious. This includes interacting with cloud APIs, developing scientific applications, or even creating web applications. As you gain experience in python you'll be using more and more of these packages developed by others to power your own code. In this example... - Source: dev.to / 7 months ago
Labelbox - Build computer vision products for the real world
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Universal Data Tool - Machine learning, data labeling tool, computer vision, annotate-images, classification, dataset
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.
